Create app.py

app.py

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+import gradio as gr
+import cv2
+import torch
+import numpy as np
+from transformers import CLIPProcessor, CLIPVisionModel
+from PIL import Image
+from torch import nn
+import requests
+import matplotlib.pyplot as plt
+from huggingface_hub import hf_hub_download
+
+# ... (rest of your code remains the same)
+
+def process_image_classification(image):
+    model, processor, reverse_mapping, device = load_model()
+    
+    # Convert image to PIL Image
+    image = Image.fromarray(image)
+    
+    inputs = processor(images=image, return_tensors="pt")
+    pixel_values = inputs.pixel_values.to(device)
+    
+    with torch.no_grad():
+        logits, attentions = model(pixel_values, output_attentions=True)
+        probs = torch.nn.functional.softmax(logits, dim=-1)
+        prediction = torch.argmax(probs).item()
+    
+    # Generate attention map
+    attention_map = get_attention_map(attentions)
+    
+    visualization = apply_heatmap(image, attention_map)
+    
+    card_name = reverse_mapping[prediction]
+    confidence = probs[0][prediction].item()
+    
+    # Convert back to RGB for matplotlib display
+    visualization_rgb = cv2.cvtColor(visualization, cv2.COLOR_BGR2RGB)
+    
+    return visualization_rgb, card_name, confidence
+
+def gradio_interface():
+    gr_interface = gr.Interface(
+        fn=process_image_classification,
+        inputs=gr.inputs.Image(type="numpy"),
+        outputs=[
+            gr.outputs.Image(label="Heatmap Plot"),
+            gr.outputs.Textbox(label="Predicted Card"),
+            gr.outputs.Textbox(label="Confidence")
+        ],
+        title="Uno Card Recognizer",
+        description="Upload an image or use your webcam to recognize an Uno card."
+    )
+    gr_interface.launch()
+
+if __name__ == "__main__":
+    gradio_interface()