Update app.py

app.py

@@ -2,30 +2,16 @@ 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
+import random
 
 # 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")
 
-# Recreate the model architecture
-def create_model(input_shape, num_classes):
-    inputs = tf.keras.Input(shape=input_shape)
-    x = tf.keras.layers.Dense(64, activation='relu')(inputs)
-    x = tf.keras.layers.Dense(32, activation='relu')(x)
-    outputs = tf.keras.layers.Dense(num_classes, activation='sigmoid')(x)
-    return tf.keras.Model(inputs=inputs, outputs=outputs)
-
-# Load model weights
-input_shape = 33  # Adjust this based on your actual input shape
-num_classes = 29  # Adjust this based on your actual number of classes
-dl_model = create_model(input_shape, num_classes)
-dl_model.load_weights('improved_car_damage_prediction_model_weights.h5')
-
 # Load parts list
 with open('cars117.json', 'r', encoding='utf-8') as f:
     data = json.load(f)
@@ -63,16 +49,15 @@ def process_image(image):
     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]
+    # Simulate part prediction (for demonstration purposes)
+    num_predictions = random.randint(3, 5)
+    predicted_parts = [(part, random.random()) for part in random.sample(all_parts, num_predictions)]
     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]]))
+            "\n".join([f"{part}: {prob:.2f}" for part, prob in predicted_parts]))
 
 def create_heatmap(features):
     heatmap = np.sum(features, axis=0)
@@ -87,10 +72,10 @@ iface = gr.Interface(
         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")
+        gr.Textbox(label="Predicted Parts to Replace (Simulated)")
     ],
-    title="Car Damage Assessment",
-    description="Upload an image of a damaged car to get an assessment."
+    title="Car Damage Assessment (Demo Version)",
+    description="Upload an image of a damaged car to get a simulated assessment. Note: Part predictions are randomly generated for demonstration purposes."
 )
 
 iface.launch()