update space

.idea/.gitignore

@@ -1,8 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml
-# Editor-based HTTP Client requests
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml

.idea/Generative_AI_with_poultry_disease_detection_system_v2.iml

@@ -1,11 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
-      <excludeFolder url="file://$MODULE_DIR$/.venv" />
-    </content>
-    <orderEntry type="jdk" jdkName="Python 3.12 (Generative_AI_with_poultry_disease_detection_system_v2)" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-    <orderEntry type="library" name="jquery-3.6.0" level="application" />
-  </component>
-</module>

.idea/git_toolbox_blame.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="GitToolBoxBlameSettings">
-    <option name="version" value="2" />
-  </component>
-</project>

.idea/git_toolbox_prj.xml

@@ -1,15 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="GitToolBoxProjectSettings">
-    <option name="commitMessageIssueKeyValidationOverride">
-      <BoolValueOverride>
-        <option name="enabled" value="true" />
-      </BoolValueOverride>
-    </option>
-    <option name="commitMessageValidationEnabledOverride">
-      <BoolValueOverride>
-        <option name="enabled" value="true" />
-      </BoolValueOverride>
-    </option>
-  </component>
-</project>

.idea/inspectionProfiles/profiles_settings.xml

@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>

.idea/jsLibraryMappings.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="JavaScriptLibraryMappings">
-    <file url="file://$PROJECT_DIR$" libraries="{jquery-3.6.0}" />
-  </component>
-</project>

.idea/misc.xml

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Black">
-    <option name="sdkName" value="Python 3.12 (Generative_AI_with_poultry)" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (Generative_AI_with_poultry_disease_detection_system_v2)" project-jdk-type="Python SDK" />
-</project>

.idea/modules.xml

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/Generative_AI_with_poultry_disease_detection_system_v2.iml" filepath="$PROJECT_DIR$/.idea/Generative_AI_with_poultry_disease_detection_system_v2.iml" />
-    </modules>
-  </component>
-</project>

.idea/vcs.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="" vcs="Git" />
-  </component>
-</project>

app.py

@@ -3,7 +3,6 @@
 import os
 import time
 import logging
-from datetime import datetime
 from fastapi import FastAPI, Request, HTTPException, BackgroundTasks
 from fastapi.templating import Jinja2Templates
 from fastapi.responses import HTMLResponse
@@ -11,13 +10,22 @@ from fastapi.staticfiles import StaticFiles
 import tensorflow as tf
 from routes.authentication import auth_router
 from routes.disease_detection import disease_router
-from services.health_monitoring_service import evaluate_health_data
+from routes.health_dashboard import dashboard_router
+from services.health_monitoring_service import evaluate_health_data, get_health_alerts, send_alerts
+from huggingface_hub import login
 
 # Setup logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Check GPU availability
+# Load Hugging Face Token
+HF_TOKEN = os.getenv('HF_Token')
+if HF_TOKEN:
+    login(token=HF_TOKEN, add_to_git_credential=True)
+else:
+    logger.warning("Hugging Face token not found in environment variables.")
+
+# Check GPU availability for TensorFlow
 gpu_devices = tf.config.list_physical_devices('GPU')
 if gpu_devices:
     logger.info(f"GPUs available: {len(gpu_devices)}")
@@ -32,14 +40,15 @@ templates = Jinja2Templates(directory="templates")
 static_dir = "static"
 if os.path.isdir(static_dir):
     app.mount("/static", StaticFiles(directory=static_dir), name="static")
-    logger.info(f"Mounted static directory at /static")
+    logger.info("Mounted static directory at /static")
 else:
     logger.error("Static directory not found.")
     raise HTTPException(status_code=500, detail="Static directory not found.")
 
-# Include routers for authentication and disease detection
+# Include routers for authentication, disease detection, and health dashboard
 app.include_router(auth_router, prefix="/auth", tags=["Authentication"])
 app.include_router(disease_router, prefix="/disease", tags=["Disease Detection"])
+app.include_router(dashboard_router, prefix="/dashboard", tags=["Dashboard"])
 
 @app.get("/", response_class=HTMLResponse)
 async def landing_page(request: Request):
@@ -47,7 +56,7 @@ async def landing_page(request: Request):
     logger.info("Landing page accessed")
     return templates.TemplateResponse("landing.html", {"request": request})
 
