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from flask import Flask, render_template, request, jsonify, Response, stream_with_context
from google import genai
from google.genai import types
import os
from PIL import Image
import io
import base64
import json
import re
app = Flask(__name__)
GOOGLE_API_KEY = os.environ.get("GEMINI_API_KEY")
client = genai.Client(
api_key=GOOGLE_API_KEY,
)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/free')
def indexx():
return render_template('maj.html')
def process_markdown_and_code(text):
"""Traite le texte pour identifier et formater le code et le markdown"""
# Convertit le texte en HTML formaté
# Cette fonction pourrait être étendue pour utiliser une bibliothèque de markdown
return text
def format_code_execution_result(response_parts):
"""Formate les résultats d'exécution de code pour l'affichage HTML"""
result = []
for part in response_parts:
# Traitement du texte (équivalent à display(Markdown(part.text)))
if hasattr(part, 'text') and part.text is not None:
result.append({
'type': 'markdown',
'content': part.text
})
# Traitement du code exécutable
if hasattr(part, 'executable_code') and part.executable_code is not None:
result.append({
'type': 'code',
'content': part.executable_code.code
})
# Traitement des résultats d'exécution
if hasattr(part, 'code_execution_result') and part.code_execution_result is not None:
result.append({
'type': 'execution_result',
'content': part.code_execution_result.output
})
# Traitement des images (équivalent à display(Image(data=part.inline_data.data)))
if hasattr(part, 'inline_data') and part.inline_data is not None:
# Encodage de l'image en base64 pour l'affichage HTML
img_data = base64.b64encode(part.inline_data.data).decode('utf-8')
result.append({
'type': 'image',
'content': img_data,
'format': 'png' # Supposé comme png par défaut
})
return result
@app.route('/solve', methods=['POST'])
def solve():
try:
image_data = request.files['image'].read()
img = Image.open(io.BytesIO(image_data))
buffered = io.BytesIO()
img.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
def generate():
mode = 'starting'
try:
response = client.models.generate_content_stream(
model="gemini-2.5-pro-exp-03-25",
contents=[
{'inline_data': {'mime_type': 'image/png', 'data': img_str}},
"""Résous ça en français with rendering latex"""
],
config=types.GenerateContentConfig(
# Ajouter l'outil d'exécution de code
tools=[types.Tool(
code_execution=types.ToolCodeExecution
)]
)
)
for chunk in response:
for part in chunk.candidates[0].content.parts:
if hasattr(part, 'thought') and part.thought:
if mode != "thinking":
yield f'data: {json.dumps({"mode": "thinking"})}\n\n'
mode = "thinking"
else:
if mode != "answering":
yield f'data: {json.dumps({"mode": "answering"})}\n\n'
mode = "answering"
# Gestion des différents types de contenu
if hasattr(part, 'text') and part.text is not None:
yield f'data: {json.dumps({"content": part.text, "type": "text"})}\n\n'
if hasattr(part, 'executable_code') and part.executable_code is not None:
yield f'data: {json.dumps({"content": part.executable_code.code, "type": "code"})}\n\n'
if hasattr(part, 'code_execution_result') and part.code_execution_result is not None:
yield f'data: {json.dumps({"content": part.code_execution_result.output, "type": "result"})}\n\n'
if hasattr(part, 'inline_data') and part.inline_data is not None:
img_data = base64.b64encode(part.inline_data.data).decode('utf-8')
yield f'data: {json.dumps({"content": img_data, "type": "image"})}\n\n'
except Exception as e:
print(f"Error during generation: {e}")
yield f'data: {json.dumps({"error": str(e)})}\n\n'
return Response(
stream_with_context(generate()),
mimetype='text/event-stream',
headers={
'Cache-Control': 'no-cache',
'X-Accel-Buffering': 'no'
}
)
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/solved', methods=['POST'])
def solved():
try:
image_data = request.files['image'].read()
img = Image.open(io.BytesIO(image_data))
buffered = io.BytesIO()
img.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
def generate():
mode = 'starting'
try:
response = client.models.generate_content_stream(
model="gemini-2.5-flash-preview-04-17",
contents=[
{'inline_data': {'mime_type': 'image/png', 'data': img_str}},
"""Résous ça en français with rendering latex. utilise python pour les calculs et figures."""
],
config=types.GenerateContentConfig(
thinking_config=types.ThinkingConfig(
thinking_budget=16000
),
# Ajouter l'outil d'exécution de code
tools=[types.Tool(
code_execution=types.ToolCodeExecution
)]
)
)
for chunk in response:
for part in chunk.candidates[0].content.parts:
if hasattr(part, 'thought') and part.thought:
if mode != "thinking":
yield f'data: {json.dumps({"mode": "thinking"})}\n\n'
mode = "thinking"
else:
if mode != "answering":
yield f'data: {json.dumps({"mode": "answering"})}\n\n'
mode = "answering"
# Gestion des différents types de contenu
if hasattr(part, 'text') and part.text is not None:
yield f'data: {json.dumps({"content": part.text, "type": "text"})}\n\n'
if hasattr(part, 'executable_code') and part.executable_code is not None:
yield f'data: {json.dumps({"content": part.executable_code.code, "type": "code"})}\n\n'
if hasattr(part, 'code_execution_result') and part.code_execution_result is not None:
yield f'data: {json.dumps({"content": part.code_execution_result.output, "type": "result"})}\n\n'
if hasattr(part, 'inline_data') and part.inline_data is not None:
img_data = base64.b64encode(part.inline_data.data).decode('utf-8')
yield f'data: {json.dumps({"content": img_data, "type": "image"})}\n\n'
except Exception as e:
print(f"Error during generation: {e}")
yield f'data: {json.dumps({"error": str(e)})}\n\n'
return Response(
stream_with_context(generate()),
mimetype='text/event-stream',
headers={
'Cache-Control': 'no-cache',
'X-Accel-Buffering': 'no'
}
)
except Exception as e:
return jsonify({'error': str(e)}), 500
if __name__ == '__main__':
app.run(debug=True) |