Update app/gradio_app.py

app/gradio_app.py

@@ -1,87 +1,100 @@
-import gradio as gr
-from PIL import Image
-from .model import predict
-import os
-
-model_path = os.path.join(
-    os.path.dirname(os.path.dirname(__file__)), 
-    "saved_model", 
-    "InceptionV3_Dogs_and_Cats_Classification.h5"
-)
-
-def classify_image(image):
-    if image is None:
-        return None, {"error": "Please upload an image"}
-    
-    try:
-        label, confidence, probs = predict(image)
-        results = {
-            "Predicted Class": label,
-            "Confidence": f"{confidence * 100:.2f}%",
-            "Cat Probability": f"{probs['Cat'] * 100:.2f}%",
-            "Dog Probability": f"{probs['Dog'] * 100:.2f}%"
-        }
-        return image, results
-    
-    except Exception as e:
-        return image, {"error": f"Classification failed: {str(e)}"}
-
-with gr.Blocks(title="Cats vs Dogs Classifier", theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # Cats vs Dogs Classifier
-        
-        Upload an image of a cat or dog, and the InceptionV3 model will classify it!
-        
-        **Model:** InceptionV3 (Transfer Learning)  
-        **Classes:** Cat | Dog  
-        **Image Size:** 256x256 pixels
-
-        **NOTE:**
-            - You can upload an image from your device, just press "X" icon and start uploading.
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("### Upload Image")
-            image_input = gr.Image(
-                type="pil",
-                label="Upload Image",
-                sources=["upload", "webcam"],
-                interactive=True
-            )
-        with gr.Column():
-            gr.Markdown("### Prediction Results")
-            output = gr.JSON(label="Classification Results")
-    
-    submit_btn = gr.Button("Classify Image", variant="primary", scale=1)
-    submit_btn.click(
-        fn=classify_image,
-        inputs=image_input,
-        outputs=[image_input, output]
-    )
-    
-    gr.Markdown("### Examples")
-    gr.Examples(
-        examples=[
-            ["examples/cat1.jpg"],
-            ["examples/cat2.jpg"],
-            ["examples/cat3.jpg"],
-            ["examples/dog1.jpg"],
-            ["examples/dog2.jpg"]
-        ],
-        inputs=image_input,
-        outputs=[image_input, output],
-        fn=classify_image,
-        run_on_click=True,
-        label="Example Images (Click to run)"
-    )
-
-if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        show_error=True
+import gradio as gr
+from PIL import Image
+from .model import predict
+import os
+import random
+from datasets import load_dataset
+
+# Load Hugging Face dataset locally
+ds = load_dataset("AIOmarRehan/Cats_and_Dogs", split="train")  # adjust split if needed
+
+model_path = os.path.join(
+    os.path.dirname(os.path.dirname(__file__)), 
+    "saved_model", 
+    "InceptionV3_Dogs_and_Cats_Classification.h5"
+)
+
+def classify_image(image):
+    if image is None:
+        return None, {"error": "Please upload an image"}
+    try:
+        label, confidence, probs = predict(image)
+        results = {
+            "Predicted Class": label,
+            "Confidence": f"{confidence * 100:.2f}%",
+            "Cat Probability": f"{probs['Cat'] * 100:.2f}%",
+            "Dog Probability": f"{probs['Dog'] * 100:.2f}%"
+        }
+        return image, results
+    except Exception as e:
+        return image, {"error": f"Classification failed: {str(e)}"}
+
+def get_random_image():
+    # Pick a random sample from the dataset
+    sample = ds.shuffle(seed=random.randint(0, 9999))[0]
+    img = Image.open(sample["image"]).convert("RGB")
+    return img
+
+with gr.Blocks(title="Cats vs Dogs Classifier", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # Cats vs Dogs Classifier
+        
+        Upload an image of a cat or dog, and the InceptionV3 model will classify it!
+        
+        **Model:** InceptionV3 (Transfer Learning)  
+        **Classes:** Cat | Dog  
+        **Image Size:** 256x256 pixels
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Upload Image")
+            image_input = gr.Image(
+                type="pil",
+                label="Upload Image",
+                sources=["upload", "webcam"],
+                interactive=True
+            )
+            random_btn = gr.Button("Random Image from Dataset")
+        with gr.Column():
+            gr.Markdown("### Prediction Results")
+            output = gr.JSON(label="Classification Results")
+    
+    submit_btn = gr.Button("Classify Image", variant="primary", scale=1)
+    submit_btn.click(
+        fn=classify_image,
+        inputs=image_input,
+        outputs=[image_input, output]
+    )
+    
+    random_btn.click(
+        fn=get_random_image,
+        inputs=[],
+        outputs=image_input
+    )
+    
+    gr.Markdown("### Examples")
+    gr.Examples(
+        examples=[
+            ["examples/cat1.jpg"],
+            ["examples/cat2.jpg"],
+            ["examples/cat3.jpg"],
+            ["examples/dog1.jpg"],
+            ["examples/dog2.jpg"]
+        ],
+        inputs=image_input,
+        outputs=[image_input, output],
+        fn=classify_image,
+        run_on_click=True,
+        label="Example Images (Click to run)"
+    )
+
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
     )