Update app.py

app.py

@@ -1,3 +1,103 @@
 import gradio as gr
+import numpy as np
+import tensorflow as tf
+from PIL import Image, ImageDraw
+import io
 
-gr.load("models/Mohaddz/Detr-spareparts").launch()
+# Load your models here
+ part_seg_model = tf.keras.models.load_model('path_to_part_seg_model')
+ damage_seg_model = tf.keras.models.load_model('path_to_damage_seg_model')
+
+# Define your labels
+part_labels = ["front-bumper", "fender", "hood", "door", "trunk"]  # Add all your part labels
+damage_labels = ["dent", "scratch", "misalignment", "crack"]  # Add all your damage labels
+
+def inference_part_seg(image):
+    # Implement your part segmentation inference here
+    # For now, we'll return a random mask
+    return np.random.randint(0, len(part_labels), size=image.shape[:2])
+
+def inference_damage_seg(image):
+    # Implement your damage segmentation inference here
+    # For now, we'll return a random mask
+    return np.random.randint(0, len(damage_labels), size=image.shape[:2])
+
+def combine_masks(part_mask, damage_mask):
+    part_damage_pairs = []
+    for part_id, part_name in enumerate(part_labels):
+        if part_name == "cars-8I1q":
+            continue
+        for damage_id, damage_name in enumerate(damage_labels):
+            if damage_name == "etc":
+                continue
+            part_binary = (part_mask == part_id)
+            damage_binary = (damage_mask == damage_id)
+            intersection = np.logical_and(part_binary, damage_binary)
+            if np.any(intersection):
+                part_damage_pairs.append((part_name, damage_name))
+    return part_damage_pairs
+
+def create_one_hot_vector(part_damage_pairs):
+    vector = np.zeros(len(part_labels) * len(damage_labels))
+    for part, damage in part_damage_pairs:
+        if part in part_labels and damage in damage_labels:
+            part_index = part_labels.index(part)
+            damage_index = damage_labels.index(damage)
+            vector_index = part_index * len(damage_labels) + damage_index
+            vector[vector_index] = 1
+    return vector
+
+def visualize_results(image, part_mask, damage_mask):
+    img = Image.fromarray(image)
+    draw = ImageDraw.Draw(img)
+    
+    for i in range(img.width):
+        for j in range(img.height):
+            part = part_labels[part_mask[j, i]]
+            damage = damage_labels[damage_mask[j, i]]
+            if part != "cars-8I1q" and damage != "etc":
+                draw.point((i, j), fill="red")
+    
+    return img
+
+def process_image(image):
+    # Convert to numpy array if it's not already
+    if isinstance(image, Image.Image):
+        image = np.array(image)
+    
+    # Perform inference
+    part_mask = inference_part_seg(image)
+    damage_mask = inference_damage_seg(image)
+    
+    # Combine masks
+    part_damage_pairs = combine_masks(part_mask, damage_mask)
+    
+    # Create one-hot encoded vector
+    one_hot_vector = create_one_hot_vector(part_damage_pairs)
+    
+    # Visualize results
+    result_image = visualize_results(image, part_mask, damage_mask)
+    
+    return result_image, part_damage_pairs, one_hot_vector.tolist()
+
+def gradio_interface(input_image):
+    result_image, part_damage_pairs, one_hot_vector = process_image(input_image)
+    
+    # Convert part_damage_pairs to a string for display
+    damage_description = "\n".join([f"{part} : {damage}" for part, damage in part_damage_pairs])
+    
+    return result_image, damage_description, str(one_hot_vector)
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Image(type="pil", label="Detected Damage"),
+        gr.Textbox(label="Damage Description"),
+        gr.Textbox(label="One-hot Encoded Vector")
+    ],
+    title="Car Damage Assessment",
+    description="Upload an image of a damaged car to get an assessment of the damage."
+)
+
+iface.launch()