app and requirements

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from transformers import SegformerForSemanticSegmentation, AutoFeatureExtractor
+import cv2
+import json
+
+# Load models
+part_seg_model = SegformerForSemanticSegmentation.from_pretrained("Mohaddz/huggingCars")
+damage_seg_model = SegformerForSemanticSegmentation.from_pretrained("Mohaddz/DamageSeg")
+feature_extractor = AutoFeatureExtractor.from_pretrained("Mohaddz/huggingCars")
+dl_model = tf.keras.models.load_model('improved_car_damage_prediction_model.h5')
+
+# Load parts list
+with open('cars117.json', 'r', encoding='utf-8') as f:
+    data = json.load(f)
+all_parts = sorted(list(set(part for entry in data.values() for part in entry.get('replaced_parts', []))))
+
+def process_image(image):
+    # Convert to RGB if it's not
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    
+    # Prepare input for the model
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    
+    # Get damage segmentation
+    with torch.no_grad():
+        damage_output = damage_seg_model(**inputs).logits
+    damage_features = damage_output.squeeze().detach().numpy()
+    
+    # Create damage segmentation heatmap
+    damage_heatmap = create_heatmap(damage_features)
+    damage_heatmap_resized = cv2.resize(damage_heatmap, (image.size[0], image.size[1]))
+    
+    # Create annotated damage image
+    image_array = np.array(image)
+    damage_mask = np.argmax(damage_features, axis=0)
+    damage_mask_resized = cv2.resize(damage_mask, (image.size[0], image.size[1]), interpolation=cv2.INTER_NEAREST)
+    overlay = np.zeros_like(image_array)
+    overlay[damage_mask_resized > 0] = [255, 0, 0]  # Red color for damage
+    annotated_image = cv2.addWeighted(image_array, 1, overlay, 0.5, 0)
+    
+    # Process for part prediction and heatmap
+    with torch.no_grad():
+        part_output = part_seg_model(**inputs).logits
+    part_features = part_output.squeeze().detach().numpy()
+    part_heatmap = create_heatmap(part_features)
+    part_heatmap_resized = cv2.resize(part_heatmap, (image.size[0], image.size[1]))
+    
+    # Predict parts to replace
+    input_vector = np.concatenate([part_features.mean(axis=(1, 2)), damage_features.mean(axis=(1, 2))])
+    prediction = dl_model.predict(np.array([input_vector]))
+    predicted_parts = [(all_parts[i], float(prob)) for i, prob in enumerate(prediction[0]) if prob > 0.1]
+    predicted_parts.sort(key=lambda x: x[1], reverse=True)
+    
+    return (Image.fromarray(annotated_image), 
+            Image.fromarray(damage_heatmap_resized), 
+            Image.fromarray(part_heatmap_resized), 
+            "\n".join([f"{part}: {prob:.2f}" for part, prob in predicted_parts[:5]]))
+
+def create_heatmap(features):
+    heatmap = np.sum(features, axis=0)
+    heatmap = (heatmap - heatmap.min()) / (heatmap.max() - heatmap.min())
+    heatmap = np.uint8(255 * heatmap)
+    return cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
+
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Image(type="pil", label="Annotated Damage"),
+        gr.Image(type="pil", label="Damage Heatmap"),
+        gr.Image(type="pil", label="Part Segmentation Heatmap"),
+        gr.Textbox(label="Predicted Parts to Replace")
+    ],
+    title="Car Damage Assessment",
+    description="Upload an image of a damaged car to get an assessment."
+)
+
+iface.launch(share=True)