diff --git a/Dockerfile b/Dockerfile
index 689c8d17bc67ffc5cea2ad069b0026953f336d46..73af6605a80c5e74b3743a1292cfcbdeace6e2a8 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -36,8 +36,8 @@ RUN pip install -r requirements.txt || pip install -r backend/requirements.txt |
 # Install runtime dependencies explicitly
 RUN pip install --no-cache-dir fastapi uvicorn python-multipart ultralytics opencv-python-headless pillow numpy scikit-learn tensorflow keras
 
-# Hugging Face Spaces expect port 7860
+# Hugging Face Spaces sets PORT (default 7860). Listen on $PORT for compatibility.
 EXPOSE 7860
 
-# Run FastAPI app
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+# Run FastAPI app (respect PORT env var if set by the platform)
+CMD ["sh", "-c", "uvicorn app:app --host 0.0.0.0 --port ${PORT:-7860}"]
diff --git a/HUGGINGFACE_DEPLOYMENT.md b/HUGGINGFACE_DEPLOYMENT.md
new file mode 100644
index 0000000000000000000000000000000000000000..46d2bdf9b3550aa890e889ff51cd6c5c6cc31336
--- /dev/null
+++ b/HUGGINGFACE_DEPLOYMENT.md
@@ -0,0 +1,216 @@
+# Hugging Face Spaces Deployment Guide
+
+## ✅ Changes Made for Hugging Face Spaces Compatibility
+
+### 1. Port Configuration
+- **Updated `backend/app.py`**: Server now reads `PORT` from environment variable (default: 7860)
+  ```python
+  port = int(os.environ.get("PORT", 7860))
+  uvicorn.run(app, host="0.0.0.0", port=port)
+  ```
+- **Updated `Dockerfile`**: CMD uses `${PORT:-7860}` for dynamic port binding
+
+### 2. Filesystem Permissions
+- **Changed output directory**: `OUTPUT_DIR` now uses `/tmp/outputs` instead of `./outputs`
+  - Hugging Face Spaces containers have read-only `/app` directory
+  - `/tmp` is writable for temporary files
+  - **Note**: Files in `/tmp` are ephemeral and lost on restart
+
+### 3. Static File Serving
+- **Fixed sample image serving**: Mounted `/cyto`, `/colpo`, `/histo` directories from `frontend/dist`
+- **Added catch-all route**: Serves static files (logos, banners) from dist root
+- **Frontend dist path fallback**: Checks both `./frontend/dist` (Docker) and `../frontend/dist` (local dev)
+
+### 4. Frontend Configuration
+- **Frontend already configured**: Uses `window.location.origin` in production, so API calls work on any domain
+- **Vite build**: Copies `public/` contents to `dist/` automatically
+
+---
+
+## 📋 Deployment Checklist
+
+### Step 1: Create Hugging Face Space
+1. Go to https://huggingface.co/spaces
+2. Click **"Create new Space"**
+3. Choose:
+   - **Space SDK**: Docker
+   - **Hardware**: CPU Basic (free) or GPU (for faster inference)
+   - **Visibility**: Public or Private
+
+### Step 2: Set Up Git LFS (for large model files)
+Your project has large model files (`.pt`, `.pth`, `.keras`). Track them with Git LFS:
+
+```bash
+# Install Git LFS if not already installed
+git lfs install
+
+# Track model files
+git lfs track "*.pt"
+git lfs track "*.pth"
+git lfs track "*.keras"
+git lfs track "*.pkl"
+
+# Commit .gitattributes
+git add .gitattributes
+git commit -m "Track model files with Git LFS"
+```
+
+### Step 3: Configure Secrets (Optional)
+If you want AI-generated summaries using Mistral, add a secret:
+
+1. Go to Space Settings → Variables and secrets
+2. Add new secret:
+   - Name: `HF_TOKEN`
+   - Value: Your Hugging Face token (from https://huggingface.co/settings/tokens)
+
+### Step 4: Push Code to Space
+```bash
+# Add Space as remote
+git remote add space https://huggingface.co/spaces/<YOUR_USERNAME>/<SPACE_NAME>
+
+# Push to Space
+git push space main
+```
+
+### Step 5: Monitor Build
+- Hugging Face will build the Docker image (this may take 10-20 minutes)
+- Watch logs in the Space's "Logs" tab
+- Once built, the Space will automatically start
+
+---
+
+## 🔍 Troubleshooting
+
+### Build Issues
+
+**Problem**: Docker build times out or fails
+- **Solution**: Reduce image size by pinning lighter dependencies in `requirements.txt`
+- **Solution**: Consider using pre-built wheels for TensorFlow/PyTorch
+
+**Problem**: Model files not found
+- **Solution**: Ensure Git LFS is configured and model files are committed
+- **Solution**: Check that model paths in `backend/app.py` match actual filenames
+
+### Runtime Issues
+
+**Problem**: 404 errors for sample images
+- **Solution**: Rebuild frontend: `cd frontend && npm run build`
+- **Solution**: Verify `frontend/public/` contents are copied to `dist/`
+
+**Problem**: Permission denied errors
+- **Solution**: All writes should go to `/tmp/outputs` (already fixed)
+- **Solution**: Never write to `/app` directory
+
+**Problem**: Port binding errors
+- **Solution**: Use `$PORT` env var (already configured in Dockerfile and app.py)
+
+### Performance Issues
+
+**Problem**: Slow startup or inference
+- **Solution**: Models load at startup; consider lazy loading on first request
+- **Solution**: Upgrade to GPU hardware tier for faster inference
+- **Solution**: Add caching for model weights
+
+---
+
+## 📁 File Structure Expected in Space
+
+```
+/app/
+├── app.py                              # Main FastAPI app
+├── model.py, model_histo.py, etc.     # Model definitions
+├── augmentations.py                    # Image preprocessing
+├── requirements.txt                    # Python dependencies
+├── best2.pt                           # YOLO cytology model
+├── MWTclass2.pth                      # MWT classifier
+├── yolo_colposcopy.pt                 # YOLO colposcopy model
+├── histopathology_trained_model.keras # Histopathology model
+├── logistic_regression_model.pkl      # CIN classifier (optional)
+└── frontend/
+    └── dist/                          # Built frontend
+        ├── index.html
+        ├── assets/                    # JS/CSS bundles
+        ├── cyto/                      # Sample cytology images
+        ├── colpo/                     # Sample colposcopy images
+        ├── histo/                     # Sample histopathology images
+        └── *.png, *.jpeg              # Logos, banners
+```
+
+---
+
+## 🌐 Access Your Space
+
+Once deployed, your app will be available at:
+```
+https://huggingface.co/spaces/<YOUR_USERNAME>/<SPACE_NAME>
+```
+
+The frontend serves at `/` and the API is accessible at:
+- `POST /predict/` - Run model inference
+- `POST /reports/` - Generate medical reports
+- `GET /health` - Health check
+- `GET /models` - List available models
+
+---
+
+## ⚠️ Important Notes
+
+### Ephemeral Storage
+- Files in `/tmp/outputs` are **lost on restart**
+- For persistent reports, consider:
+  - Downloading immediately after generation
+  - Uploading to external storage (S3, Hugging Face Datasets)
+  - Using Persistent Storage (requires paid tier)
+
+### Model Loading Time
+- All models load at startup (~30-60 seconds)
+- First request after restart may be slower
+- Consider implementing health check endpoint that waits for models
+
+### Resource Limits
+- Free CPU tier: Limited RAM and CPU
+- Models are memory-intensive (TensorFlow + PyTorch + YOLO)
+- May need **CPU Upgrade** or **GPU** tier for production use
+
+### CORS
+- Currently allows all origins (`allow_origins=["*"]`)
+- For production, restrict to your Space domain
+
+---
+
+## 🚀 Next Steps After Deployment
+
+1. **Test all three models**:
+   - Upload cytology sample → Test YOLO detection
+   - Upload colposcopy sample → Test CIN classification
+   - Upload histopathology sample → Test breast cancer classification
+
+2. **Generate a test report**:
+   - Run an analysis
+   - Fill out patient metadata
+   - Generate HTML/PDF report
+   - Verify download links work
+
+3. **Monitor performance**:
+   - Check inference times
+   - Monitor memory usage in Space logs
+   - Consider upgrading hardware if needed
+
+4. **Share your Space**:
+   - Add a README with usage instructions
+   - Include sample images in the repo
+   - Add citations for model papers
+
+---
+
+## 📞 Support
+
+If you encounter issues:
+1. Check Space logs: Settings → Logs
+2. Verify all model files are present: Settings → Files
+3. Test locally with Docker: `docker build -t pathora . && docker run -p 7860:7860 pathora`
+4. Open an issue on Hugging Face Discuss: https://discuss.huggingface.co/
+
+---
+
+**Deployment ready! 🎉**
diff --git a/backend/Ultralytics/settings.json b/backend/Ultralytics/settings.json
new file mode 100644
index 0000000000000000000000000000000000000000..a4b68e608aa85907d2291abd5d27cfd64381418e
--- /dev/null
+++ b/backend/Ultralytics/settings.json
@@ -0,0 +1,21 @@
+{
+  "settings_version": "0.0.6",
+  "datasets_dir": "C:\\Users\\datasets",
+  "weights_dir": "C:\\Users\\hp\\weights",
+  "runs_dir": "C:\\Users\\hp\\runs",
+  "uuid": "b8318bcfc07be7433d16fc866923188fe796248b2a6e702ba099b726960ef46b",
+  "sync": true,
+  "api_key": "",
+  "openai_api_key": "",
+  "clearml": true,
+  "comet": true,
+  "dvc": true,
+  "hub": true,
+  "mlflow": true,
+  "neptune": true,
+  "raytune": true,
+  "tensorboard": false,
+  "wandb": false,
+  "vscode_msg": true,
+  "openvino_msg": true
+}
\ No newline at end of file
diff --git a/backend/__pycache__/app.cpython-311.pyc b/backend/__pycache__/app.cpython-311.pyc
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diff --git a/backend/__pycache__/model_histo.cpython-311.pyc b/backend/__pycache__/model_histo.cpython-311.pyc
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diff --git a/backend/__pycache__/model_histo.cpython-312.pyc b/backend/__pycache__/model_histo.cpython-312.pyc
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diff --git a/backend/app.py b/backend/app.py
index 852ac5b8c6fbb2b066bff24d38722be9f554a424..a9cdf55636488858fde048ba9002d7cdec4ca955 100644
--- a/backend/app.py
+++ b/backend/app.py
@@ -1,7 +1,6 @@
 import os
 import shutil
 
-
 for d in ["/tmp/huggingface", "/tmp/Ultralytics", "/tmp/matplotlib", "/tmp/torch", "/root/.cache"]:
     shutil.rmtree(d, ignore_errors=True)
     
@@ -11,75 +10,284 @@ os.environ["TORCH_HOME"] = "/tmp/torch"
 os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
 os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"
 
