Update app.py

app.py

@@ -1,3 +1,153 @@
+import gradio as gr
+import torch
+from transformers import SwinForImageClassification, AutoFeatureExtractor
+import mediapipe as mp
+import cv2
+from PIL import Image
+import numpy as np
+import os
+
+# Face shape descriptions
+face_shape_descriptions = {
+    "Heart": "dengan dahi lebar dan dagu yang runcing.",
+    "Oblong": "yang lebih panjang dari lebar dengan garis pipi lurus.",
+    "Oval": "dengan proporsi seimbang dan dagu sedikit melengkung.",
+    "Round": "dengan garis rahang melengkung dan pipi penuh.",
+    "Square": "dengan rahang tegas dan dahi lebar."
+}
+
+# Frame images path
+glasses_images = {
+    "Oval": "glasses/oval.jpg",
+    "Round": "glasses/round.jpg",
+    "Square": "glasses/square.jpg",
+    "Octagon": "glasses/octagon.jpg",
+    "Cat Eye": "glasses/cat eye.jpg",
+    "Pilot (Aviator)": "glasses/aviator.jpg"
+}
+
+# Ensure the 'glasses' directory exists and contains the images
+if not os.path.exists("glasses"):
+    os.makedirs("glasses")
+    # Create dummy image files if they don't exist
+    for _, path in glasses_images.items():
+        if not os.path.exists(path):
+            dummy_image = Image.new('RGB', (200, 100), color='gray')
+            dummy_image.save(path)
+
+id2label = {0: 'Heart', 1: 'Oblong', 2: 'Oval', 3: 'Round', 4: 'Square'}
+label2id = {v: k for k, v in id2label.items()}
+
+# Load model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model_checkpoint = "microsoft/swin-tiny-patch4-window7-224"
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)
+
+model = SwinForImageClassification.from_pretrained(
+    model_checkpoint,
+    label2id=label2id,
+    id2label=id2label,
+    ignore_mismatched_sizes=True
+)
+
+# Load your trained weights
+# Ensure 'LR-0001-adamW-32-64swin.pth' is in the same directory or provide the correct path
+if os.path.exists('LR-0001-adamW-32-64swin.pth'):
+    state_dict = torch.load('LR-0001-adamW-32-64swin.pth', map_location=device)
+    model.load_state_dict(state_dict, strict=False)
+    model.to(device)
+    model.eval()
+else:
+    print("Warning: Trained weights file 'LR-0001-adamW-32-64swin.pth' not found. Using pre-trained weights only.")
+
+# Initialize Mediapipe
+mp_face_detection = mp.solutions.face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5)
+
+# --- New: Decision tree function
+def recommend_glasses_tree(face_shape):
+    face_shape = face_shape.lower()
+    if face_shape == "square":
+        return ["Oval", "Round"]
+    elif face_shape == "round":
+        return ["Square", "Octagon", "Cat Eye"]
+    elif face_shape == "oval":
+        return ["Oval", "Pilot (Aviator)", "Cat Eye", "Round"]
+    elif face_shape == "heart":
+        return ["Pilot (Aviator)", "Cat Eye", "Round"]
+    elif face_shape == "oblong":
+        return ["Square", "Oval", "Pilot (Aviator)", "Cat Eye"]
+    else:
+        return []
+
+# Preprocess function
+def preprocess_image(image):
+    img = np.array(image, dtype=np.uint8)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+    results = mp_face_detection.process(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+
+    if results.detections:
+        detection = results.detections[0]
+        bbox = detection.location_data.relative_bounding_box
+        h, w, _ = img.shape
+        x1 = int(bbox.xmin * w)
+        y1 = int(bbox.ymin * h)
+        x2 = int((bbox.xmin + bbox.width) * w)
+        y2 = int((bbox.ymin + bbox.height) * h)
+
+        img = img[y1:y2, x1:x2]
+    else:
+        raise ValueError("Wajah tidak terdeteksi.")
+
+    img = cv2.resize(img, (224, 224))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    inputs = feature_extractor(images=img, return_tensors="pt")
+    return inputs['pixel_values'].squeeze(0)
+
+# Prediction function
+def predict(image):
+    try:
+        inputs = preprocess_image(image).unsqueeze(0).to(device)
+        with torch.no_grad():
+            outputs = model(inputs)
+            probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+            pred_idx = torch.argmax(probs, dim=1).item()
+            pred_label = id2label[pred_idx]
+            pred_prob = probs[0][pred_idx].item() * 100
+
+        # --- Use decision tree for recommendations
+        frame_recommendations = recommend_glasses_tree(pred_label)
+
+        description = face_shape_descriptions.get(pred_label, "tidak dikenali")
+        gallery_items = []
+
+        # Load images for all recommended frames
+        for frame in frame_recommendations:
+            frame_image_path = glasses_images.get(frame)
+            if frame_image_path and os.path.exists(frame_image_path):
+                try:
+                    frame_image = Image.open(frame_image_path)
+                    gallery_items.append((frame_image, frame))  # Tambahkan nama frame
+                except Exception as e:
+                    print(f"Error loading image for {frame}: {e}")
+
+        # Build explanation text
+        if frame_recommendations:
+            recommended_frames_text = ', '.join(frame_recommendations)
+            explanation = (f"Bentuk wajah kamu adalah {pred_label} ({pred_prob:.2f}%). "
+                           f"Kamu memiliki bentuk wajah {description} "
+                           f"Rekomendasi bentuk kacamata yang sesuai dengan wajah kamu adalah: {recommended_frames_text}.")
+        else:
+            explanation = (f"Bentuk wajah kamu adalah {pred_label} ({pred_prob:.2f}%). "
+                           f"Tidak ada rekomendasi frame untuk bentuk wajah ini.")
+
+        return pred_label, explanation, gallery_items
+
+    except ValueError as ve:
+        return "Error", str(ve), []
+    except Exception as e:
+        return "Error", f"Terjadi kesalahan: {str(e)}", []
+
 # Gradio Interface
 with gr.Blocks(theme=gr.themes.Soft()) as iface:
     gr.Markdown("# Program Rekomendasi Kacamata Berdasarkan Bentuk Wajah")
@@ -5,8 +155,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as iface:
 
     with gr.Row():
         with gr.Column():
-            image_input = gr.Image(type="pil", image_mode="single", interactive=False, label="Unggah Gambar")  # Changed to "single" and added label
-            upload_button = gr.Button("Unggah Gambar")  # Add a button to upload the image
+            image_input = gr.Image(type="pil")
             confirm_button = gr.Button("Konfirmasi")
             restart_button = gr.Button("Restart")
         with gr.Column():
@@ -14,8 +163,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as iface:
             explanation_output = gr.Textbox(label="Penjelasan")
             recommendation_gallery = gr.Gallery(label="Rekomendasi Kacamata", columns=3, show_label=False)
 
-    # Adjust the actions
-    upload_button.click(lambda: None, inputs=None, outputs=[image_input])  # Handle image upload
     confirm_button.click(predict, inputs=image_input, outputs=[detected_shape, explanation_output, recommendation_gallery])
     restart_button.click(lambda: (None, "", [], []), inputs=None, outputs=[image_input, detected_shape, explanation_output, recommendation_gallery])