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
import pandas as pd
import logging

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration
MODEL_REPO = "Ahmedhassan54/Image-Classification"
MODEL_FILE = "best_model.h5"

# Download model from Hugging Face Hub
def load_model_from_hf():
    try:
        logger.info("Attempting to load model...")
        
        # Check if model exists in cache
        model_path = hf_hub_download(
            repo_id=MODEL_REPO,
            filename=MODEL_FILE,
            cache_dir=".",
            force_download=True  # Ensure fresh download
        )
        logger.info(f"Model downloaded to: {model_path}")
        
        # Load model
        logger.info("Loading model...")
        model = tf.keras.models.load_model(model_path)
        logger.info("Model loaded successfully!")
        return model
        
    except Exception as e:
        logger.error(f"Model loading failed: {str(e)}")
        raise gr.Error(f"⚠️ Model loading failed: {str(e)}. Check the logs for details.")

# Load model when the app starts
try:
    model = load_model_from_hf()
except Exception as e:
    model = None
    logger.error(f"Proceeding without model due to: {str(e)}")

def classify_image(image):
    try:
        logger.info("\nClassification started...")
        
        # Debug: Check input type
        logger.info(f"Input type: {type(image)}")
        
        if image is None:
            raise ValueError("No image provided")
        
        # Convert image if needed
        if isinstance(image, np.ndarray):
            logger.info("Converting numpy array to PIL Image")
            image = Image.fromarray(image)
        elif not isinstance(image, Image.Image):
            raise ValueError(f"Unexpected image type: {type(image)}")
        
        # Preprocess image
        logger.info("Preprocessing image...")
        image = image.resize((150, 150))
        image_array = np.array(image) / 255.0
        
        # Add batch dimension
        if len(image_array.shape) == 3:
            image_array = np.expand_dims(image_array, axis=0)
        
        logger.info(f"Image array shape: {image_array.shape}")
        
        # Make prediction
        logger.info("Making prediction...")
        if model is None:
            raise gr.Error("Model failed to load. Cannot make predictions.")
        
        prediction = model.predict(image_array, verbose=0)
        logger.info(f"Raw prediction: {prediction}")
        
        confidence = float(prediction[0][0])
        logger.info(f"Confidence score: {confidence}")
        
        # Format outputs
        label_output = {
            "Cat": round(1 - confidence, 4),
            "Dog": round(confidence, 4)
        }
        
        # Create dataframe for bar plot
        plot_data = pd.DataFrame({
            'Class': ['Cat', 'Dog'],
            'Confidence': [1 - confidence, confidence]
        })
        
        logger.info("Classification successful!")
        logger.info(f"Results: {label_output}")
        
        return label_output, plot_data
        
    except Exception as e:
        logger.error(f"Error during classification: {str(e)}", exc_info=True)
        raise gr.Error(f"🔴 Classification failed: {str(e)}")

# Custom CSS
css = """

.gradio-container {

    background: linear-gradient(to right, #f5f7fa, #c3cfe2);

}

footer {

    visibility: hidden

}

.error-message {

    color: red !important;

    font-weight: bold !important;

}

"""

# Build the interface
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    gr.Markdown("""

    # 🐾 Cat vs Dog Classifier 🦮

    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")
            with gr.Row():
                submit_btn = gr.Button("Classify", variant="primary")
                clear_btn = gr.Button("Clear")
        
        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,
        label="Try these examples:"
    )
    
    # Button actions
    submit_btn.click(
        fn=classify_image,
        inputs=image_input,
        outputs=[label_output, confidence_bar],
        api_name="classify"
    )
    
    clear_btn.click(
        fn=lambda: [None, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})],
        inputs=None,
        outputs=[image_input, confidence_bar],
        show_progress=False
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(debug=True)