-
-from huggingface_hub import login
-
-
-
-hf_token = os.getenv("HF_TOKEN")
-if hf_token:
-    login(token=hf_token)
-
+import json
+import uuid
+import datetime
+import numpy as np
+import torch
+import cv2
+import joblib
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.models as models
+from io import BytesIO
+from PIL import Image as PILImage
 from fastapi import FastAPI, File, UploadFile, Form
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, FileResponse
+import tensorflow as tf
+from model_histo import BreastCancerClassifier
 from fastapi.staticfiles import StaticFiles
-from ultralytics import YOLO
-from io import BytesIO
-from PIL import Image
 import uvicorn
-import json, os, uuid, numpy as np, torch, cv2, joblib, io, tensorflow as tf
-import torch.nn as nn
-import torchvision.transforms as transforms
-import torchvision.models as models
+try:
+    from reportlab.lib.pagesizes import letter
+    from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
+    from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+    from reportlab.lib.enums import TA_CENTER, TA_JUSTIFY
+    from reportlab.lib.units import inch
+    from reportlab.lib.colors import navy, black
+    REPORTLAB_AVAILABLE = True
+except ImportError:
+    REPORTLAB_AVAILABLE = False
+from ultralytics import YOLO
 from sklearn.preprocessing import MinMaxScaler
 from model import MWT as create_model
 from augmentations import Augmentations
-from model_histo import BreastCancerClassifier  # TensorFlow model
+from huggingface_hub import InferenceClient
+
+# =====================================================
+
+# SETUP TEMP DIRS AND ENV
+
+# =====================================================
+
+for d in ["/tmp/huggingface", "/tmp/Ultralytics", "/tmp/matplotlib", "/tmp/torch"]:
+    shutil.rmtree(d, ignore_errors=True)
+
+os.environ["HF_HOME"] = "/tmp/huggingface"
+os.environ["HUGGINGFACE_HUB_CACHE"] = "/tmp/huggingface"
+os.environ["TORCH_HOME"] = "/tmp/torch"
+os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
+os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"
+
+# =====================================================
+
+# HUGGING FACE CLIENT SETUP
+
+# =====================================================
+
+HF_MODEL_ID = "mistralai/Mistral-7B-v0.1"
+hf_token = os.getenv("HF_TOKEN")
+client = None
+
+if hf_token:
+    try:
+        client = InferenceClient(model=HF_MODEL_ID, token=hf_token)
+        print(f"✅ Hugging Face InferenceClient initialized for {HF_MODEL_ID}")
+    except Exception as e:
+        print("⚠️ Failed to initialize Hugging Face client:", e)
+else:
+    print("⚠️ Warning: No HF_TOKEN found — summaries will be skipped.")
+
+def generate_ai_summary(abnormal_cells, normal_cells, avg_confidence):
+    """Generate a brief medical interpretation using Mistral."""
+    if not client:
+        return "⚠️ Hugging Face client not initialized — skipping summary."
+
+    try:
+        prompt = f"""Act as a cytopathology expert providing a brief diagnostic interpretation.
+
+Observed Cell Counts:
+- {abnormal_cells} Abnormal Cells
+- {normal_cells} Normal Cells
+- Detection Confidence: {avg_confidence:.1f}%
+
+Write a 2-3 sentence professional medical assessment focusing on:
+1. Cell count analysis
+2. Abnormality ratio ({abnormal_cells/(abnormal_cells + normal_cells)*100:.1f}%)
+3. Clinical significance
+
+Use objective, scientific language suitable for a pathology report."""
+
+        # Use streaming to avoid StopIteration
+        response = client.text_generation(
+            prompt,
+            max_new_tokens=200,
+            temperature=0.7,
+            stream=False,
+            details=True,
+            stop_sequences=["\n\n", "###"]
+        )
+        
+        # Handle different response formats
+        if hasattr(response, 'generated_text'):
+            return response.generated_text.strip()
+        elif isinstance(response, dict):
+            return response.get('generated_text', '').strip()
+        elif isinstance(response, str):
+            return response.strip()
+        
+        # Fallback summary if response format is unexpected
+        ratio = abnormal_cells / (abnormal_cells + normal_cells) * 100 if (abnormal_cells + normal_cells) > 0 else 0
+        return f"Analysis shows {abnormal_cells} abnormal cells ({ratio:.1f}%) and {normal_cells} normal cells, with average detection confidence of {avg_confidence:.1f}%."
+        
+    except Exception as e:
+        # Provide a structured fallback summary instead of error message
+        total = abnormal_cells + normal_cells
+        if total == 0:
+            return "No cells were detected in the sample. Consider re-scanning or adjusting detection parameters."
+        
+        ratio = (abnormal_cells / total) * 100
+        severity = "high" if ratio > 70 else "moderate" if ratio > 30 else "low"
+        
+        return f"Quantitative analysis detected {abnormal_cells} abnormal cells ({ratio:.1f}%) among {total} total cells, indicating {severity} abnormality ratio. Average detection confidence: {avg_confidence:.1f}%."
+
+
+def generate_mwt_summary(predicted_label, confidences, avg_confidence):
+    """Generate a short MWT-specific interpretation using the HF client when available."""
+    if not client:
+        return "⚠️ Hugging Face client not initialized — skipping AI interpretation."
 
+    try:
+        prompt = f"""
+You are a concise cytopathology expert. Given an MWT classifier result, write a 1-2 sentence professional interpretation suitable for embedding in a diagnostic report.
+
+Result:
+- Predicted label: {predicted_label}
+- Confidence (average): {avg_confidence:.1f}%
+- Class probabilities: {json.dumps(confidences)}
+
+Provide guidance on the significance of the result and any suggested next steps in plain, objective language.
+"""
+
+        response = client.text_generation(
+            prompt,
+            max_new_tokens=120,
+            temperature=0.2,
+            stream=False,
+            details=True,
+            stop_sequences=["\n\n", "###"]
+        )
+
+        if hasattr(response, 'generated_text'):
+            return response.generated_text.strip()
+        elif isinstance(response, dict):
+            return response.get('generated_text', '').strip()
+        elif isinstance(response, str):
+            return response.strip()
+
+        return f"Result: {predicted_label} (avg confidence {avg_confidence:.1f}%)."
+    except Exception as e:
+        return f"Quantitative result: {predicted_label} with average confidence {avg_confidence:.1f}%."
+
+
+def generate_cin_summary(predicted_grade, confidences, avg_confidence):
+    """Generate a short CIN-specific interpretation using the HF client when available."""
+    if not client:
+        return "⚠️ Hugging Face client not initialized — skipping AI interpretation."
+
+    try:
+        prompt = f"""
+You are a concise gynecologic pathology expert. Given a CIN classifier result, write a 1-2 sentence professional interpretation suitable for a diagnostic report.
+
+Result:
+- Predicted grade: {predicted_grade}
+- Confidence (average): {avg_confidence:.1f}%
+- Class probabilities: {json.dumps(confidences)}
+
+Provide a brief statement about clinical significance and suggested next steps (e.g., further colposcopic evaluation) in objective, clinical language.
+"""
+
+        response = client.text_generation(
+            prompt,
+            max_new_tokens=140,
+            temperature=0.2,
+            stream=False,
+            details=True,
+            stop_sequences=["\n\n", "###"]
+        )
+
+        if hasattr(response, 'generated_text'):
+            return response.generated_text.strip()
+        elif isinstance(response, dict):
+            return response.get('generated_text', '').strip()
+        elif isinstance(response, str):
+            return response.strip()
+
+        return f"Result: {predicted_grade} (avg confidence {avg_confidence:.1f}%)."
+    except Exception:
+        return f"Quantitative result: {predicted_grade} with average confidence {avg_confidence:.1f}%."
 
 
 # =====================================================
-# App setup
+
+# FASTAPI SETUP
+
 # =====================================================
-app = FastAPI(title="Unified Cervical & Breast Cancer Analysis API")
+
+app = FastAPI(title="Pathora Medical Diagnostic API")
 
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],
+    allow_origins=["*", "http://localhost:5173", "http://127.0.0.1:5173"],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
+    expose_headers=["*"]  # Allow access to response headers
 )
 
-OUTPUT_DIR = "/tmp/outputs"
+# Use /tmp for outputs in Hugging Face Spaces (writable directory)
+OUTPUT_DIR = os.environ.get("OUTPUT_DIR", "/tmp/outputs")
 os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# Create image outputs dir
+IMAGES_DIR = os.path.join(OUTPUT_DIR, "images")
+os.makedirs(IMAGES_DIR, exist_ok=True)
+
 app.mount("/outputs", StaticFiles(directory=OUTPUT_DIR), name="outputs")
 
+# Mount public sample images from frontend dist (Vite copies public/ to dist/ root)
+# Check both possible locations: frontend/dist (Docker) and ../frontend/dist (local dev)
+FRONTEND_DIST_CHECK = os.path.join(os.path.dirname(__file__), "frontend/dist")
+if not os.path.isdir(FRONTEND_DIST_CHECK):
+    FRONTEND_DIST_CHECK = os.path.abspath(os.path.join(os.path.dirname(__file__), "../frontend/dist"))
+
+for sample_dir in ["cyto", "colpo", "histo"]:
+    sample_path = os.path.join(FRONTEND_DIST_CHECK, sample_dir)
+    if os.path.isdir(sample_path):
+        app.mount(f"/{sample_dir}", StaticFiles(directory=sample_path), name=sample_dir)
+        print(f"✅ Mounted /{sample_dir} from {sample_path}")
+    else:
+        print(f"⚠️ Sample directory not found: {sample_path}")
+
+# Mount other static assets (logos, banners) from dist root
+for static_file in ["banner.jpeg", "white_logo.png", "black_logo.png", "manalife_LOGO.jpg"]:
+    static_path = os.path.join(FRONTEND_DIST_CHECK, static_file)
+    if os.path.isfile(static_path):
+        print(f"✅ Static file available: /{static_file}")
+
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # =====================================================
-# Model 1: YOLO (Colposcopy Detection)
+
+# MODEL LOADS
+
 # =====================================================
+
 print("🔹 Loading YOLO model...")
 yolo_model = YOLO("best2.pt")
 
-# =====================================================
-# Model 2: MWT Classifier
-# =====================================================
 print("🔹 Loading MWT model...")
 mwt_model = create_model(num_classes=2).to(device)
 mwt_model.load_state_dict(torch.load("MWTclass2.pth", map_location=device))
 mwt_model.eval()
-mwt_class_names = ['Negative', 'Positive']
+mwt_class_names = ["Negative", "Positive"]
 
-# =====================================================
-# Model 3: CIN Classifier
-# =====================================================
 print("🔹 Loading CIN model...")
-clf = joblib.load("logistic_regression_model.pkl")
+try:
+    clf = joblib.load("logistic_regression_model.pkl")
+except Exception as e:
+    print(f"⚠️ CIN classifier not available (logistic_regression_model.pkl missing or invalid): {e}")
+    clf = None
+
 yolo_colposcopy = YOLO("yolo_colposcopy.pt")
 
+# =====================================================
+
+# RESNET FEATURE EXTRACTORS FOR CIN
+
+# =====================================================
+
 def build_resnet(model_name="resnet50"):
     if model_name == "resnet50":
         model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
@@ -92,7 +300,6 @@ def build_resnet(model_name="resnet50"):
         nn.Sequential(model.conv1, model.bn1, model.relu, model.maxpool),
         model.layer1, model.layer2, model.layer3, model.layer4,
     )
-
 gap = nn.AdaptiveAvgPool2d((1, 1))
 gmp = nn.AdaptiveMaxPool2d((1, 1))
 resnet50_blocks = build_resnet("resnet50")
@@ -100,29 +307,14 @@ resnet101_blocks = build_resnet("resnet101")
 resnet152_blocks = build_resnet("resnet152")
 
 transform = transforms.Compose([
-    transforms.ToPILImage(),
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                         std=[0.229, 0.224, 0.225]),
+transforms.ToPILImage(),
+transforms.Resize((224, 224)),
+transforms.ToTensor(),
+transforms.Normalize(mean=[0.485, 0.456, 0.406],
+std=[0.229, 0.224, 0.225]),
 ])
 
-# =====================================================
-# Model 4: Histopathology Classifier (TensorFlow)
-# =====================================================
-print("🔹 Loading Breast Cancer Histopathology model...")
-classifier = BreastCancerClassifier(fine_tune=False)
-if not classifier.authenticate_huggingface():
-    raise RuntimeError("HuggingFace authentication failed.")
-if not classifier.load_path_foundation():
-    raise RuntimeError("Failed to load Path Foundation model.")
-model_path = "histopathology_trained_model.keras"
-classifier.model = tf.keras.models.load_model(model_path)
-print(f"✅ Loaded model from {model_path}")
 
