app.py

from flask import Flask, render_template, request, jsonify
import google.generativeai as genai
import os
from PIL import Image
import io
import base64

app = Flask(__name__)

# Configuration Gemini
token = os.environ.get("TOKEN")
genai.configure(api_key=token)

generation_config = {
    "temperature": 1,
    "top_p": 0.95,
    "top_k": 64,
    "max_output_tokens": 8192,
}

safety_settings = [
    {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
    {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
    {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
    {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
]

mm = """ resous cet exercice. tu répondras en détaillant au maximum ton procédé de calcul. réponse attendue uniquement en Latex"""

model = genai.GenerativeModel(
    model_name="gemini-1.5-pro",
    generation_config=generation_config,
    safety_settings=safety_settings
)

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

@app.route('/generate', methods=['POST'])
def generate():
    try:
        # Récupérer l'image depuis la requête
        image_data = request.json.get('image')
        if not image_data:
            return jsonify({"result": "djo"})
        
        # Convertir l'image base64 en image PIL
        image_data = image_data.split(',')[1]
        image_bytes = base64.b64decode(image_data)
        image = Image.open(io.BytesIO(image_bytes))
        
        # Générer le contenu
        response = model.generate_content([mm, image])
        result = response.text
        
        return jsonify({"result": result})
    except Exception as e:
        return jsonify({"error": str(e)}), 500

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