-# Example health metrics for periodic monitoring
+# health metrics for periodic monitoring
 health_metrics = {
     "weight_loss_percentage": 6,
     "mortality_rate": 1,
@@ -55,15 +64,18 @@ health_metrics = {
 }
 
 def monitor_health():
-    """Periodically evaluate health data and log notifications if thresholds are crossed."""
+    """Evaluate health data and log notifications if thresholds are crossed."""
     while True:
+        # Evaluate health metrics and send alerts
         notifications = evaluate_health_data(health_metrics)
         if notifications["notifications"]:
             logger.info(f"Health Notifications: {notifications['notifications']}")
+            # Optionally, send alerts to the farmer
+            send_alerts(notifications["notifications"], os.getenv("FARMER_EMAIL"))
         time.sleep(3600)  # Run every hour
 
 @app.on_event("startup")
 async def startup_event():
-    """Start background health monitoring on startup."""
+    """Initialize background health monitoring on startup."""
     logger.info("Starting background health monitoring.")
     BackgroundTasks().add_task(monitor_health)

requirements.txt

@@ -10,4 +10,5 @@ jinja2
 python-multipart
 numpy
 pillow
-tensorflow[and-cuda]~=2.9
+tensorflow[and-cuda]~=2.9
+huggingface_hub

routes/disease_detection.py

@@ -3,16 +3,11 @@
 from fastapi import APIRouter, UploadFile, File, HTTPException
 from fastapi.responses import JSONResponse
 from PIL import Image
-import io
 import numpy as np
-from services.image_preprocessing import preprocess_image
-from services.disease_detection_service import load_disease_model
-from services.health_monitoring_service import get_treatment_recommendation
-
+import io
+from services.disease_detection_service import bot
 
 disease_router = APIRouter()
-model = load_disease_model()
-
 
 
 @disease_router.post("/detect_disease", response_class=JSONResponse)
@@ -21,36 +16,34 @@ async def detect_disease(file: UploadFile = File(...)):
     Detect disease from uploaded poultry fecal image and provide treatment suggestions.
 
     Parameters:
-        file (UploadFile): Uploaded image file to analyze.
+        file (UploadFile): Image file to analyze.
 
     Returns:
-        JSONResponse: Prediction result with disease notification and treatment suggestion.
+        JSONResponse: Prediction results including disease, status, confidence, recommendation, and details.
     """
     if file.content_type not in ["image/jpeg", "image/png"]:
         raise HTTPException(status_code=400, detail="Invalid file type. Please upload a JPEG or PNG image.")
 
-    # Load image and preprocess
-    image_data = await file.read()
-    image = Image.open(io.BytesIO(image_data))
-    processed_image = preprocess_image(image)
-
-    # Predict using the loaded model
-    prediction = model.predict(processed_image)
-    predicted_class = np.argmax(prediction, axis=1).item()
-
-    # Map prediction to disease label (adjust labels as per model output)
-    label_map = {0: "Healthy", 1: "Infected", 2: "Coccidiosis", 3: "Avian Influenza"}
-    disease = label_map.get(predicted_class, "Unknown")
-    confidence = prediction[0][predicted_class] * 100
-
-    # Get treatment recommendation and notifications
-    treatment_info = get_treatment_recommendation(disease)
-
-    result = {
-        "disease": disease,
+    try:
+        # Read and convert image to numpy array
+        image_data = await file.read()
+        image = Image.open(io.BytesIO(image_data)).convert("RGB")
+        image_np = np.array(image)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error loading image: {e}")
+
+    # Use bot to diagnose disease and generate a response
+    detailed_response, disease_name, status, recommendation, confidence = bot.diagnose_and_respond(image_np)
+
+    # Check if disease was detected successfully
+    if disease_name is None:
+        return JSONResponse(content={"error": "Unable to detect disease. Please try again."}, status_code=500)
+
+    # Return JSON response with prediction details
+    return JSONResponse(content={
+        "disease": disease_name,
+        "status": status,
         "confidence": f"{confidence:.2f}%",
-        "notification": treatment_info["notification"],
-        "suggestion": treatment_info["suggestion"]
-    }
-
-    return JSONResponse(content=result)
+        "recommendation": recommendation,
+        "details": detailed_response
+    })

routes/health_dashboard.py

@@ -0,0 +1,25 @@
+# routes/health_dashboard.py
+
+from fastapi import APIRouter, Request
+from fastapi.templating import Jinja2Templates
+from datetime import datetime
+
+from starlette.responses import HTMLResponse
+
+dashboard_router = APIRouter()
+templates = Jinja2Templates(directory="templates")
+
+# Simulate a database or storage for alerts
+ALERT_HISTORY = []
+
+@dashboard_router.get("/dashboard", response_class=HTMLResponse)
+async def health_dashboard(request: Request):
+    """Render the health dashboard with current and historical alerts."""
+    # For demonstration purposes, we're displaying recent alerts.
+    # In a real implementation, fetch this data from a database.
+    context = {
+        "request": request,
+        "alerts": ALERT_HISTORY,
+        "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    }
+    return templates.TemplateResponse("dashboard.html", context)

