Upload app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 import tensorflow as tf
 import numpy as np
@@ -6,14 +5,13 @@ from PIL import Image
 from huggingface_hub import hf_hub_download
 import os
 
-
-MODEL_REPO = "Ahmedhassan54/Image-Classification" 
+# Configuration
+MODEL_REPO = "your_hf_username/cat-dog-classifier"  # 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(
@@ -21,59 +19,78 @@ def load_model_from_hf():
                 filename=MODEL_FILE,
                 cache_dir="."
             )
-          
             os.system(f"cp {model_path} {MODEL_FILE}")
         
-        
-        model = tf.keras.models.load_model(MODEL_FILE)
-        print("Model loaded successfully!")
-        return model
+        return tf.keras.models.load_model(MODEL_FILE)
     except Exception as e:
-        print(f"Error loading model: {str(e)}")
-        raise
-
+        raise gr.Error(f"Model loading failed: {str(e)}")
 
 model = load_model_from_hf()
 
-
 def classify_image(image):
     try:
-       
-        image = image.resize((150, 150)) 
-        image_array = np.array(image) / 255.0  
-        image_array = np.expand_dims(image_array, axis=0) 
+        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])
         
-        if confidence > 0.5:
-            return {
-                "Dog": confidence * 100,
-                "Cat": (1 - confidence) * 100
-            }
-        else:
-            return {
-                "Cat": (1 - confidence) * 100,
-                "Dog": confidence * 100
-            }
+        return {
+            "Dog": confidence,
+            "Cat": 1 - confidence
+        }
     except Exception as e:
-        return f"Error processing image: {str(e)}"
-
+        raise gr.Error(f"Classification error: {str(e)}")
 
-demo = gr.Interface(
-    fn=classify_image,
-    inputs=gr.Image(type="pil", label="Upload Image"),
-    outputs=gr.Label(num_top_classes=2, label="Predictions"),
-    title="🐱 Cat vs Dog Classifier 🐶",
-    description="Upload an image to classify whether it's a cat or dog",
-    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"]
-    ],
-    allow_flagging="never"
-)
+# 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(debug=True, server_port=7860)
+    demo.launch()