app.py

import os
import tempfile
import gradio as gr
from model import GarbageClassifier

classifier = GarbageClassifier(
    "best_model.pth",
    classes_path="classes.json",
    device="cpu",
)

def predict_image(image):
    """Gradio interface for single image prediction."""
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
            image.save(tmp.name)
            result = classifier.predict(tmp.name)

        os.unlink(tmp.name)

        output_lines = [
            f"Class: {result['class']}",
            f"Confidence: {result['confidence'] * 100:.2f}%",
            "",
            "All probabilities:",
        ]

        for class_name, prob in sorted(
            result["all_probabilities"].items(), key=lambda x: x[1], reverse=True
        ):
            output_lines.append(f"- {class_name}: {prob * 100:.2f}%")

        return "\n".join(output_lines)
    except Exception as e:
        # Log the error to the Space logs
        print(f"Prediction error: {e}")
        return f"An error occurred during prediction: {str(e)}"

interface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Markdown(),   # <-- fixed
    title="Garbage Classification AI",
    description="Upload an image of garbage and get a classification result.",
    examples=[],
    theme=gr.themes.Soft(),
)

if __name__ == "__main__":
    interface.launch()