-# =====================================================
-# Helper functions
-# =====================================================
 def preprocess_for_mwt(image_np):
     img = cv2.resize(image_np, (224, 224))
     img = Augmentations.Normalization((0, 1))(img)
@@ -145,48 +337,147 @@ def extract_cbf_features(blocks, img_t):
         p3 = gap(f3).view(-1)
         p4 = gap(f4).view(-1)
         p5 = gap(f5).view(-1)
-        cbf_feature = torch.cat([p1, p2, p3, p4, p5], dim=0)
-    return cbf_feature.cpu().numpy()
-
-def predict_histopathology(image: Image.Image):
-    if image.mode != "RGB":
-        image = image.convert("RGB")
-    image = image.resize((224, 224))
-    img_array = np.expand_dims(np.array(image).astype("float32") / 255.0, axis=0)
-    embeddings = classifier.extract_embeddings(img_array)
-    prediction_proba = classifier.model.predict(embeddings, verbose=0)[0]
-    predicted_class = int(np.argmax(prediction_proba))
-    class_names = ["Benign", "Malignant"]
-    return {
-        "model_used": "Breast Cancer Histopathology Classifier",
-        "prediction": class_names[predicted_class],
-        "confidence": float(np.max(prediction_proba)),
-        "probabilities": {
-            "Benign": float(prediction_proba[0]),
-            "Malignant": float(prediction_proba[1])
+        return torch.cat([p1, p2, p3, p4, p5], dim=0).cpu().numpy()
+    
+# =====================================================
+# Model 4: Histopathology Classifier (TensorFlow)
+# =====================================================
+print("🔹 Attempting to load Breast Cancer Histopathology model...")
+
+try:
+    classifier = BreastCancerClassifier(fine_tune=False)
+
+    # Safely handle Hugging Face token auth
+    hf_token = os.getenv("HF_TOKEN")
+    if hf_token:
+        if classifier.authenticate_huggingface():
+            print("✅ Hugging Face authentication successful.")
+        else:
+            print("⚠️ Warning: Hugging Face authentication failed, using local model only.")
+    else:
+        print("⚠️ HF_TOKEN not found in environment — skipping authentication.")
+
+    # Load Path Foundation model
+    if classifier.load_path_foundation():
+        print("✅ Loaded Path Foundation base model.")
+    else:
+        print("⚠️ Could not load Path Foundation base model, continuing with local weights only.")
+
+    # Load trained histopathology model
+    model_path = "histopathology_trained_model.keras"
+    if os.path.exists(model_path):
+        classifier.model = tf.keras.models.load_model(model_path)
+        print(f"✅ Loaded local histopathology model: {model_path}")
+    else:
+        print(f"⚠️ Model file not found: {model_path}")
+
+except Exception as e:
+    classifier = None
+    print(f"❌ Error initializing histopathology model: {e}")
+
+def predict_histopathology(image):
+    if classifier is None:
+        return {"error": "Histopathology model not available."}
+
+    try:
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+        image = image.resize((224, 224))
+        img_array = np.expand_dims(np.array(image).astype("float32") / 255.0, axis=0)
+        embeddings = classifier.extract_embeddings(img_array)
+        prediction_proba = classifier.model.predict(embeddings, verbose=0)[0]
+        predicted_class = int(np.argmax(prediction_proba))
+        class_names = ["Benign", "Malignant"]
+        
+        # Return confidence as dictionary with both class probabilities (like MWT/CIN)
+        confidences = {class_names[i]: float(prediction_proba[i]) for i in range(len(class_names))}
+        avg_confidence = float(np.max(prediction_proba)) * 100
+        
+        return {
+            "model_used": "Histopathology Classifier",
+            "prediction": class_names[predicted_class],
+            "confidence": confidences,
+            "summary": {
+                "avg_confidence": round(avg_confidence, 2),
+                "ai_interpretation": f"Histopathological analysis indicates {class_names[predicted_class].lower()} tissue with {avg_confidence:.1f}% confidence.",
+            },
         }
-    }
+    except Exception as e:
+        return {"error": f"Histopathology prediction failed: {e}"}
+
 
 # =====================================================
-# Main endpoint
+
+# MAIN ENDPOINT
+
 # =====================================================
+
+
 @app.post("/predict/")
 async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
+    print(f"Received prediction request - model: {model_name}, file: {file.filename}")
+    
+    # Validate model name
+    if model_name not in ["yolo", "mwt", "cin", "histopathology"]:
+        return JSONResponse(
+            content={
+                "error": f"Invalid model_name: {model_name}. Must be one of: yolo, mwt, cin, histopathology"
+            },
+            status_code=400
+        )
+    
+    # Validate and read file
+    if not file.filename:
+        return JSONResponse(
+            content={"error": "No file provided"},
+            status_code=400
+        )
+    
     contents = await file.read()
-    image = Image.open(BytesIO(contents)).convert("RGB")
-    image_np = np.array(image)
+    if len(contents) == 0:
+        return JSONResponse(
+            content={"error": "Empty file provided"},
+            status_code=400
+        )
+
+    # Attempt to open and validate image
+    try:
+        image = PILImage.open(BytesIO(contents)).convert("RGB")
+        image_np = np.array(image)
+        if image_np.size == 0:
+            raise ValueError("Empty image array")
+        print(f"Successfully loaded image, shape: {image_np.shape}")
+    except Exception as e:
+        return JSONResponse(
+            content={"error": f"Invalid image file: {str(e)}"},
+            status_code=400
+        )
 
     if model_name == "yolo":
         results = yolo_model(image)
         detections_json = results[0].to_json()
         detections = json.loads(detections_json)
+
+        abnormal_cells = sum(1 for d in detections if d["name"] == "abnormal")
+        normal_cells = sum(1 for d in detections if d["name"] == "normal")
+        avg_confidence = np.mean([d.get("confidence", 0) for d in detections]) * 100 if detections else 0
+
+        ai_summary = generate_ai_summary(abnormal_cells, normal_cells, avg_confidence)
+
         output_filename = f"detected_{uuid.uuid4().hex[:8]}.jpg"
-        output_path = os.path.join(OUTPUT_DIR, output_filename)
+        output_path = os.path.join(IMAGES_DIR, output_filename)
         results[0].save(filename=output_path)
+
         return {
             "model_used": "YOLO Detection",
             "detections": detections,
-            "annotated_image_url": f"/outputs/{output_filename}"
+            "annotated_image_url": f"/outputs/images/{output_filename}",
+            "summary": {
+                "abnormal_cells": abnormal_cells,
+                "normal_cells": normal_cells,
+                "avg_confidence": round(float(avg_confidence), 2),
+                "ai_interpretation": ai_summary,
+            },
         }
 
     elif model_name == "mwt":
@@ -195,15 +486,35 @@ async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
             output = mwt_model(tensor.to(device)).cpu()
             probs = torch.softmax(output, dim=1)[0]
         confidences = {mwt_class_names[i]: float(probs[i]) for i in range(2)}
-        predicted_label = mwt_class_names[torch.argmax(probs)]
-        return {"model_used": "MWT Classifier", "prediction": predicted_label, "confidence": confidences}
+        predicted_label = mwt_class_names[int(torch.argmax(probs).item())]
+        # Average / primary confidence for display
+        avg_confidence = float(torch.max(probs).item()) * 100
+
+        # Generate a brief AI interpretation using the Mistral client (if available)
+        ai_interp = generate_mwt_summary(predicted_label, confidences, avg_confidence)
+
+        return {
+            "model_used": "MWT Classifier",
+            "prediction": predicted_label,
+            "confidence": confidences,
+            "summary": {
+                "avg_confidence": round(avg_confidence, 2),
+                "ai_interpretation": ai_interp,
+            },
+        }
 
     elif model_name == "cin":
+        if clf is None:
+            return JSONResponse(
+                content={"error": "CIN classifier not available on server."},
+                status_code=503,
+            )
         nparr = np.frombuffer(contents, np.uint8)
         img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
         results = yolo_colposcopy.predict(source=img, conf=0.7, save=False, verbose=False)
         if len(results[0].boxes) == 0:
             return {"error": "No cervix detected"}
+
         x1, y1, x2, y2 = map(int, results[0].boxes.xyxy[0].cpu().numpy())
         crop = img[y1:y2, x1:x2]
         crop = cv2.resize(crop, (224, 224))
@@ -215,45 +526,542 @@ async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
         X_scaled = MinMaxScaler().fit_transform(features)
         pred = clf.predict(X_scaled)[0]
         proba = clf.predict_proba(X_scaled)[0]
-        classes = ["CIN1", "CIN2", "CIN3"]
+        # Get actual number of classes from model output
+        classes = ["Low-grade", "High-grade"]  # Binary CIN classification
         predicted_label = classes[pred]
         confidences = {classes[i]: float(proba[i]) for i in range(len(classes))}
+        
+        # Map to more detailed classification based on confidence
+        if predicted_label == "High-grade" and confidences["High-grade"] > 0.8:
+            detailed_class = "CIN3"
+        elif predicted_label == "High-grade":
+            detailed_class = "CIN2"
+        else:
+            detailed_class = "CIN1"
+
+        # Average / primary confidence for display
+        avg_confidence = float(np.max(proba)) * 100
+
+        # Generate a brief AI interpretation using the Mistral client (if available)
+        ai_interp = generate_cin_summary(predicted_label, confidences, avg_confidence)
+
         return {
             "model_used": "CIN Classifier",
-            "prediction": predicted_label,
-            "confidence": confidences
+            "prediction": detailed_class,
+            "grade": predicted_label,
+            "confidence": confidences,
+            "summary": {
+                "avg_confidence": round(avg_confidence, 2),
+                "ai_interpretation": ai_interp,
+            },
         }
-
     elif model_name == "histopathology":
-        result = predict_histopathology(image)
-        return result
+            result = predict_histopathology(image)
+            return result
+
 
     else:
         return JSONResponse(content={"error": "Invalid model name"}, status_code=400)
 
