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from flask import Flask, request, render_template, jsonify
import PIL.Image
import google.generativeai as genai
import os
from tempfile import NamedTemporaryFile
from gradio_client import Client, handle_file # Importez gradio_client
app = Flask(__name__)
# Configuration de Gemini
generation_config = {
"temperature": 1,
"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"},
]
GOOGLE_API_KEY = os.environ.get("TOKEN")
genai.configure(api_key=GOOGLE_API_KEY)
# Fonction pour interroger Gemini
def query_gemini(image_path, prompt="Résous ce problème mathématiques. Je veux qu'en réponse tu me donnes un rendu complet en utilisant du Latex."):
img = PIL.Image.open(image_path)
model = genai.GenerativeModel(
model_name="gemini-1.5-pro-002",
generation_config=generation_config,
safety_settings=safety_settings
)
try:
response = model.generate_content([prompt, img], request_options={"timeout": 600})
return response.text
except Exception as e:
return str(e)
# Fonction pour interroger Qwen2
def query_qwen2(image_path, question="Résous ce problème mathématiques. Donne la réponse en utilisant LaTeX."):
try:
client = Client("Qwen/Qwen2-Math-Demo")
result = client.predict(
image=handle_file(image_path),
sketchpad=None,
question=question,
api_name="/math_chat_bot"
)
return result
except Exception as e:
return str(e)
@app.route('/')
def index():
return render_template('math.html')
@app.route('/upload', methods=['POST'])
def upload_image():
if 'image' not in request.files:
return jsonify({'error': 'Aucune image fournie'}), 400
file = request.files['image']
model_choice = request.form.get('model_choice', 'gemini') # Obtient le choix du modèle
if file.filename == '':
return jsonify({'error': 'Aucun fichier sélectionné'}), 400
with NamedTemporaryFile(delete=False) as temp_file:
file.save(temp_file.name)
try:
if model_choice == "mariam's":
result = query_gemini(temp_file.name)
else:
result = query_qwen2(temp_file.name)
os.unlink(temp_file.name)
return jsonify({'result': result, 'model': model_choice})
except Exception as e:
return jsonify({'error': str(e)}), 500
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