services/disease_detection_service.py

@@ -1,35 +1,135 @@
 # services/disease_detection_service.py
 
+import os
+import logging
 import tensorflow as tf
-from tensorflow.keras.models import load_model
+from keras.models import load_model
+import cv2
 import numpy as np
-from PIL import Image
-import os
+from huggingface_hub import login
+from pymongo import MongoClient
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Logging setup
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Load environment variables
+MONGO_URI = os.getenv("MONGO_URI")
+HF_TOKEN = os.getenv('HF_Token')
+db_client = MongoClient(MONGO_URI)
+db = db_client.poultry_farm  # MongoDB for record-keeping
+
+# Hugging Face login for Llama 3.2
+if HF_TOKEN:
+    login(token=HF_TOKEN, add_to_git_credential=True)
+else:
+    logger.warning("Hugging Face token not found in environment variables.")
 
-# Enable memory growth for GPUs
+# Mixed precision and GPU configuration
 gpus = tf.config.list_physical_devices('GPU')
 if gpus:
-    try:
-        for gpu in gpus:
-            tf.config.experimental.set_memory_growth(gpu, True)
-        print("Memory growth enabled for GPU.")
-    except RuntimeError as e:
-        print(f"Error setting memory growth: {e}")
-
-
-MODEL_PATH = "models/Final_Chicken_disease_model.h5"
-model = None
-
-def load_disease_model():
-    """Load the trained disease detection model with GPU support if available."""
-    global model
-    if model is None:
-        # Use GPU if available
-        if tf.config.list_physical_devices('GPU'):
-            print("GPU is available. Using GPU for model inference.")
-            with tf.device('/GPU:0'):
-                model = load_model(MODEL_PATH, custom_objects={'BatchNormalization': tf.keras.layers.BatchNormalization})
-        else:
-            print("GPU not available. Using CPU for model inference.")
-            model = load_model(MODEL_PATH, custom_objects={'BatchNormalization': tf.keras.layers.BatchNormalization})
-    return model
+    for gpu in gpus:
+        tf.config.experimental.set_memory_growth(gpu, True)
+    from tensorflow.keras import mixed_precision
+    policy = mixed_precision.Policy('mixed_float16')
+    mixed_precision.set_global_policy(policy)
+    logger.info("Using mixed precision with GPU.")
+else:
+    logger.info("Using CPU without mixed precision.")
+
+# Load the disease detection model
+try:
+    device_name = '/GPU:0' if gpus else '/CPU:0'
+    with tf.device(device_name):
+        disease_model = load_model('models/Final_Chicken_disease_model.h5', compile=True)
+    logger.info(f"Disease detection model loaded on {device_name}.")
+except Exception as e:
+    logger.error(f"Error loading disease model: {e}")
+
+# Llama 3.2 setup
+model_name = "meta-llama/Llama-3.2-1B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+llama_model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Set a padding token if it doesn’t already exist
+if tokenizer.pad_token is None:
+    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+    llama_model.resize_token_embeddings(len(tokenizer))
+
+# Disease mapping and treatment guidelines
+name_disease = {0: 'Coccidiosis', 1: 'Healthy', 2: 'New Castle Disease', 3: 'Salmonella'}
+status_map = {0: 'Critical', 1: 'No issue', 2: 'Critical', 3: 'Critical'}
+recommendations = {
+    0: 'Administer anti-coccidial medication, maintain hygiene, and ensure proper litter management.',
+    1: 'No treatment necessary; maintain regular monitoring and hygiene.',
+    2: 'Isolate affected birds and seek veterinary consultation for targeted treatment.',
+    3: 'Administer antibiotics as prescribed by a veterinarian and ensure biosecurity.'
+}
+
+class PoultryFarmBot:
+    def __init__(self, db):
+        self.db = db
+
+    def preprocess_image(self, image):
+        """Preprocess image for model input."""
+        try:
+            image = cv2.resize(image, (224, 224))
+            image = np.expand_dims(image, axis=0) / 255.0  # Normalize and add batch dimension
+            logger.info("Image preprocessed successfully.")
+            return image
+        except Exception as e:
+            logger.error(f"Error in image preprocessing: {e}")
+            return None
+
+    def predict_disease(self, image):
+        """Predict the disease from a preprocessed image and return detailed information."""
+        preprocessed_image = self.preprocess_image(image)
+        if preprocessed_image is None:
+            return "Image preprocessing failed.", None, None, None
+
+        try:
+            prediction = disease_model.predict(preprocessed_image)
+            predicted_class = np.argmax(prediction, axis=1).item()
+            disease_name = name_disease.get(predicted_class, "Unknown disease")
+            disease_status = status_map.get(predicted_class, "Unknown status")
+            recommendation = recommendations.get(predicted_class, "No recommendation available")
+            confidence = prediction[0][predicted_class] * 100
+            logger.info(f"Predicted: {disease_name} with {confidence:.2f}% confidence.")
+
+            # Generate detailed response using Llama 3.2
+            detailed_response = self.generate_detailed_response(disease_name, disease_status, recommendation)
+            return detailed_response, disease_name, disease_status, recommendation, confidence
+        except Exception as e:
+            logger.error(f"Prediction error: {e}")
+            return "Prediction failed.", None, None, None, None
+
+    def generate_detailed_response(self, disease_name, status, recommendation):
+        """Generate a detailed response using Llama 3.2 model for additional disease insights."""
+        prompt = (
+            f"The disease detected is {disease_name}, classified as {status}. "
+            f"Recommended action: {recommendation}. "
+            f"Here is more information about {disease_name}, including its causes, symptoms, and treatments "
+            "for effective management on a poultry farm."
+        )
+        response = self.llama_response(prompt)
+        return response.replace(prompt, "").strip()
+
+    def llama_response(self, prompt):
+        """Generate a response from the Llama 3.2 model based on a given prompt."""
+        try:
+            inputs = tokenizer(prompt, return_tensors="pt", max_length=150, truncation=True, padding=True)
+            outputs = llama_model.generate(inputs["input_ids"], max_length=150, do_sample=True, temperature=0.7)
+            return tokenizer.decode(outputs[0], skip_special_tokens=True)
+        except Exception as e:
+            logger.error(f"Llama model response error: {e}")
+            return "Error generating detailed response."
+
+    def diagnose_and_respond(self, image):
+        """Diagnose the disease and respond with detailed information and recommendations."""
+        if image is None or image.size == 0:
+            return "Please provide a valid poultry fecal image.", None, None, None, None
+        return self.predict_disease(image)
+
+# Initialize the bot instance
+bot = PoultryFarmBot(db)