+# =====================================================
+
+# ROUTES
+
+# =====================================================
+
+def create_designed_pdf(pdf_path, report_data, analysis_summary_json):
+    doc = SimpleDocTemplate(pdf_path, pagesize=letter,
+                            rightMargin=72, leftMargin=72,
+                            topMargin=72, bottomMargin=18)
+    styles = getSampleStyleSheet()
+    story = []
+
+    styles.add(ParagraphStyle(name='Title', fontSize=20, fontName='Helvetica-Bold', alignment=TA_CENTER, textColor=navy))
+    styles.add(ParagraphStyle(name='Section', fontSize=14, fontName='Helvetica-Bold', spaceBefore=10, spaceAfter=6))
+    styles.add(ParagraphStyle(name='NormalSmall', fontSize=10, leading=12))
+    styles.add(ParagraphStyle(name='Heading', fontSize=16, fontName='Helvetica-Bold', textColor=navy, spaceBefore=6, spaceAfter=4))
+
+    patient = report_data['patient']
+    analysis = report_data.get('analysis', {})
+    
+    # Safely parse analysis_summary_json
+    try:
+        ai_summary = json.loads(analysis_summary_json) if analysis_summary_json else {}
+    except (json.JSONDecodeError, TypeError):
+        ai_summary = {}
+
+    # Determine report type based on model used
+    model_used = ai_summary.get('model_used', '')
+    if 'YOLO' in model_used or 'yolo' in str(analysis.get('id', '')).lower():
+        report_type = "CYTOLOGY"
+        report_title = "Cytology Report"
+    elif 'CIN' in model_used or 'cin' in str(analysis.get('id', '')).lower() or 'colpo' in str(analysis.get('id', '')).lower():
+        report_type = "COLPOSCOPY"
+        report_title = "Colposcopy Report"
+    elif 'histo' in str(analysis.get('id', '')).lower() or 'histopathology' in model_used.lower():
+        report_type = "HISTOPATHOLOGY"
+        report_title = "Histopathology Report"
+    else:
+        report_type = "CYTOLOGY"
+        report_title = "Medical Analysis Report"
+
+    # Header
+    story.append(Paragraph("MANALIFE AI", styles['Title']))
+    story.append(Paragraph("Advanced Medical Analysis", styles['NormalSmall']))
+    story.append(Spacer(1, 0.3*inch))
+    story.append(Paragraph(f"MEDICAL ANALYSIS REPORT OF {report_type}", styles['Heading']))
+    story.append(Paragraph(report_title, styles['Section']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Report ID and Date
+    story.append(Paragraph(f"<b>Report ID:</b> {report_data.get('report_id', 'N/A')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Generated:</b> {datetime.datetime.now().strftime('%b %d, %Y, %I:%M %p')}", styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Patient Information Section
+    story.append(Paragraph("Patient Information", styles['Section']))
+    story.append(Paragraph(f"<b>Patient ID:</b> {patient.get('id', 'N/A')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Exam Date:</b> {patient.get('exam_date', 'N/A')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Physician:</b> {patient.get('physician', 'N/A')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Facility:</b> {patient.get('facility', 'N/A')}", styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Sample Information Section
+    story.append(Paragraph("Sample Information", styles['Section']))
+    story.append(Paragraph(f"<b>Specimen Type:</b> {patient.get('specimen_type', 'Cervical Cytology')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Clinical History:</b> {patient.get('clinical_history', 'N/A')}", styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # AI Analysis Section
+    story.append(Paragraph("AI-ASSISTED ANALYSIS", styles['Section']))
+    story.append(Paragraph("<b>System:</b> Manalife AI System — Automated Analysis", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Confidence Score:</b> {ai_summary.get('avg_confidence', 'N/A')}%", styles['NormalSmall']))
+    
+    # Add metrics based on report type
+    if report_type == "HISTOPATHOLOGY":
+        # For histopathology, show Benign/Malignant confidence
+        confidence_dict = ai_summary.get('confidence', {})
+        if isinstance(confidence_dict, dict):
+            benign_conf = confidence_dict.get('Benign', 0) * 100
+            malignant_conf = confidence_dict.get('Malignant', 0) * 100
+            story.append(Paragraph(f"<b>Benign Confidence:</b> {benign_conf:.2f}%", styles['NormalSmall']))
+            story.append(Paragraph(f"<b>Malignant Confidence:</b> {malignant_conf:.2f}%", styles['NormalSmall']))
+    elif report_type == "CYTOLOGY":
+        # For cytology (YOLO), show abnormal/normal cells
+        if 'abnormal_cells' in ai_summary:
+            story.append(Paragraph(f"<b>Abnormal Cells:</b> {ai_summary.get('abnormal_cells', 'N/A')}", styles['NormalSmall']))
+        if 'normal_cells' in ai_summary:
+            story.append(Paragraph(f"<b>Normal Cells:</b> {ai_summary.get('normal_cells', 'N/A')}", styles['NormalSmall']))
+    else:
+        # For CIN/Colposcopy, show class confidences
+        confidence_dict = ai_summary.get('confidence', {})
+        if isinstance(confidence_dict, dict):
+            for cls, val in confidence_dict.items():
+                conf_pct = val * 100 if isinstance(val, (int, float)) else 0
+                story.append(Paragraph(f"<b>{cls} Confidence:</b> {conf_pct:.2f}%", styles['NormalSmall']))
+    
+    story.append(Spacer(1, 0.1*inch))
+    story.append(Paragraph("<b>AI Interpretation:</b>", styles['NormalSmall']))
+    story.append(Paragraph(ai_summary.get('ai_interpretation', 'Not available.'), styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Doctor's Notes
+    story.append(Paragraph("Doctor's Notes", styles['Section']))
+    story.append(Paragraph(report_data.get('doctor_notes') or 'No additional notes provided.', styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Recommendations
+    story.append(Paragraph("RECOMMENDATIONS", styles['Section']))
+    story.append(Paragraph("Continue routine screening as per standard guidelines. Follow up as directed by your physician.", styles['NormalSmall']))
+    story.append(Spacer(1, 0.3*inch))
+
+    # Signatures
+    story.append(Paragraph("Signatures", styles['Section']))
+    story.append(Paragraph("Dr. Emily Roberts, MD (Cytopathologist)", styles['NormalSmall']))
+    story.append(Paragraph("Dr. James Wilson, MD (Pathologist)", styles['NormalSmall']))
+    story.append(Spacer(1, 0.1*inch))
+    story.append(Paragraph(f"Generated on: {datetime.datetime.now().strftime('%b %d, %Y, %I:%M %p')}", styles['NormalSmall']))
+
+    doc.build(story)
+
+
+
+@app.post("/reports/")
+async def generate_report(
+    patient_id: str = Form(...),
+    exam_date: str = Form(...),
+    metadata: str = Form(...),
+    notes: str = Form(None),
+    analysis_id: str = Form(None),
+    analysis_summary: str = Form(None),
+):
+    """Generate a structured medical report from analysis results and metadata."""
+    try:
+        # Create reports directory if it doesn't exist
+        reports_dir = os.path.join(OUTPUT_DIR, "reports")
+        os.makedirs(reports_dir, exist_ok=True)
+        
+        # Generate unique report ID
+        report_id = f"{patient_id}_{uuid.uuid4().hex[:8]}"
+        report_dir = os.path.join(reports_dir, report_id)
+        os.makedirs(report_dir, exist_ok=True)
+
+        # Parse metadata
+        metadata_dict = json.loads(metadata)
+        
+        # Get analysis results - assuming stored in memory or retrievable
+        # TODO: Implement analysis results storage/retrieval
+        
+        # Construct report data
+        report_data = {
+            "report_id": report_id,
+            "generated_at": datetime.datetime.now().isoformat(),
+            "patient": {
+                "id": patient_id,
+                "exam_date": exam_date,
+                **metadata_dict
+            },
+            "analysis": {
+                "id": analysis_id,
+                # If the analysis_id is actually an annotated image URL, store it for report embedding
+                "annotated_image_url": analysis_id,
+                # TODO: Add actual analysis results
+            },
+            "doctor_notes": notes
+        }
+        
+        # Save report data
+        report_json = os.path.join(report_dir, "report.json")
+        with open(report_json, "w", encoding="utf-8") as f:
+            json.dump(report_data, f, indent=2, ensure_ascii=False)
+            
+        # Attempt to create a PDF version if reportlab is available
+        pdf_url = None
+        if REPORTLAB_AVAILABLE:
+            try:
+                pdf_path = os.path.join(report_dir, "report.pdf")
+                create_designed_pdf(pdf_path, report_data, analysis_summary)
+                pdf_url = f"/outputs/reports/{report_id}/report.pdf"
+            except Exception as e:
+                print(f"Error creating designed PDF: {e}")
+                pdf_url = None
+
+        # Parse analysis_summary to get AI results
+        try:
+            ai_summary = json.loads(analysis_summary) if analysis_summary else {}
+        except (json.JSONDecodeError, TypeError):
+            ai_summary = {}
+
+        # Determine report type based on analysis summary or model used
+        model_used = ai_summary.get('model_used', '')
+        if 'YOLO' in model_used or 'yolo' in str(analysis_id).lower():
+            report_type = "Cytology"
+            report_title = "Cytology Report"
+        elif 'CIN' in model_used or 'cin' in str(analysis_id).lower() or 'colpo' in str(analysis_id).lower():
+            report_type = "Colposcopy"
+            report_title = "Colposcopy Report"
+        elif 'histo' in str(analysis_id).lower() or 'histopathology' in model_used.lower():
+            report_type = "Histopathology"
+            report_title = "Histopathology Report"
+        else:
+            # Default fallback
+            report_type = "Cytology"
+            report_title = "Medical Analysis Report"
+        
+        # Build analysis metrics HTML based on report type
+        if report_type == "Histopathology":
+            # For histopathology, show Benign/Malignant confidence from the confidence dict
+            confidence_dict = ai_summary.get('confidence', {})
+            benign_conf = confidence_dict.get('Benign', 0) * 100 if isinstance(confidence_dict, dict) else 0
+            malignant_conf = confidence_dict.get('Malignant', 0) * 100 if isinstance(confidence_dict, dict) else 0
+            
+            analysis_metrics_html = f"""
+            <tr><th>System</th><td>Manalife AI System — Automated Analysis</td></tr>
+            <tr><th>Confidence Score</th><td>{ai_summary.get('avg_confidence', 'N/A')}%</td></tr>
+            <tr><th>Benign Confidence</th><td>{benign_conf:.2f}%</td></tr>
+            <tr><th>Malignant Confidence</th><td>{malignant_conf:.2f}%</td></tr>
+            """
+        elif report_type == "Cytology":
+            # For cytology (YOLO), show abnormal/normal cells
+            analysis_metrics_html = f"""
+            <tr><th>System</th><td>Manalife AI System — Automated Analysis</td></tr>
+            <tr><th>Confidence Score</th><td>{ai_summary.get('avg_confidence', 'N/A')}%</td></tr>
+            <tr><th>Abnormal Cells</th><td>{ai_summary.get('abnormal_cells', 'N/A')}</td></tr>
+            <tr><th>Normal Cells</th><td>{ai_summary.get('normal_cells', 'N/A')}</td></tr>
+            """
+        else:
+            # For CIN/Colposcopy or other models, show generic confidence
+            confidence_dict = ai_summary.get('confidence', {})
+            confidence_rows = ""
+            if isinstance(confidence_dict, dict):
+                for cls, val in confidence_dict.items():
+                    conf_pct = val * 100 if isinstance(val, (int, float)) else 0
+                    confidence_rows += f"<tr><th>{cls} Confidence</th><td>{conf_pct:.2f}%</td></tr>\n            "
+            
+            analysis_metrics_html = f"""
+            <tr><th>System</th><td>Manalife AI System — Automated Analysis</td></tr>
+            <tr><th>Confidence Score</th><td>{ai_summary.get('avg_confidence', 'N/A')}%</td></tr>
+            {confidence_rows}
+            """
+
+        # Build final HTML including download links and embedded annotated image
+        report_html = os.path.join(report_dir, "report.html")
+        json_url = f"/outputs/reports/{report_id}/report.json"
+        html_url = f"/outputs/reports/{report_id}/report.html"
+        annotated_img = report_data.get("analysis", {}).get("annotated_image_url") or ""
+        
+        # Get base URL for the annotated image (if it's a relative path)
+        annotated_img_full = f"http://localhost:8000{annotated_img}" if annotated_img and annotated_img.startswith('/') else annotated_img
+
+        download_pdf_btn = f'<a href="{pdf_url}" download style="text-decoration:none"><button class="btn-secondary">Download PDF</button></a>' if pdf_url else ''
+        
+        # Format generated time
+        generated_time = datetime.datetime.now().strftime('%b %d, %Y, %I:%M %p')
+
+        html_content = f"""<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width,initial-scale=1" />
+  <title>Medical Analysis Report — Manalife AI</title>
+  <style>
+    :root{{--bg:#f8fafc;--card:#ffffff;--muted:#6b7280;--accent:#0f172a}}
+    body{{font-family:Inter,ui-sans-serif,system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial;margin:0;background:var(--bg);color:var(--accent);line-height:1.45}}
+    .container{{max-width:900px;margin:36px auto;padding:20px}}
+    header{{display:flex;align-items:center;gap:16px}}
+    .brand{{display:flex;flex-direction:column}}
+    h1{{margin:0;font-size:20px}}
+    .sub{{color:var(--muted);font-size:13px}}
+    .card{{background:var(--card);box-shadow:0 6px 18px rgba(15,23,42,0.06);border-radius:12px;padding:20px;margin-top:18px}}
+    .grid{{display:grid;grid-template-columns:1fr 1fr;gap:12px}}
+    .section-title{{font-weight:600;margin-top:8px}}
+    table{{width:100%;border-collapse:collapse;margin-top:8px}}
+    td,th{{padding:8px;border-bottom:1px dashed #e6e9ef;text-align:left;font-size:14px}}
+    .full{{grid-column:1/-1}}
+    .muted{{color:var(--muted);font-size:13px}}
+    .footer{{margin-top:20px;font-size:13px;color:var(--muted)}}
+    .pill{{background:#eef2ff;color:#1e3a8a;padding:6px 10px;border-radius:999px;font-weight:600;font-size:13px}}
+    @media (max-width:700px){{.grid{{grid-template-columns:1fr}}}}
+    .signatures{{display:flex;gap:20px;flex-wrap:wrap;margin-top:12px}}
+    .sig{{background:#fbfbfd;border:1px solid #f0f1f5;padding:10px;border-radius:8px;min-width:180px}}
+    .annotated-image{{max-width:100%;height:auto;border-radius:8px;margin-top:12px;border:1px solid #e6e9ef}}
+    .btn-primary{{padding:10px 14px;border-radius:8px;border:1px solid #2563eb;background:#2563eb;color:white;font-weight:700;cursor:pointer}}
+    .btn-secondary{{padding:10px 14px;border-radius:8px;border:1px solid #e6eefc;background:#eef2ff;font-weight:700;cursor:pointer}}
+    .actions-bar{{margin-top:12px;display:flex;gap:8px;flex-wrap:wrap}}
+  </style>
+</head>
+<body>
+  <div class="container">
+    <header>
+            <div>
+                <!-- Use the static logo from frontend public/ (copied to dist by Vite) -->
+                <img src="/manalife_LOGO.jpg" alt="Manalife Logo" width="64" height="64">
+            </div>
+      <div class="brand">
+        <h1>MANALIFE AI — Medical Analysis</h1>
+        <div class="sub">Advanced cytological colposcopy and histopathology reporting</div>
+        <div class="muted">contact@manalife.ai • +1 (555) 123-4567</div>
+      </div>
+    </header>
+
+    <div class="card">
+      <div style="display:flex;justify-content:space-between;align-items:center;gap:12px;flex-wrap:wrap">
+        <div>
+          <div class="muted">MEDICAL ANALYSIS REPORT OF {report_type.upper()}</div>
+          <h2 style="margin:6px 0 0 0">{report_title}</h2>
+        </div>
+        <div style="text-align:right">
+          <div class="pill">Report ID: {report_id}</div>
+          <div class="muted" style="margin-top:6px">Generated: {generated_time}</div>
+        </div>
+      </div>
+
+      <hr style="border:none;border-top:1px solid #eef2f6;margin:16px 0">
+
+      <div class="grid">
+        <div>
+          <div class="section-title">Patient Information</div>
+          <table>
+            <tr><th>Patient ID</th><td>{patient_id}</td></tr>
+            <tr><th>Exam Date</th><td>{exam_date}</td></tr>
+            <tr><th>Physician</th><td>{metadata_dict.get('physician', 'N/A')}</td></tr>
+            <tr><th>Facility</th><td>{metadata_dict.get('facility', 'N/A')}</td></tr>
+          </table>
+        </div>
+
+        <div>
+          <div class="section-title">Sample Information</div>
+          <table>
+            <tr><th>Specimen Type</th><td>{metadata_dict.get('specimen_type', 'N/A')}</td></tr>
+            <tr><th>Clinical History</th><td>{metadata_dict.get('clinical_history', 'N/A')}</td></tr>
+            <tr><th>Collected</th><td>{exam_date}</td></tr>
+            <tr><th>Reported</th><td>{generated_time}</td></tr>
+          </table>
+        </div>
+
+        <div class="full">
+          <div class="section-title">AI-Assisted Analysis</div>
+          <table>
+            {analysis_metrics_html}
+          </table>
+          <div style="margin-top:12px;padding:12px;background:#f8fafc;border-radius:8px;border-left:4px solid #2563eb">
+            <div style="font-weight:600;margin-bottom:6px">AI Interpretation:</div>
+            <div class="muted">{ai_summary.get('ai_interpretation', 'No AI interpretation available.')}</div>
+          </div>
+        </div>
+
+        {'<div class="full"><div class="section-title">Annotated Analysis Image</div><img src="' + annotated_img_full + '" class="annotated-image" alt="Annotated Analysis Result" /></div>' if annotated_img else ''}
+
+        <div class="full">
+          <div class="section-title">Doctor\'s Notes</div>
+          <p class="muted">{notes or 'No additional notes provided.'}</p>
+        </div>
+
+        <div class="full">
+          <div class="section-title">Recommendations</div>
+          <p class="muted">Continue routine screening as per standard guidelines. Follow up as directed by your physician.</p>
+        </div>
+
+        <div class="full">
+          <div class="section-title">Signatures</div>
+          <div class="signatures">
+            <div class="sig">
+              <div style="font-weight:700">Dr. Emily Roberts</div>
+              <div class="muted">MD, pathologist</div>
+            </div>
+            <div class="sig">
+              <div style="font-weight:700">Dr. James Wilson</div>
+              <div class="muted">MD, pathologist</div>
+            </div>
+          </div>
+        </div>
+      </div>
+
+      <div class="footer">
+        <div>AI System: Manalife AI — Automated Analysis</div>
+        <div style="margin-top:6px">Report generated: {report_data['generated_at']}</div>
+      </div>
+    </div>
+
+    <div class="actions-bar">
+      {download_pdf_btn}
+      <button class="btn-secondary" onclick="window.print()">Print Report</button>
+    </div>
+  </div>
+</body>
+</html>"""
+
+        with open(report_html, "w", encoding="utf-8") as f:
+            f.write(html_content)
+
+        return {
+            "report_id": report_id,
+            "json_url": json_url,
+            "html_url": html_url,
+            "pdf_url": pdf_url,
+        }
+        
+    except Exception as e:
+        return JSONResponse(
+            content={"error": f"Failed to generate report: {str(e)}"},
+            status_code=500
+        )
+
+@app.get("/reports/{report_id}")
+async def get_report(report_id: str):
+    """Fetch a generated report by ID."""
+    report_dir = os.path.join(OUTPUT_DIR, "reports", report_id)
+    report_json = os.path.join(report_dir, "report.json")
+    
+    if not os.path.exists(report_json):
+        return JSONResponse(
+            content={"error": "Report not found"},
+            status_code=404
+        )
+        
+    with open(report_json, "r") as f:
+        report_data = json.load(f)
+    
+    return report_data
+
+@app.get("/reports")
+async def list_reports(patient_id: str = None):
+    """List all generated reports, optionally filtered by patient ID."""
+    reports_dir = os.path.join(OUTPUT_DIR, "reports")
+    if not os.path.exists(reports_dir):
+        return {"reports": []}
+        
+    reports = []
+    for report_id in os.listdir(reports_dir):
+        report_json = os.path.join(reports_dir, report_id, "report.json")
+        if os.path.exists(report_json):
+            with open(report_json, "r") as f:
+                report_data = json.load(f)
+                if not patient_id or report_data["patient"]["id"] == patient_id:
+                    reports.append({
+                        "report_id": report_id,
+                        "patient_id": report_data["patient"]["id"],
+                        "exam_date": report_data["patient"]["exam_date"],
+                        "generated_at": report_data["generated_at"]
+                    })
+                    
+    return {"reports": sorted(reports, key=lambda r: r["generated_at"], reverse=True)}
 
