Upload app.py

app.py

@@ -1,96 +1,118 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-from huggingface_hub import hf_hub_download
-import os
-
-# Configuration
-MODEL_REPO = "Ahmedhassan54/Image-Classification"  # Replace with your HF username and repo
-MODEL_FILE = "best_model.h5"
-
-# Download model from Hugging Face Hub
-def load_model_from_hf():
-    try:
-        if not os.path.exists(MODEL_FILE):
-            print("Downloading model from Hugging Face Hub...")
-            model_path = hf_hub_download(
-                repo_id=MODEL_REPO,
-                filename=MODEL_FILE,
-                cache_dir="."
-            )
-            os.system(f"cp {model_path} {MODEL_FILE}")
-        
-        return tf.keras.models.load_model(MODEL_FILE)
-    except Exception as e:
-        raise gr.Error(f"Model loading failed: {str(e)}")
-
-model = load_model_from_hf()
-
-def classify_image(image):
-    try:
-        image = Image.fromarray(image) if isinstance(image, np.ndarray) else image
-        image = image.resize((150, 150))
-        image_array = np.array(image) / 255.0
-        image_array = np.expand_dims(image_array, axis=0)
-        
-        prediction = model.predict(image_array)
-        confidence = float(prediction[0][0])
-        
-        return {
-            "Dog": confidence,
-            "Cat": 1 - confidence
-        }
-    except Exception as e:
-        raise gr.Error(f"Classification error: {str(e)}")
-
-# Custom CSS for better UI
-css = """
-.gradio-container {
-    background: linear-gradient(to right, #f5f7fa, #c3cfe2);
-}
-footer {
-    visibility: hidden
-}
-"""
-
-# Build the interface
-with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🐾 Cat vs Dog Classifier 🦮")
-    gr.Markdown("Upload an image to classify whether it's a cat or dog")
-    
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(label="Upload Image", type="pil")
-            submit_btn = gr.Button("Classify", variant="primary")
-        
-        with gr.Column():
-            label_output = gr.Label(label="Predictions", num_top_classes=2)
-            confidence_bar = gr.BarPlot(
-                x=["Cat", "Dog"],
-                y=[0.5, 0.5],
-                y_lim=[0,1],
-                title="Confidence Scores",
-                width=400,
-                height=300
-            )
-    
-    # Example images
-    gr.Examples(
-        examples=[
-            ["https://upload.wikimedia.org/wikipedia/commons/1/15/Cat_August_2010-4.jpg"],
-            ["https://upload.wikimedia.org/wikipedia/commons/d/d9/Collage_of_Nine_Dogs.jpg"]
-        ],
-        inputs=image_input
-    )
-    
-    # Button action
-    submit_btn.click(
-        fn=classify_image,
-        inputs=image_input,
-        outputs=[label_output, confidence_bar],
-        api_name="classify"
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import os
+import pandas as pd
+
+# Configuration
+MODEL_REPO = "Ahmedhassan54/Image-Classification"  # Changed to your actual repo
+MODEL_FILE = "best_model.h5"
+
+# Download model from Hugging Face Hub
+def load_model_from_hf():
+    try:
+        if not os.path.exists(MODEL_FILE):
+            print("Downloading model from Hugging Face Hub...")
+            model_path = hf_hub_download(
+                repo_id=MODEL_REPO,
+                filename=MODEL_FILE,
+                cache_dir="."
+            )
+            os.system(f"cp {model_path} {MODEL_FILE}")
+        
+        return tf.keras.models.load_model(MODEL_FILE)
+    except Exception as e:
+        raise gr.Error(f"Model loading failed: {str(e)}")
+
+model = load_model_from_hf()
+
+def classify_image(image):
+    try:
+        # Convert image if needed
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+        
+        # Preprocess image
+        image = image.resize((150, 150))
+        image_array = np.array(image) / 255.0
+        image_array = np.expand_dims(image_array, axis=0)
+        
+        # Make prediction
+        prediction = model.predict(image_array, verbose=0)
+        confidence = float(prediction[0][0])
+        
+        # Format outputs
+        label_output = {
+            "Cat": 1 - confidence,
+            "Dog": confidence
+        }
+        
+        # Create dataframe for bar plot
+        plot_data = pd.DataFrame({
+            'Class': ['Cat', 'Dog'],
+            'Confidence': [1 - confidence, confidence]
+        })
+        
+        return label_output, plot_data
+        
+    except Exception as e:
+        print(f"Error: {str(e)}")  # Debug print
+        raise gr.Error(f"Classification error: {str(e)}")
+
+# Custom CSS
+css = """
+.gradio-container {
+    background: linear-gradient(to right, #f5f7fa, #c3cfe2);
+}
+footer {
+    visibility: hidden
+}
+"""
+
+# Build the interface
+with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🐾 Cat vs Dog Classifier 🦮")
+    gr.Markdown("Upload an image to classify whether it's a cat or dog")
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(label="Upload Image", type="pil")
+            submit_btn = gr.Button("Classify", variant="primary")
+        
+        with gr.Column():
+            label_output = gr.Label(label="Predictions", num_top_classes=2)
+            confidence_bar = gr.BarPlot(
+                pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]}),
+                x="Class",
+                y="Confidence",
+                y_lim=[0,1],
+                title="Confidence Scores",
+                width=400,
+                height=300,
+                container=False
+            )
+    
+    # Example images
+    gr.Examples(
+        examples=[
+            ["https://upload.wikimedia.org/wikipedia/commons/1/15/Cat_August_2010-4.jpg"],
+            ["https://upload.wikimedia.org/wikipedia/commons/d/d9/Collage_of_Nine_Dogs.jpg"]
+        ],
+        inputs=image_input,
+        outputs=[label_output, confidence_bar],
+        fn=classify_image,
+        cache_examples=True
+    )
+    
+    # Button action
+    submit_btn.click(
+        fn=classify_image,
+        inputs=image_input,
+        outputs=[label_output, confidence_bar],
+        api_name="classify"
+    )
+
+if __name__ == "__main__":
+    demo.launch(debug=True)