app.py

import gradio as gr
import tensorflow as tf
import numpy as np
import requests
from PIL import Image

# Load the model from Hugging Face
model_url = "https://huggingface.co/sebastiancgeorge/ensembled_waste_classification/blob/main/ensemble_waste_classifier%20(1).keras"
model_path = "ensemble_waste_classifier (1).keras"

# Download the model if not available
response = requests.get(model_url, allow_redirects=True)
open(model_path, "wb").write(response.content)

# Load the model
model = tf.keras.models.load_model(model_path)

# Define class labels
CLASS_LABELS = ["Cardboard", "Glass", "Metal", "Paper", "Plastic", "Trash"]

# Preprocess the input image
def preprocess_image(image):
    image = image.resize((224, 224))  # Resize to model input size
    image = np.array(image) / 255.0   # Normalize
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    return image

# Define prediction function
def classify_image(image):
    image = preprocess_image(image)
    predictions = model.predict(image)[0]
    confidence_scores = {CLASS_LABELS[i]: float(predictions[i]) for i in range(len(CLASS_LABELS))}
    return confidence_scores

# Create Gradio Interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(),
    title="Waste Classification Model",
    description="Upload an image to classify waste into categories: Cardboard, Glass, Metal, Paper, Plastic, Trash.",
)

# Launch the app
if __name__ == "__main__":
    iface.launch()