 @app.get("/models")
 def get_models():
     return {"available_models": ["yolo", "mwt", "cin", "histopathology"]}
 
-
 @app.get("/health")
 def health():
-    return {"message": "Unified Cervical & Breast Cancer API is running!"}
+    return {"message": "Pathora Medical Diagnostic API is running!"}
+
+# FRONTEND
+
+# =====================================================
 
-# After other app.mount()s
-app.mount("/outputs", StaticFiles(directory=OUTPUT_DIR), name="outputs")
-app.mount("/assets", StaticFiles(directory="frontend/dist/assets"), name="assets")
-from fastapi.staticfiles import StaticFiles
 
-app.mount("/", StaticFiles(directory="frontend/dist", html=True), name="static")
+# Serve frontend only if it has been built; avoid startup failure when dist/ is missing.
+FRONTEND_DIST = os.path.abspath(os.path.join(os.path.dirname(__file__), "../frontend/dist"))
 
+# Check if frontend/dist exists in /app (Docker), otherwise check relative to script location
+if not os.path.isdir(FRONTEND_DIST):
+    # Fallback for Docker: frontend is copied to ./frontend/dist during build
+    FRONTEND_DIST = os.path.join(os.path.dirname(__file__), "frontend/dist")
+
+ASSETS_DIR = os.path.join(FRONTEND_DIST, "assets")
+
+if os.path.isdir(ASSETS_DIR):
+    app.mount("/assets", StaticFiles(directory=ASSETS_DIR), name="assets")
+else:
+    print("ℹ️ Frontend assets directory not found — skipping /assets mount.")
 
 @app.get("/")
 async def serve_frontend():
-    index_path = os.path.join("frontend", "dist", "index.html")
-    return FileResponse(index_path)
+    index_path = os.path.join(FRONTEND_DIST, "index.html")
+    if os.path.isfile(index_path):
+        return FileResponse(index_path)
+    return JSONResponse({"message": "Backend is running. Frontend build not found."})
+
+@app.get("/{file_path:path}")
+async def serve_static_files(file_path: str):
+    """Serve static files from frontend dist (images, logos, etc.)"""
+    # Skip API routes
+    if file_path.startswith(("predict", "reports", "models", "health", "outputs", "assets", "cyto", "colpo", "histo")):
+        return JSONResponse({"error": "Not found"}, status_code=404)
+    
+    # Try to serve file from dist root
+    static_file = os.path.join(FRONTEND_DIST, file_path)
+    if os.path.isfile(static_file):
+        return FileResponse(static_file)
+    
+    # Fallback to index.html for client-side routing
+    index_path = os.path.join(FRONTEND_DIST, "index.html")
+    if os.path.isfile(index_path):
+        return FileResponse(index_path)
+    
+    return JSONResponse({"error": "Not found"}, status_code=404)
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
-    
\ No newline at end of file
+    # Use PORT provided by the environment (Hugging Face Spaces sets PORT=7860)
+    port = int(os.environ.get("PORT", 7860))
+    uvicorn.run(app, host="0.0.0.0", port=port)
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new file mode 100644
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diff --git a/frontend/public/colpo/nor3.jpg b/frontend/public/colpo/nor3.jpg
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diff --git a/frontend/public/cyto/HSIL1.jpg b/frontend/public/cyto/HSIL1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..70035118e44d3b610c532ef408a7893c89722dbe
--- /dev/null
+++ b/frontend/public/cyto/HSIL1.jpg
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+size 280092
diff --git a/frontend/public/cyto/HSIL2.JPG b/frontend/public/cyto/HSIL2.JPG
new file mode 100644
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--- /dev/null
+++ b/frontend/public/cyto/HSIL2.JPG
@@ -0,0 +1,3 @@
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+size 301744
diff --git a/frontend/public/cyto/LSIL1.png b/frontend/public/cyto/LSIL1.png
new file mode 100644
index 0000000000000000000000000000000000000000..bdcc09f779999e7db017bfacfa99bdcded7ce4a5
--- /dev/null
+++ b/frontend/public/cyto/LSIL1.png
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diff --git a/frontend/public/cyto/LSIL2.png b/frontend/public/cyto/LSIL2.png
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--- /dev/null
+++ b/frontend/public/cyto/LSIL2.png
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+size 481705
diff --git a/frontend/public/cyto/cyt1.jpg b/frontend/public/cyto/cyt1.jpg
deleted file mode 100644
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--- a/frontend/public/cyto/cyt1.jpg
+++ /dev/null
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-size 716187
diff --git a/frontend/public/cyto/cyt3.png b/frontend/public/cyto/cyt3.png
deleted file mode 100644
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--- a/frontend/public/cyto/cyt3.png
+++ /dev/null
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-size 5858373
diff --git a/frontend/public/cyto/neg1.bmp b/frontend/public/cyto/neg1.bmp
new file mode 100644
index 0000000000000000000000000000000000000000..1cc954a847d368a55c47f14578d358ec912baf5b
--- /dev/null
+++ b/frontend/public/cyto/neg1.bmp
@@ -0,0 +1,3 @@
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+size 9437238
diff --git a/frontend/public/cyto/cyt2.png b/frontend/public/cyto/neg2.png
similarity index 100%
rename from frontend/public/cyto/cyt2.png
rename to frontend/public/cyto/neg2.png
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index ac7c3486faf653fc9b8a0df1ac0738b52eead378..11015f45777216debabbd4a5a3f520ee0d3bf708 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -8,9 +8,6 @@ import { Footer } from "./components/Footer";
 import { ProgressBar } from "./components/progressbar";
 
 export function App() {
-// ----------------------------
-// State Management
-// ----------------------------
 const [selectedTest, setSelectedTest] = useState("cytology");
 const [uploadedImage, setUploadedImage] = useState<string | null>(null);
 const [selectedModel, setSelectedModel] = useState("");
@@ -19,18 +16,14 @@ const [showResults, setShowResults] = useState(false);
 const [currentStep, setCurrentStep] = useState(0);
 const [loading, setLoading] = useState(false);
 
-// ----------------------------
 // Progress bar logic
-// ----------------------------
 useEffect(() => {
 if (showResults) setCurrentStep(2);
 else if (uploadedImage) setCurrentStep(1);
 else setCurrentStep(0);
 }, [uploadedImage, showResults]);
 
-// ----------------------------
-// Reset logic — new test
-// ----------------------------
+// Reset logic
 useEffect(() => {
 setCurrentStep(0);
 setShowResults(false);
@@ -39,52 +32,79 @@ setSelectedModel("");
 setApiResult(null);
 }, [selectedTest]);
 
-// ----------------------------
-// Analyze handler (Backend call)
-// ----------------------------
-const handleAnalyze = async () => {
-if (!uploadedImage || !selectedModel) {
-alert("Please select a model and upload an image first!");
-return;
-}
-
-
-setLoading(true);
-setShowResults(false);
-setApiResult(null);
-
-try {
-  // Convert Base64 → File
-  const blob = await fetch(uploadedImage).then((r) => r.blob());
-  const file = new File([blob], "input.jpg", { type: blob.type });
-
-  const formData = new FormData();
-  formData.append("file", file);
-  formData.append("analysis_type", selectedTest);
-  formData.append("model_name", selectedModel);
-
-  // POST to backend
-const baseURL =
-      import.meta.env.MODE === "development"
+  const handleAnalyze = async () => {
+    console.log('Analyze button clicked', { uploadedImage, selectedModel });
+    if (!uploadedImage || !selectedModel) {
+      alert("Please select a model and upload an image first!");
+      return;
+    }
+
+    setLoading(true);
+    setShowResults(false);
+    setApiResult(null);
+
+    try {
+      // Extract file extension from data URL or use .jpg default
+      const extension = uploadedImage.startsWith('data:image/')
+        ? uploadedImage.split(';')[0].split('/')[1]
+        : 'jpg';
+      
+      // Create a more descriptive filename
+      const filename = `analysis_input.${extension}`;
+      
+      let blob: Blob;
+      if (uploadedImage.startsWith('data:')) {
+        // Handle data URLs (from file upload)
+        blob = await fetch(uploadedImage).then(r => r.blob());
+      } else {
+        // Handle sample images (relative URLs)
+        blob = await fetch(uploadedImage)
+          .then(r => r.blob())
+          .catch(() => {
+            // If fetch fails, try with base URL
+            const baseURL = import.meta.env.MODE === "development"
+              ? "http://127.0.0.1:5173"
+              : window.location.origin;
+            return fetch(`${baseURL}${uploadedImage}`).then(r => r.blob());
+          });
+      }
+
+      const file = new File([blob], filename, { type: blob.type || `image/${extension}` });
+      const formData = new FormData();
+      formData.append("file", file);
+      formData.append("model_name", selectedModel);
+
+      console.log('Sending request:', {
+        filename,
+        type: file.type,
+        size: file.size,
+        model: selectedModel
+      });
+
+      const baseURL = import.meta.env.MODE === "development"
         ? "http://127.0.0.1:8000"
         : window.location.origin;
 
