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 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') @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 ce problème en français en utilisant des formules mathématiques LaTeX quand nécessaire. Présente ta réponse de manière claire et structurée.""" ], config=types.GenerateContentConfig( thinking_config=types.ThinkingConfig( thinking_budget=8000 ), tools=[types.Tool( code_execution=types.ToolCodeExecution )] ) ) for chunk in response: for part in chunk.candidates[0].content.parts: # Gestion des modes (thinking/answering) if hasattr(part, 'thought') and part.thought: if mode != "thinking": yield f'data: {json.dumps({"mode": "thinking"})}\n\n' mode = "thinking" yield f'data: {json.dumps({"content": part.thought})}\n\n' elif part.text is not None: if mode != "answering": yield f'data: {json.dumps({"mode": "answering"})}\n\n' mode = "answering" yield f'data: {json.dumps({"content": part.text})}\n\n' # Gestion du code exécutable elif hasattr(part, 'executable_code') and part.executable_code is not None: if mode != "answering": yield f'data: {json.dumps({"mode": "answering"})}\n\n' mode = "answering" # Formater le code pour l'affichage code_content = f"```python\n{part.executable_code.code}\n```" yield f'data: {json.dumps({"content": code_content})}\n\n' # Gestion des résultats de l'exécution du code elif hasattr(part, 'code_execution_result') and part.code_execution_result is not None: if mode != "answering": yield f'data: {json.dumps({"mode": "answering"})}\n\n' mode = "answering" yield f'data: {json.dumps({"content": f"```\n{part.code_execution_result.output}\n```"})}\n\n' # Gestion des images inline elif hasattr(part, 'inline_data') and part.inline_data is not None: if mode != "answering": yield f'data: {json.dumps({"mode": "answering"})}\n\n' mode = "answering" # Convertir l'image en base64 pour l'affichage HTML image_data = part.inline_data.data image_format = part.inline_data.mime_type.split('/')[-1] image_src = f"data:{part.inline_data.mime_type};base64,{image_data}" image_tag = f'Generated Image' yield f'data: {json.dumps({"content": image_tag})}\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(): # Version similaire avec le modèle flash 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 ce problème en français en utilisant des formules mathématiques LaTeX quand nécessaire. Présente ta réponse de manière claire et structurée.""" ], config=types.GenerateContentConfig( tools=[types.Tool( code_execution=types.ToolCodeExecution )] ) ) for chunk in response: for part in chunk.candidates[0].content.parts: # Gestion des modes (thinking/answering) if hasattr(part, 'thought') and part.thought: if mode != "thinking": yield f'data: {json.dumps({"mode": "thinking"})}\n\n' mode = "thinking" yield f'data: {json.dumps({"content": part.thought})}\n\n' elif part.text is not None: if mode != "answering": yield f'data: {json.dumps({"mode": "answering"})}\n\n' mode = "answering" yield f'data: {json.dumps({"content": part.text})}\n\n' # Gestion du code exécutable elif hasattr(part, 'executable_code') and part.executable_code is not None: if mode != "answering": yield f'data: {json.dumps({"mode": "answering"})}\n\n' mode = "answering" # Formater le code pour l'affichage code_content = f"```python\n{part.executable_code.code}\n```" yield f'data: {json.dumps({"content": code_content})}\n\n' # Gestion des résultats de l'exécution du code elif hasattr(part, 'code_execution_result') and part.code_execution_result is not None: if mode != "answering": yield f'data: {json.dumps({"mode": "answering"})}\n\n' mode = "answering" yield f'data: {json.dumps({"content": f"```\n{part.code_execution_result.output}\n```"})}\n\n' # Gestion des images inline elif hasattr(part, 'inline_data') and part.inline_data is not None: if mode != "answering": yield f'data: {json.dumps({"mode": "answering"})}\n\n' mode = "answering" # Convertir l'image en base64 pour l'affichage HTML image_data = part.inline_data.data image_format = part.inline_data.mime_type.split('/')[-1] image_src = f"data:{part.inline_data.mime_type};base64,{image_data}" image_tag = f'Generated Image' yield f'data: {json.dumps({"content": image_tag})}\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)