import gradio as gr
import sys
import os
from flask import Flask, request, jsonify
import threading

# Import your inference function directly
from test_mode import run_inference

def classify_meme(image):
    try:
        # Convert PIL to bytes if needed
        import io
        if hasattr(image, 'save'):
            img_bytes = io.BytesIO()
            image.save(img_bytes, format='PNG')
            img_bytes = img_bytes.getvalue()
        else:
            img_bytes = image
            
        result = run_inference(img_bytes)
        
        if "error" in result:
            return f"Error: {result['error']}"
        
        prediction = result['prediction']
        confidence = max(result['probabilities'][0]) * 100
        
        return f"Classification: {prediction}\nConfidence: {confidence:.1f}%"
    except Exception as e:
        return f"Error: {str(e)}"

# Simple Gradio interface with API enabled
iface = gr.Interface(
    fn=classify_meme,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="MemeSenseX Backend",
    description="Meme content classifier"
)

# Launch with API enabled
iface.launch(share=False)