app.py

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 = "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(
                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()