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app.py

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+import gradio as gr
+from image_classifier import ImageClassifier
+import numpy as np
+
+classifier = ImageClassifier()
+
+def classify_image(image):
+    # Convert Gradio image to the format expected by our classifier
+    # Our classifier expects a file path or URL, so we'll save the image temporarily
+    import tempfile
+    import os
+    from PIL import Image
+    
+    # Save the image temporarily
+    with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmp:
+        if isinstance(image, np.ndarray):
+            # Convert numpy array to PIL Image
+            pil_img = Image.fromarray(image.astype('uint8'), 'RGB')
+        else:
+            pil_img = image
+        pil_img.save(tmp.name)
+        tmp_path = tmp.name
+    
+    # Classify the image
+    try:
+        results = classifier.classify_image(tmp_path)
+        # Format results for display
+        labels = [res['label'] for res in results]
+        confidences = [res['probability'] for res in results]
+        
+        # Clean up temporary file
+        os.remove(tmp_path)
+        
+        return labels, confidences
+    except Exception as e:
+        # Clean up temporary file even if there's an error
+        os.remove(tmp_path)
+        raise e
+
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil", label="Upload an image for classification"),
+    outputs=[
+        gr.Label(num_top_classes=5, label="Top Predictions"),
+        gr.BarPlot(x="Label", y="Confidence", title="Confidence Scores", width=500, height=300)
+    ],
+    title="🖼️ Computer Vision Model",
+    description="This model performs image classification using a pre-trained ResNet model.",
+    examples=[]
+)
+
+if __name__ == "__main__":
+    demo.launch()