services/health_monitoring_service.py

@@ -2,38 +2,36 @@
 
 from datetime import datetime, timedelta
 from typing import Dict, List
-
 from services.email_notification_service import send_email
 
 # Dictionary to map diseases to suggested treatments
 TREATMENT_GUIDELINES = {
     "Healthy": {
         "notification": "The flock is healthy.",
-        "suggestion": "Continue regular health checks and a balanced diet."
-    },
-    "Infected": {
-        "notification": "Disease detected in poultry.",
-        "suggestion": "Isolate affected birds and administer prescribed antibiotics. Consult a veterinarian for further assistance."
+        "suggestion": "No treatment necessary; maintain regular monitoring and hygiene."
     },
     "Coccidiosis": {
         "notification": "Coccidiosis symptoms detected.",
-        "suggestion": "Administer amprolium or sulfa drugs as recommended. Ensure clean water and reduce stress."
+        "suggestion": "Administer anti-coccidial medication, maintain hygiene, and ensure proper litter management."
     },
-    "Avian Influenza": {
-        "notification": "Avian Influenza suspected.",
-        "suggestion": "Report to the relevant authorities immediately. Quarantine the flock and halt bird movements."
+    "New Castle Disease": {
+        "notification": "New Castle Disease suspected.",
+        "suggestion": "Isolate affected birds and seek veterinary consultation for targeted treatment."
+    },
+    "Salmonella": {
+        "notification": "Salmonella infection detected.",
+        "suggestion": "Administer antibiotics as prescribed by a veterinarian and ensure biosecurity."
     }
 }
 
-# Notification rules for various conditions
+# Notification thresholds for health metrics
 HEALTH_THRESHOLDS = {
     "weight_loss_percentage": 5,  # Alert if weight loss > 5% in a week
-    "mortality_rate": 2,  # Alert if mortality rate > 2% in a week
+    "mortality_rate": 2,          # Alert if mortality rate > 2% in a week
     "reduced_feed_intake_percentage": 10  # Alert if feed intake drops > 10%
 }
 
-
-def get_health_alerts(health_metrics: dict) -> Dict[str, str]:
+def get_health_alerts(health_metrics: dict) -> List[str]:
     """
     Check health metrics and return alerts if thresholds are crossed.
 
