Update app.py

app.py

@@ -1,46 +1,23 @@
 import gradio as gr
 import numpy as np
 from PIL import Image, ImageDraw
-from transformers import TFSegformerForSemanticSegmentation
-import tensorflow as tf
-
-# Load models from Hugging Face
-part_seg_model = TFSegformerForSemanticSegmentation.from_pretrained("Mohaddz/huggingCars")
-damage_seg_model = TFSegformerForSemanticSegmentation.from_pretrained("Mohaddz/DamageSegMohaddz/DamageSeg")
 
 # Define your labels
-part_labels = ["front-bumper", "fender", "hood", "door", "trunk", "cars-8I1q"]  # Add all your part labels
-damage_labels = ["dent", "scratch", "misalignment", "crack", "etc"]  # Add all your damage labels
-
-def preprocess_image(image):
-    # Resize the image
-    image = tf.image.resize(image, (512, 512))
-    # Normalize the image
-    image = tf.keras.applications.imagenet_utils.preprocess_input(image)
-    return tf.expand_dims(image, 0)  # Add batch dimension
-
-def inference_seg(model, image):
-    outputs = model(image, training=False)
-    logits = outputs.logits
-    mask = tf.argmax(logits, axis=-1)
-    return mask.numpy().squeeze()
-
-def inference_part_seg(image):
-    preprocessed_image = preprocess_image(image)
-    return inference_seg(part_seg_model, preprocessed_image)
+part_labels = ["front-bumper", "fender", "hood", "door", "trunk"]
+damage_labels = ["dent", "scratch", "misalignment", "crack"]
 
-def inference_damage_seg(image):
-    preprocessed_image = preprocess_image(image)
-    return inference_seg(damage_seg_model, preprocessed_image)
+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):
-        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)
@@ -59,22 +36,20 @@ def create_one_hot_vector(part_damage_pairs):
     return vector
 
 def visualize_results(image, part_mask, damage_mask):
-    img = Image.fromarray((image * 255).astype('uint8'))
+    img = Image.fromarray(image)
     draw = ImageDraw.Draw(img)
     
-    for i in range(img.width):
-        for j in range(img.height):
+    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]]
-            if part != "cars-8I1q" and damage != "etc":
-                draw.point((i, j), fill="red")
+            draw.point((i, j), fill="red")
     
     return img
 
 def process_image(image):
-    # Perform inference
-    part_mask = inference_part_seg(image)
-    damage_mask = inference_damage_seg(image)
+    # Mock inference
+    part_mask, damage_mask = mock_inference(image)
     
     # Combine masks
     part_damage_pairs = combine_masks(part_mask, damage_mask)
@@ -99,12 +74,12 @@ iface = gr.Interface(
     fn=gradio_interface,
     inputs=gr.Image(type="numpy"),
     outputs=[
-        gr.Image(type="pil", label="Detected Damage"),
+        gr.Image(type="pil", label="Detected Damage (Mocked)"),
         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."
+    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()