app.py

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")

# 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)
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()