-    const res = await axios.post(`${baseURL}/predict/`, formData, {
-      headers: { "Content-Type": "multipart/form-data" },
-    });
-
-    setApiResult(res.data);
-    setShowResults(true);
-  } catch (err) {
-    console.error("❌ Error during inference:", err);
-    alert("Error analyzing the image. Check backend logs.");
-  } finally {
-    setLoading(false);
-  }
+      const res = await axios.post(`${baseURL}/predict/`, formData, {
+        headers: { "Content-Type": "multipart/form-data" },
+      });
+
+      if (res.data.error) {
+        throw new Error(res.data.error);
+      }
+
+      console.log('Received response:', res.data);
+      setApiResult(res.data);
+      setShowResults(true);
+    } catch (err: any) {
+      console.error("❌ Error during inference:", err);
+      const errorMessage = err.response?.data?.error || err.message || "Unknown error occurred";
+      alert(`Error analyzing the image: ${errorMessage}`);
+    } finally {
+      setLoading(false);
+    }
 };
-// ----------------------------
-// Layout
-// ----------------------------
+
 return ( <div className="flex flex-col min-h-screen w-full bg-gray-50"> <Header /> <ProgressBar currentStep={currentStep} />
 
 
@@ -93,7 +113,6 @@ return ( <div className="flex flex-col min-h-screen w-full bg-gray-50"> <Header
 
     <main className="flex-1 p-6">
       <div className="max-w-7xl mx-auto grid grid-cols-1 lg:grid-cols-2 gap-6">
-        {/* Upload & Model Selection */}
         <UploadSection
           selectedTest={selectedTest}
           uploadedImage={uploadedImage}
@@ -103,10 +122,11 @@ return ( <div className="flex flex-col min-h-screen w-full bg-gray-50"> <Header
           onAnalyze={handleAnalyze}
         />
 
-        {/* Results Panel (show while loading OR when results ready) */}
-        {(loading || showResults) && (
+        {showResults && (
           <ResultsPanel
-            uploadedImage={apiResult?.annotated_image_url || uploadedImage}
+            uploadedImage={
+              apiResult?.annotated_image_url || uploadedImage
+            }
             result={apiResult}
             loading={loading}
           />
@@ -120,4 +140,4 @@ return ( <div className="flex flex-col min-h-screen w-full bg-gray-50"> <Header
 
 
 );
-}
\ No newline at end of file
+}
diff --git a/frontend/src/components/Footer.tsx b/frontend/src/components/Footer.tsx
index 66f5377d30f20b2b02f88e3cdf4226c582bad4e2..bb6873165d33dc76decc53d48ee9918c26dcc2e9 100644
--- a/frontend/src/components/Footer.tsx
+++ b/frontend/src/components/Footer.tsx
@@ -1,5 +1,3 @@
-import React from 'react';
-
 export function Footer() {
   return (
     <footer
@@ -10,7 +8,7 @@ export function Footer() {
       }}
     >
       {/* Overlay for readability */}
-      <div className="absolute inset-0 bg-gradient-to-t from-slate-900/95 via-slate-900/70 to-transparent" />
+      <div className="absolute inset-0 bg-gradient-to-t from-slate-900/80 via-slate-900/50 to-transparent" />
 
       {/* Main footer content */}
       <div className="relative max-w-7xl mx-auto px-8">
diff --git a/frontend/src/components/Header.tsx b/frontend/src/components/Header.tsx
index cccce8c9c74f068effc53db69219ed462941a9fc..49d0174feb74dadd5c364857226da88c49adf03b 100644
--- a/frontend/src/components/Header.tsx
+++ b/frontend/src/components/Header.tsx
@@ -10,7 +10,7 @@ export function Header() {
             "url('/banner.jpeg')",
         }}
       >
-        <div className="w-full h-full bg-gradient-to-r from-blue-900/80 to-teal-900/80 flex items-center px-8">
+        <div className="w-full h-full bg-gradient-to-r from-teal-900/40 to-blue-900/40 flex items-center px-8">
           {/* Logo + Title */}
           <div className="flex items-center gap-4">
             <img
diff --git a/frontend/src/components/ImageWithFallback.tsx b/frontend/src/components/ImageWithFallback.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..84591535b3fa73b08b7f51104b3f24c02fe854b1
--- /dev/null
+++ b/frontend/src/components/ImageWithFallback.tsx
@@ -0,0 +1,39 @@
+import { useEffect, useRef, useState } from "react";
+
+interface ImageWithFallbackProps {
+  src: string;
+  alt: string;
+  className?: string;
+}
+
+export function ImageWithFallback({ src, alt, className }: ImageWithFallbackProps) {
+  const imgRef = useRef<HTMLImageElement>(null);
+  const [imgSrc, setImgSrc] = useState(src);
+  const [error, setError] = useState(false);
+
+  useEffect(() => {
+    setImgSrc(src);
+    setError(false);
+  }, [src]);
+
+  const handleError = () => {
+    if (!error && imgSrc.startsWith('/')) {
+      setError(true);
+      // Try with explicit origin if relative URL
+      const baseURL = import.meta.env.MODE === "development"
+        ? "http://127.0.0.1:8000"
+        : window.location.origin;
+      setImgSrc(`${baseURL}${imgSrc}`);
+    }
+  };
+
+  return (
+    <img
+      ref={imgRef}
+      src={imgSrc}
+      alt={alt}
+      className={className}
+      onError={handleError}
+    />
+  );
+}
\ No newline at end of file
diff --git a/frontend/src/components/ReportModal.tsx b/frontend/src/components/ReportModal.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..8d85530bd740e93803822fa703b95da825dd4e19
--- /dev/null
+++ b/frontend/src/components/ReportModal.tsx
@@ -0,0 +1,226 @@
+import { useState } from 'react';
+import { XIcon } from 'lucide-react';
+
+interface ReportModalProps {
+  isOpen: boolean;
+  onClose: () => void;
+  onSubmit: (formData: FormData) => void; // ✅ changed
+  analysisId: string;
+  analysisSummaryJson: string;
+}
+
+
+export interface ReportFormData {
+  patient_id: string;
+  exam_date: string;
+  metadata: {
+    physician: string;
+    facility: string;
+    specimen_type: string;
+    clinical_history?: string;
+  };
+  notes?: string;
+  analysis_id: string;
+}
+
+
+export function ReportModal({ isOpen, onClose, onSubmit, analysisId, analysisSummaryJson }: ReportModalProps) {
+  const [formData, setFormData] = useState<ReportFormData>({
+    patient_id: '',
+    exam_date: new Date().toISOString().split('T')[0],
+    metadata: {
+      physician: '',
+      facility: '',
+      specimen_type: '',
+      clinical_history: '',
+    },
+    notes: '',
+    analysis_id: analysisId,
+  });
+
+  if (!isOpen) return null;
+
+  const handleSubmit = (e: React.FormEvent) => {
+  e.preventDefault();
+
+  // Build FormData for FastAPI endpoint
+  const payload = new FormData();
+  payload.append("patient_id", formData.patient_id);
+  payload.append("exam_date", formData.exam_date);
+  payload.append("metadata", JSON.stringify(formData.metadata));
+  payload.append("notes", formData.notes || "");
+  payload.append("analysis_id", formData.analysis_id);
+  payload.append("analysis_summary", analysisSummaryJson); // 🧠 from /predict/
+
+  // Pass the FormData object to the parent onSubmit
+  onSubmit(payload);
+};
+
+
+  const handleChange = (e: React.ChangeEvent<HTMLInputElement | HTMLTextAreaElement>) => {
+    const { name, value } = e.target;
+    if (name.startsWith('metadata.')) {
+      const field = name.split('.')[1];
+      setFormData(prev => ({
+        ...prev,
+        metadata: {
+          ...prev.metadata,
+          [field]: value,
+        },
+      }));
+    } else {
+      setFormData(prev => ({
+        ...prev,
+        [name]: value,
+      }));
+    }
+  };
+
+  return (
+    <div className="fixed inset-0 bg-black bg-opacity-50 z-50 flex items-center justify-center p-4">
+      <div className="bg-white rounded-lg shadow-xl max-w-2xl w-full max-h-[90vh] overflow-y-auto">
+        <div className="p-6 space-y-6">
+          <div className="flex items-center justify-between border-b pb-3">
+            <h2 className="text-2xl font-semibold text-gray-900">Generate Medical Report</h2>
+            <button
+              onClick={onClose}
+              className="text-gray-400 hover:text-gray-500"
+            >
+              <XIcon className="w-6 h-6" />
+            </button>
+          </div>
+
+          <form onSubmit={handleSubmit} className="space-y-6">
+            {/* Patient Information */}
+            <div className="space-y-4">
+              <h3 className="text-lg font-medium text-gray-900">Patient Information</h3>
+              <div className="grid grid-cols-1 gap-4 sm:grid-cols-2">
+                <div>
+                  <label htmlFor="patient_id" className="block text-sm font-medium text-gray-700">
+                    Patient ID *
+                  </label>
+                  <input
+                    required
+                    type="text"
+                    name="patient_id"
+                    id="patient_id"
+                    value={formData.patient_id}
+                    onChange={handleChange}
+                    className="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-blue-500 focus:ring-blue-500"
+                  />
+                </div>
+                <div>
+                  <label htmlFor="exam_date" className="block text-sm font-medium text-gray-700">
+                    Exam Date *
+                  </label>
+                  <input
+                    required
+                    type="date"
+                    name="exam_date"
+                    id="exam_date"
+                    value={formData.exam_date}
+                    onChange={handleChange}
+                    className="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-blue-500 focus:ring-blue-500"
+                  />
+                </div>
+              </div>
+            </div>
+
+            {/* Slide Metadata */}
+            <div className="space-y-4">
+              <h3 className="text-lg font-medium text-gray-900">Slide Metadata</h3>
+              <div className="grid grid-cols-1 gap-4 sm:grid-cols-2">
+                <div>
+                  <label htmlFor="metadata.physician" className="block text-sm font-medium text-gray-700">
+                    Physician Name *
+                  </label>
+                  <input
+                    required
+                    type="text"
+                    name="metadata.physician"
+                    id="metadata.physician"
+                    value={formData.metadata.physician}
+                    onChange={handleChange}
+                    className="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-blue-500 focus:ring-blue-500"
+                  />
+                </div>
+                <div>
+                  <label htmlFor="metadata.facility" className="block text-sm font-medium text-gray-700">
+                    Facility *
+                  </label>
+                  <input
+                    required
+                    type="text"
+                    name="metadata.facility"
+                    id="metadata.facility"
+                    value={formData.metadata.facility}
+                    onChange={handleChange}
+                    className="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-blue-500 focus:ring-blue-500"
+                  />
+                </div>
+                <div>
+                  <label htmlFor="metadata.specimen_type" className="block text-sm font-medium text-gray-700">
+                    Specimen Type *
+                  </label>
+                  <input
+                    required
+                    type="text"
+                    name="metadata.specimen_type"
+                    id="metadata.specimen_type"
+                    value={formData.metadata.specimen_type}
+                    onChange={handleChange}
+                    className="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-blue-500 focus:ring-blue-500"
+                  />
+                </div>
+                <div>
+                  <label htmlFor="metadata.clinical_history" className="block text-sm font-medium text-gray-700">
+                    Clinical History
+                  </label>
+                  <input
+                    type="text"
+                    name="metadata.clinical_history"
+                    id="metadata.clinical_history"
+                    value={formData.metadata.clinical_history}
+                    onChange={handleChange}
+                    className="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-blue-500 focus:ring-blue-500"
+                  />
+                </div>
+              </div>
+            </div>
+
+            {/* Notes */}
+            <div className="space-y-4">
+              <h3 className="text-lg font-medium text-gray-900">Doctor's Notes</h3>
+              <textarea
+                name="notes"
+                id="notes"
+                rows={4}
+                value={formData.notes}
+                onChange={handleChange}
+                placeholder="Add any additional notes or observations..."
+                className="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-blue-500 focus:ring-blue-500"
+              />
+            </div>
+
+            {/* Actions */}
+            <div className="flex items-center justify-end space-x-3 pt-4 border-t">
+              <button
+                type="button"
+                onClick={onClose}
+                className="px-4 py-2 text-sm font-medium text-gray-700 hover:text-gray-500"
+              >
+                Cancel
+              </button>
+              <button
+                type="submit"
+                className="px-4 py-2 text-sm font-medium text-white bg-blue-600 rounded-md hover:bg-blue-700 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500"
+              >
+                Generate Report
+              </button>
+            </div>
+          </form>
+        </div>
+      </div>
+    </div>
+  );
+}
\ No newline at end of file
diff --git a/frontend/src/components/ResultsPanel.tsx b/frontend/src/components/ResultsPanel.tsx
index 8e51d6ae4b3c0904a2299aad9e65ac7cf084c5f9..bc6b4e8f33237ac8c83c3f82bc8d90be54084e4c 100644
--- a/frontend/src/components/ResultsPanel.tsx
+++ b/frontend/src/components/ResultsPanel.tsx
@@ -1,183 +1,225 @@
-import { useState, useEffect } from "react";
-import { DownloadIcon, InfoIcon, Loader2Icon } from "lucide-react";
+import { useState } from "react";
+import { DownloadIcon, FileTextIcon, Loader2Icon } from "lucide-react";
+import { ImageWithFallback } from "./ImageWithFallback";
+import { ReportModal } from "./ReportModal";
+import axios from "axios";
 
