app.py

import os
import requests
import yaml
from flask import Flask, request, jsonify
from dotenv import load_dotenv
from PIL import Image
import io
from transformers import pipeline

# Load environment variables
load_dotenv()
api_key = os.getenv('HF_API_KEY')
model_path = os.getenv('MODEL_PATH')

app = Flask(__name__)

# Load configuration
with open('config.yaml', 'r') as file:
    config = yaml.safe_load(file)

def get_model_predictions(text):
    headers = {"Authorization": f"Bearer {api_key}"}
    payload = {"inputs": text}
    response = requests.post(f"https://api-inference.huggingface.co/models/{model_path}", headers=headers, json=payload)
    return response.json()

def process_image(image_file):
    image = Image.open(image_file)
    # Implement image processing here
    return "Image processed"

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    text = data.get('text')
    image_file = data.get('image')
    
    if text:
        prediction = get_model_predictions(text)
        return jsonify(prediction)
    elif image_file:
        image = io.BytesIO(image_file)
        result = process_image(image)
        return jsonify({"result": result})
    else:
        return jsonify({"error": "No input provided"})

@app.route('/', methods=['GET'])
def index():
    return "Welcome to My AI! Use /predict to interact."

if __name__ == '__main__':
    app.run(debug=True, use_reloader=False)