app.py

import gradio as gr
import requests
import io
import os
import tempfile
from PIL import Image
import traceback

# API details
API_URL = "https://api-inference.huggingface.co/models/SaladSlayer00/twin_matcher"
headers = {"Authorization": f"{os.getenv('TOKEN')}"}

# Function to query the API with an image file
def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    response.raise_for_status()  # Ensure successful response
    return response.json()

# Function to process the image and predict
def predict(image):
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpeg') as tmp_file:
            image.save(tmp_file, format="JPEG")
            tmp_file_path = tmp_file.name

        predictions = query(tmp_file_path)

        top_prediction = max(predictions, key=lambda x: x['score'])
        result = (top_prediction['label'], top_prediction['score'])

        os.remove(tmp_file_path)

        return result
    except Exception as e:
        print(f"Exception during prediction: {e}")
        traceback.print_exc()
        return "Error during prediction", "N/A"

# Gradio interface
with gr.Interface(fn=predict, 
                  inputs=gr.Image(type="pil"), 
                  outputs=["text", "number"],
                  title="Celebrity Lookalike Predictor",
                  description="Take a snapshot to see which celebrity you look like!") as demo:
    demo.launch()