 interface ResultsPanelProps {
-  uploadedImage: string | null;
-  result?: any;
-  loading?: boolean;
+uploadedImage: string | null;
+result?: any;
+loading?: boolean;
 }
 
 export function ResultsPanel({ uploadedImage, result, loading }: ResultsPanelProps) {
-  const [isModalOpen, setIsModalOpen] = useState(false);
-  const [modalImage, setModalImage] = useState<string | null>(null);
-
-  useEffect(() => {
-    if (result?.annotated_image_url) {
-      setModalImage(result.annotated_image_url);
-    } else if (uploadedImage) {
-      setModalImage(uploadedImage);
-    } else {
-      setModalImage(null);
+  const [showReportModal, setShowReportModal] = useState(false);
+  
+  const handleGenerateReport = async (formData: FormData) => {
+    try {
+      const baseURL = import.meta.env.MODE === "development"
+        ? "http://127.0.0.1:8000"
+        : window.location.origin;
+
+      const response = await axios.post(`${baseURL}/reports/`, formData, {
+        headers: { "Content-Type": "multipart/form-data" },
+      });
+
+      if (response.data.html_url) {
+        // Open report in new tab
+        window.open(`${baseURL}${response.data.html_url}`, "_blank");
+      }
+      if (response.data.pdf_url) {
+        // Open PDF in new tab when available
+        window.open(`${baseURL}${response.data.pdf_url}`, "_blank");
+      }
+      
+      setShowReportModal(false);
+    } catch (err: any) {
+      console.error("Failed to generate report:", err);
+      alert(err.response?.data?.error || "Failed to generate report");
     }
-  }, [result, uploadedImage]);
+  };
 
-  // Loading state UI
   if (loading) {
-    return (
-      <div className="bg-white rounded-lg shadow-sm p-8 flex flex-col items-center justify-center">
-        <Loader2Icon className="w-12 h-12 text-transparent bg-clip-text bg-gradient-to-r from-blue-800 to-teal-600 animate-spin mb-4" />
-        <p className="text-gray-700 font-medium mb-2">Analyzing image... Please wait</p>
-        <div className="w-56 h-2 rounded-full overflow-hidden mt-2">
-          <div className="h-full bg-gradient-to-r from-blue-800 to-teal-600 animate-pulse" />
-        </div>
+    return ( 
+      <div className="bg-white rounded-lg shadow-sm p-6 flex flex-col items-center justify-center"> 
+        <Loader2Icon className="w-10 h-10 text-blue-600 animate-spin mb-3" /> 
+        <p className="text-gray-600 font-medium">Analyzing image...</p> 
       </div>
     );
   }
 
   if (!result) {
-    return (
+    return ( 
       <div className="bg-white rounded-lg shadow-sm p-6 text-center text-gray-500">
-        No analysis result available yet.
+        No analysis result available yet. 
       </div>
     );
-  }
-
-  const {
-    prediction,
-    confidence,
-    probabilities,
-    detections,
-    summary,
-    annotated_image_url,
-    model_name,
-    analysis_type,
-  } = result;
-
-  const handleDownload = () => {
-    if (annotated_image_url) {
-      const link = document.createElement("a");
-      link.href = annotated_image_url;
-      link.download = "analysis_result.jpg";
-      link.click();
-    }
-  };
-
-  const openModal = (url?: string) => {
-    setModalImage(url || annotated_image_url || uploadedImage);
-    setIsModalOpen(true);
-  };
-
-  const closeModal = () => {
-    setIsModalOpen(false);
-  };
+}
 
-  return (
-    <div className="bg-white rounded-lg shadow-sm p-6">
-      {/* Header */}
-      <div className="flex items-center justify-between mb-6">
-        <div>
-          <h2 className="text-2xl font-bold text-gray-800">
-            {model_name ? model_name.toUpperCase() : "Analysis Result"}
-          </h2>
-          <p className="text-sm text-gray-500 capitalize">{analysis_type || "Test Type"}</p>
-        </div>
+const {
+model_used,
+detections,
+annotated_image_url,
+summary,
+  // prediction (not used here)
+confidence,
+} = result;
+
+const handleDownload = () => {
+if (annotated_image_url) {
+const link = document.createElement("a");
+link.href = annotated_image_url;
+link.download = "analysis_result.jpg";
+link.click();
+}
+};
+
+return ( <div className="bg-white rounded-lg shadow-sm p-6">
+{/* Header */} 
+  <div className="flex items-center justify-between mb-6"> 
+    <div> 
+      <h2 className="text-2xl font-bold text-gray-800">
+        {model_used || "Analysis Result"} 
+      </h2> 
+      <p className="text-sm text-gray-500">Automated Image Analysis</p> 
+    </div>
 
-        {annotated_image_url && (
-          <button
-            onClick={handleDownload}
-            className="flex items-center gap-2 bg-gradient-to-r from-blue-800 to-teal-600 text-white px-4 py-2 rounded-lg hover:opacity-90 transition-all"
-          >
-            <DownloadIcon className="w-4 h-4" />
-            Download Image
-          </button>
-        )}
+    <div className="flex items-center gap-3">
+      {annotated_image_url && (
+        <button
+          onClick={handleDownload}
+          className="flex items-center gap-2 bg-green-600 text-white px-4 py-2 rounded-lg hover:bg-green-700 transition-colors"
+        >
+          <DownloadIcon className="w-4 h-4" />
+          Download Image
+        </button>
+      )}
+      <button
+        onClick={() => setShowReportModal(true)}
+        className="flex items-center gap-2 bg-blue-600 text-white px-4 py-2 rounded-lg hover:bg-blue-700 transition-colors"
+      >
+        <FileTextIcon className="w-4 h-4" />
+        Generate Report
+      </button>
+    </div>
+  </div>
+
+  {/* Image */}
+  <div className="relative mb-6 rounded-lg overflow-hidden border border-gray-200">
+    <ImageWithFallback
+      src={annotated_image_url || uploadedImage || "/ui.jpg"}
+      alt="Analysis Result"
+      className="w-full h-64 object-cover"
+    />
+  </div>
+
+  {/* Summary Section */}
+  {summary && (
+    <div className="bg-gray-50 p-4 rounded-lg mb-6">
+      <h3 className="text-lg font-semibold text-gray-800 mb-2">
+        AI Summary
+      </h3>
+      <p className="text-gray-700 text-sm leading-relaxed">
+        <strong>Abnormal Cells:</strong> {summary.abnormal_cells} <br />
+        <strong>Normal Cells:</strong> {summary.normal_cells} <br />
+        <strong>Average Confidence:</strong> {summary.avg_confidence?.toFixed(2)}% <br />
+      </p>
+      <div className="mt-3 text-gray-800 text-sm italic border-t pt-2">
+        {summary.ai_interpretation || "No AI interpretation available."}
       </div>
+    </div>
+  )}
+
+  {/* Detection list */}
+  {detections && detections.length > 0 && (
+    <div className="mb-6">
+      <h4 className="font-semibold text-gray-900 mb-3">
+        Detected Objects
+      </h4>
+      <ul className="text-sm text-gray-700 list-disc list-inside space-y-1">
+        {detections.map((det: any, i: number) => (
+          <li key={i}>
+            {det.name || "Object"} – {(det.confidence * 100).toFixed(1)}%
+          </li>
+        ))}
+      </ul>
+    </div>
+  )}
 
-      {/* Image (click to zoom) */}
-      <div className="relative mb-6 rounded-lg overflow-hidden border border-gray-200">
-        <img
-          src={annotated_image_url || uploadedImage || "/ui.jpg"}
-          alt="Analysis Result"
-          className="w-full h-64 object-cover cursor-pointer transition-transform hover:scale-105"
-          onClick={() => openModal()}
-        />
-      </div>
+  {/* Probability / MWT visualization */}
+  {confidence && (
+    <div className="mb-6">
+      <h4 className="font-semibold text-gray-900 mb-3">Confidence Levels</h4>
 
-      {/* Results Summary */}
-      <div className="mb-6">
-        {prediction && (
-          <h3
-            className={`text-3xl font-bold ${
-              prediction.toLowerCase().includes("malignant") || prediction.toLowerCase().includes("abnormal")
-                ? "text-red-600"
-                : "text-green-600"
-            }`}
-          >
-            {prediction}
-          </h3>
-        )}
-
-        {confidence && (
-          <div className="mt-2">
+  {/* If MWT, CIN, or Histopathology classifier, show a visual bar for average confidence and per-class bars */}
+  {model_used && /mwt|cin|histopathology/i.test(model_used) ? (
+        <div>
+          {/* Average confidence bar */}
+          <div className="mb-3">
             <div className="flex items-center justify-between mb-1">
-              <span className="font-semibold text-gray-900">Confidence: {(confidence * 100).toFixed(2)}%</span>
-              <InfoIcon className="w-4 h-4 text-gray-400" />
+              <span className="text-sm font-medium text-gray-700">Average confidence</span>
+              <span className="text-sm font-mono text-gray-600">
+                {summary?.avg_confidence ? `${summary.avg_confidence.toFixed(2)}%` : "-"}
+              </span>
             </div>
-            <div className="w-full h-3 bg-gray-200 rounded-full overflow-hidden">
+            <div className="w-full bg-gray-200 rounded-full h-4 overflow-hidden">
               <div
-                className={`h-full ${
-                  confidence > 0.7 ? "bg-green-500" : confidence > 0.4 ? "bg-yellow-500" : "bg-red-500"
-                }`}
-                style={{ width: `${confidence * 100}%` }}
+                className="h-4 bg-gradient-to-r from-amber-600 to-amber-400"
+                style={{ width: `${summary?.avg_confidence ?? 0}%` }}
               />
             </div>
           </div>
-        )}
-
-        {summary && (
-          <p className="mt-4 text-gray-700 text-sm leading-relaxed">{summary}</p>
-        )}
-      </div>
-
-      {/* Detections / Probabilities */}
-      {detections && detections.length > 0 && (
-        <div className="mb-6">
-          <h4 className="font-semibold text-gray-900 mb-3">Detected Regions:</h4>
-          <ul className="text-sm text-gray-700 list-disc list-inside space-y-1">
-            {detections.map((det: any, i: number) => (
-              <li key={i}>{det.name || "object"} – {(det.confidence * 100).toFixed(1)}%</li>
-            ))}
-          </ul>
-        </div>
-      )}
 
-      {probabilities && (
-        <div className="mb-6">
-          <h4 className="font-semibold text-gray-900 mb-3">Class Probabilities:</h4>
-          <pre className="bg-gray-100 rounded-lg p-3 text-sm">{JSON.stringify(probabilities, null, 2)}</pre>
-        </div>
-      )}
-
-      {/* Report Button */}
-      <button className="w-full bg-gradient-to-r from-blue-800 to-teal-600 text-white py-3 rounded-lg font-medium hover:opacity-95 transition-colors flex items-center justify-center gap-2">
-        <DownloadIcon className="w-5 h-5" />
-        Generate Report
-      </button>
+          {/* Per-class bars */}
+          <div className="space-y-2">
+            {Object.entries(confidence).map(([cls, val]) => {
+              const num = Number(val as any) || 0;
+              const pct = (num * 100);
+              
+              // Color coding: Positive/Malignant/High-grade = red, Negative/Benign/Low-grade = green
+              const isNegative = cls.toLowerCase().includes("negative") || 
+                                cls.toLowerCase().includes("benign") || 
+                                cls.toLowerCase().includes("low-grade");
+              
+              return (
+                <div key={cls}>
+                  <div className="flex items-center justify-between text-sm mb-1">
+                    <span className="text-gray-700">{cls}</span>
+                    <span className="text-gray-600">{pct.toFixed(2)}%</span>
+                  </div>
+                  <div className="w-full bg-gray-100 rounded-full h-3">
+                    <div
+                      className={`h-3 rounded-full ${isNegative ? "bg-green-500" : "bg-red-500"}`}
+                      style={{ width: `${pct.toFixed(2)}%` }}
+                    />
+                  </div>
+                </div>
+              );
+            })}
+          </div>
 
