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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) |