Delete app.py

app.py

@@ -1,85 +0,0 @@
-import gradio as gr
-import numpy as np
-from PIL import Image, ImageDraw
-
-# Define your labels
-part_labels = ["front-bumper", "fender", "hood", "door", "trunk"]
-damage_labels = ["dent", "scratch", "misalignment", "crack"]
-
-def mock_inference(image):
-    # This function mocks the segmentation model output
-    # It randomly assigns labels to different parts of the image
-    height, width = image.shape[:2]
-    part_mask = np.random.randint(0, len(part_labels), (height, width))
-    damage_mask = np.random.randint(0, len(damage_labels), (height, width))
-    return part_mask, damage_mask
-
-def combine_masks(part_mask, damage_mask):
-    part_damage_pairs = []
-    for part_id, part_name in enumerate(part_labels):
-        for damage_id, damage_name in enumerate(damage_labels):
-            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(0, img.width, 10):  # Sample every 10th pixel for efficiency
-        for j in range(0, img.height, 10):
-            part = part_labels[part_mask[j, i]]
-            damage = damage_labels[damage_mask[j, i]]
-            draw.point((i, j), fill="red")
-    
-    return img
-
-def process_image(image):
-    # Mock inference
-    part_mask, damage_mask = mock_inference(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="numpy"),
-    outputs=[
-        gr.Image(type="pil", label="Detected Damage (Mocked)"),
-        gr.Textbox(label="Damage Description"),
-        gr.Textbox(label="One-hot Encoded Vector")
-    ],
-    title="Car Damage Assessment (Demo)",
-    description="Upload an image of a damaged car to get a mocked assessment of the damage. Note: This is a demo using random predictions, not actual model inference."
-)
-
-iface.launch()