-      {/* Modal for zoom */}
-      {isModalOpen && modalImage && (
-        <div
-          className="fixed inset-0 bg-black/70 z-50 flex items-center justify-center p-4"
-          onClick={closeModal}
-        >
-          <div className="max-w-5xl max-h-[90vh] w-full" onClick={(e) => e.stopPropagation()}>
-            <img src={modalImage} alt="Zoomed result" className="w-full h-auto object-contain rounded-lg shadow-2xl" />
-            <div className="mt-3 text-right">
-              <button onClick={closeModal} className="px-4 py-2 bg-white rounded-md">Close</button>
-            </div>
+          {/* Mistral comment */}
+          <div className="mt-4 bg-gray-50 p-3 rounded-lg text-sm italic text-gray-800 border-t">
+            {summary?.ai_interpretation || "No AI interpretation available."}
           </div>
         </div>
+      ) : (
+        // Fallback display for non-MWT models
+        <pre className="bg-gray-100 rounded-lg p-3 text-sm overflow-x-auto">
+          {JSON.stringify(confidence, null, 2)}
+        </pre>
       )}
     </div>
-  );
-}
\ No newline at end of file
+  )}
+
+  {/* Report Generation Modal */}
+  <ReportModal 
+    isOpen={showReportModal}
+    onClose={() => setShowReportModal(false)}
+    onSubmit={handleGenerateReport}
+    analysisId={annotated_image_url || ""}
+    analysisSummaryJson={summary ? JSON.stringify({ ...summary, model_used, confidence }) : "{}"}
+  />
+</div>
+);
+}
diff --git a/frontend/src/components/Sidebar.tsx b/frontend/src/components/Sidebar.tsx
index 7718da927e6d6492d6cb12734474644853b4dfd3..44fd9a2f4663a04cb99e2dde02caa7cb7bfd29d6 100644
--- a/frontend/src/components/Sidebar.tsx
+++ b/frontend/src/components/Sidebar.tsx
@@ -1,4 +1,12 @@
-import { FileTextIcon, HelpCircleIcon } from 'lucide-react';
+import { useState } from "react";
+import {
+  FileTextIcon,
+  HelpCircleIcon,
+  PlusCircle,
+  ChevronDown,
+  ChevronRight,
+} from "lucide-react";
+import { Microscope, Stethoscope, FlaskConical } from "lucide-react";
 
 interface SidebarProps {
   selectedTest: string;
@@ -6,57 +14,81 @@ interface SidebarProps {
 }
 
 export function Sidebar({ selectedTest, onTestChange }: SidebarProps) {
+  const [isTestOpen, setIsTestOpen] = useState(true);
+
   const testTypes = [
-    { value: 'cytology', label: 'Cytology Analysis' },
-    { value: 'colposcopy', label: 'Colposcopy Analysis' },
-    { value: 'histopathology', label: 'Histopathology Analysis' },
+    { value: "cytology", label: "Cytology Analysis", icon: Microscope },
+    { value: "colposcopy", label: "Colposcopy Analysis", icon: Stethoscope },
+    { value: "histopathology", label: "Histopathology Analysis", icon: FlaskConical },
   ];
 
   return (
-    <aside className="w-64 bg-white border-r border-gray-200 p-4">
-      <div className="space-y-4">
-        {/* Gradient Header */}
-        <h3 className="text-lg font-semibold text-white mb-3 px-3 py-2 rounded-md bg-gradient-to-r from-blue-800 to-teal-600 shadow-sm">
-          New Test
-        </h3>
-
-        {/* Test Selection */}
-        <div className="flex flex-col gap-2">
-          {testTypes.map((test) => (
-            <label
-              key={test.value}
-              className={`flex items-center gap-3 px-3 py-2 rounded-lg cursor-pointer border transition-all duration-200 
-                ${
-                  selectedTest === test.value
-                    ? 'border-blue-600 bg-blue-50 text-blue-900 font-medium shadow-sm'
-                    : 'border-gray-200 hover:border-blue-300 hover:bg-gray-50 text-gray-700'
-                }`}
-            >
-              <input
-                type="radio"
-                name="testType"
-                value={test.value}
-                checked={selectedTest === test.value}
-                onChange={() => onTestChange(test.value)}
-                className="form-radio h-4 w-4 text-blue-600 focus:ring-blue-500"
-              />
-              <span>{test.label}</span>
-            </label>
-          ))}
+    <aside className="w-72 h-screen bg-white border-r border-gray-200 p-4 flex flex-col justify-between">
+      <div className="space-y-2">
+        {/* --- Start New Test Dropdown --- */}
+        <div>
+          <button
+            onClick={() => setIsTestOpen(!isTestOpen)}
+            className="flex items-center justify-between w-full px-3 py-3 rounded-lg hover:bg-gray-50 transition-colors text-gray-800 font-semibold"
+          >
+            <div className="flex items-center gap-3">
+              <PlusCircle className="w-5 h-5 text-teal-600" />
+              <span>Start New Test</span>
+            </div>
+            {isTestOpen ? (
+              <ChevronDown className="w-5 h-5 text-gray-500" />
+            ) : (
+              <ChevronRight className="w-5 h-5 text-gray-500" />
+            )}
+          </button>
+
+          {isTestOpen && (
+            <div className="mt-1 ml-6 space-y-1">
+              {testTypes.map(({ value, label, icon: Icon }) => (
+                <button
+                  key={value}
+                  onClick={() => onTestChange(value)}
+                  className={`flex items-center w-full gap-3 px-3 py-2 rounded-md text-sm transition-all duration-200
+                    ${
+                      selectedTest === value
+                        ? "bg-gradient-to-r from-teal-700 to-teal-600 text-white font-medium shadow-sm"
+                        : "text-gray-700 hover:bg-gray-50"
+                    }`}
+                >
+                  <Icon
+                    className={`w-4 h-4 ${
+                      selectedTest === value ? "text-white" : "text-gray-500"
+                    }`}
+                  />
+                  <span
+                    className={`${
+                      selectedTest === value ? "text-white" : "text-gray-700"
+                    }`}
+                  >
+                    {label}
+                  </span>
+                </button>
+              ))}
+            </div>
+          )}
         </div>
 
-        {/* History */}
-        <button className="w-full flex items-center gap-3 px-4 py-3 text-gray-700 hover:bg-gray-50 rounded-lg transition-colors">
-          <FileTextIcon className="w-5 h-5" />
-          <span>History</span>
-        </button>
-
-        {/* Help */}
-        <button className="w-full flex items-center gap-3 px-4 py-3 text-gray-700 hover:bg-gray-50 rounded-lg transition-colors">
-          <HelpCircleIcon className="w-5 h-5" />
-          <span>Help</span>
-        </button>
+        {/* --- Divider --- */}
+        <hr className="border-gray-200 my-4" />
+
+        {/* --- Other Options --- */}
+        <div>
+          <button className="w-full flex items-center gap-3 px-3 py-3 text-gray-700 hover:bg-gray-50 rounded-lg transition-colors">
+            <FileTextIcon className="w-5 h-5 text-gray-500" />
+            <span>History</span>
+          </button>
+
+          <button className="w-full flex items-center gap-3 px-3 py-3 text-gray-700 hover:bg-gray-50 rounded-lg transition-colors">
+            <HelpCircleIcon className="w-5 h-5 text-gray-500" />
+            <span>Help</span>
+          </button>
+        </div>
       </div>
     </aside>
   );
-}
\ No newline at end of file
+}
diff --git a/frontend/src/components/UploadSection.tsx b/frontend/src/components/UploadSection.tsx
index 5dcc4b4deed6e5f0da6efae1c28c24d8b4426034..dd88d046e07e3a8bc58dadbb07706eec4bce296e 100644
--- a/frontend/src/components/UploadSection.tsx
+++ b/frontend/src/components/UploadSection.tsx
@@ -154,7 +154,12 @@ export function UploadSection({
 
       {/* Analyze Button */}
       <button
-        onClick={onAnalyze}
+        type="button"
+        onClick={(e) => {
+          // Prevent clicks from bubbling to the upload area (which would trigger file select)
+          e.stopPropagation();
+          onAnalyze();
+        }}
         disabled={!uploadedImage || !selectedModel}
         className={`w-full mt-6 text-white py-3 rounded-lg font-medium transition-colors ${
           !uploadedImage || !selectedModel
diff --git a/frontend/src/components/progressbar.tsx b/frontend/src/components/progressbar.tsx
index 28e763212ce04c7d1d81c3c43d73a4fd6de66aa2..abe609301528e3f06a2ad3ee1d7ff678ca283980 100644
--- a/frontend/src/components/progressbar.tsx
+++ b/frontend/src/components/progressbar.tsx
@@ -22,9 +22,9 @@ export function ProgressBar({ currentStep }: ProgressBarProps) {
               <div
                 className={`w-12 h-12 rounded-full flex items-center justify-center text-white font-semibold transition-all duration-300 ${
                   currentStep > step.index
-                    ? "bg-gradient-to-r from-blue-800 to-teal-600"
+                    ? "bg-gradient-to-r from-teal-500 to-teal-700"
                     : currentStep === step.index
-                    ? "bg-gradient-to-r from-blue-600 to-teal-500"
+                    ? "bg-gradient-to-r from-teal-600 to-blue-700"
                     : "bg-gray-300 text-gray-600"
                 }`}
               >
@@ -48,7 +48,7 @@ export function ProgressBar({ currentStep }: ProgressBarProps) {
               <div
                 className={`h-1 w-32 mx-4 rounded-full transition-all duration-300 ${
                   currentStep > step.index
-                    ? "bg-gradient-to-r from-blue-800 to-teal-600"
+                    ? "bg-gradient-to-r from-teal-500 via-teal-600 to-blue-600"
                     : "bg-gray-300"
                 }`}
               />
@@ -58,4 +58,4 @@ export function ProgressBar({ currentStep }: ProgressBarProps) {
       </div>
     </div>
   );
-}
+}
\ No newline at end of file
diff --git a/frontend/src/env.d.ts b/frontend/src/env.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..a27efa842f35521e40da09cf3b782c1a5afac306
--- /dev/null
+++ b/frontend/src/env.d.ts
@@ -0,0 +1,10 @@
+/// <reference types="vite/client" />
+
+interface ImportMetaEnv {
+  readonly MODE: string
+  // Add other env variables as needed
+}
+
+interface ImportMeta {
+  readonly env: ImportMetaEnv
+}
\ No newline at end of file
diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json
index a7fc6fbf23de2a53e36754bc4a2c306d0291d7b2..d50dc0776124ae1dfbd856d84dd8776bc99102cc 100644
--- a/frontend/tsconfig.json
+++ b/frontend/tsconfig.json
@@ -3,11 +3,11 @@
     "target": "ES2020",
     "useDefineForClassFields": true,
     "lib": ["ES2020", "DOM", "DOM.Iterable"],
-    "module": "ESNext",
+      "module": "esnext",
     "skipLibCheck": true,
 
     /* Bundler mode */
-    "moduleResolution": "bundler",
+      "moduleResolution": "node",
     "allowImportingTsExtensions": true,
     "resolveJsonModule": true,
     "isolatedModules": true,
@@ -18,8 +18,9 @@
     "strict": true,
     "noUnusedLocals": true,
     "noUnusedParameters": true,
-    "noFallthroughCasesInSwitch": true
+    "noFallthroughCasesInSwitch": true,
+    "allowSyntheticDefaultImports": true
   },
-  "include": ["src"],
+  "include": ["src", "src/env.d.ts"],
   "references": [{ "path": "./tsconfig.node.json" }]
 }
diff --git a/tempCodeRunnerFile.python b/tempCodeRunnerFile.python
new file mode 100644
index 0000000000000000000000000000000000000000..a1c315b33e650c201b1d80c0fb0c254774923a3c
--- /dev/null
+++ b/tempCodeRunnerFile.python
@@ -0,0 +1,6 @@
+from huggingface_hub import InferenceClient
+import os
+
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3", token=os.getenv("HF_TOKEN"))
+res = client.text_generation("Hello, this is a test.", max_new_tokens=20)
+print(res)