@@ -41,7 +39,7 @@ def get_health_alerts(health_metrics: dict) -> Dict[str, str]:
         health_metrics (dict): Health metrics like weight loss, mortality rate, and feed intake.
 
     Returns:
-        dict: Notifications and suggestions if any health issues are detected.
+        list: Notifications and suggestions if any health issues are detected.
     """
     alerts = []
     if health_metrics.get("weight_loss_percentage", 0) > HEALTH_THRESHOLDS["weight_loss_percentage"]:
@@ -55,16 +53,13 @@ def get_health_alerts(health_metrics: dict) -> Dict[str, str]:
 
     return alerts
 
-
-def send_alerts(alerts: list, farmer_email: str):
+def send_alerts(alerts: List[str], farmer_email: str):
     """Send alert notifications to the farmer's email."""
     if alerts:
         message = "\n".join(alerts)
         subject = "Poultry Health Alert"
         send_email(farmer_email, subject, message)
 
-
-
 def get_treatment_recommendation(disease: str) -> Dict[str, str]:
     """Get notification and treatment suggestion based on disease detected."""
     return TREATMENT_GUIDELINES.get(disease, {
@@ -72,8 +67,7 @@ def get_treatment_recommendation(disease: str) -> Dict[str, str]:
         "suggestion": "Consult a veterinarian for diagnosis and treatment."
     })
 
-
-def evaluate_health_data(health_metrics: dict) -> dict[str, list[str]]:
+def evaluate_health_data(health_metrics: dict) -> Dict[str, List[str]]:
     """
     Evaluate health data and trigger alerts if thresholds are crossed.
 
@@ -81,7 +75,7 @@ def evaluate_health_data(health_metrics: dict) -> dict[str, list[str]]:
         health_metrics (dict): Health metrics like weight loss, mortality rate, and feed intake.
 
     Returns:
-        dict: Notifications and suggestions if any health issues are detected.
+        dict: Notifications if any health issues are detected.
     """
     notifications = []
 
@@ -89,8 +83,7 @@ def evaluate_health_data(health_metrics: dict) -> dict[str, list[str]]:
         notifications.append("Significant weight loss detected. Monitor feed quality and check for underlying issues.")
 
     if health_metrics.get("mortality_rate", 0) > HEALTH_THRESHOLDS["mortality_rate"]:
-        notifications.append(
-            "Increased mortality rate. Review flock conditions and investigate potential health issues.")
+        notifications.append("Increased mortality rate. Review flock conditions and investigate potential health issues.")
 
     if health_metrics.get("reduced_feed_intake_percentage", 0) > HEALTH_THRESHOLDS["reduced_feed_intake_percentage"]:
         notifications.append("Feed intake has dropped. Check for signs of illness or environmental stress.")

services/image_preprocessing.py

@@ -2,6 +2,11 @@
 
 from PIL import Image
 import numpy as np
+import logging
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 
 def preprocess_image(image: Image.Image, target_size=(224, 224)):
@@ -15,8 +20,24 @@ def preprocess_image(image: Image.Image, target_size=(224, 224)):
     Returns:
         np.ndarray: Preprocessed image ready for model input.
     """
-    image = image.convert("RGB")  # Ensure 3 color channels
-    image = image.resize(target_size)  # Resize image
-    image_array = np.array(image) / 255.0  # Normalize to [0, 1]
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
-    return image_array
+    try:
+        # Ensure the image has 3 color channels (RGB)
+        image = image.convert("RGB")
+        logger.info("Image converted to RGB.")
+
+        # Resize image to the target size
+        image = image.resize(target_size)
+        logger.info(f"Image resized to {target_size}.")
+
+        # Normalize the image pixels to [0, 1] range
+        image_array = np.array(image) / 255.0
+        logger.info("Image normalized to [0, 1] range.")
+
+        # Add a batch dimension to the image array
+        image_array = np.expand_dims(image_array, axis=0)
+        logger.info("Batch dimension added to image array.")
+
+        return image_array
+    except Exception as e:
+        logger.error(f"Error preprocessing image: {e}")
+        return None

templates/dashboard.html

@@ -0,0 +1,21 @@
+<!-- templates/dashboard.html -->
+
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Poultry Health Dashboard</title>
+</head>
+<body>
+    <h1>Poultry Health Dashboard</h1>
+    <p>Last Updated: {{ last_updated }}</p>
+
+    <h2>Recent Alerts</h2>
+    <ul>
+        {% for alert in alerts %}
+            <li>{{ alert }}</li>
+        {% endfor %}
+    </ul>
+</body>
+</html>