app.py

from flask import Flask, render_template, request, jsonify
import os
import base64
import requests
from PIL import Image
from io import BytesIO

app = Flask(__name__)

# Configuration
UPLOAD_FOLDER = 'static/captures'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

# Hugging Face API settings (replace with your API key)
HF_API_TOKEN = "your_hugging_face_api_token"  # Get from https://huggingface.co/settings/tokens
HF_API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50"  # Example model for object detection

def query_hugging_face(image_data):
    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
    response = requests.post(HF_API_URL, headers=headers, data=image_data)
    return response.json()

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/capture', methods=['POST'])
def capture():
    try:
        # Get the base64 image data from the request
        data = request.form['image']
        header, encoded = data.split(",", 1)
        binary_data = base64.b64decode(encoded)
        
        # Save the image
        filename = f"capture_{len(os.listdir(app.config['UPLOAD_FOLDER'])) + 1}.jpg"
        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        with open(filepath, "wb") as f:
            f.write(binary_data)
        
        # Process with Hugging Face API
        with open(filepath, "rb") as f:
            hf_result = query_hugging_face(f.read())
        
        # Return the image URL and Hugging Face result
        image_url = f"/{filepath}"
        return jsonify({
            'status': 'success',
            'image_url': image_url,
            'hf_result': hf_result
        })
    except Exception as e:
        return jsonify({'status': 'error', 'message': str(e)})

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=5000)