app.py

from flask import Flask, render_template, request, jsonify, Response
from google import genai
from google.genai import types
import os
from PIL import Image
import io
import logging
import json

def load_prompt():
    return " fais une dissertation "

# Configuration du logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

app = Flask(__name__)

# Configuration du client Gemini
token = os.environ.get("TOKEN")
client = genai.Client(api_key=token)

# Configuration de génération par défaut
default_generation_config = types.GenerateContentConfig(
    temperature=1,
    max_output_tokens=8192
)

# Define model names
STANDARD_MODEL_NAME = "gemini-2.5-flash"
DEEPTHINK_MODEL_NAME = "gemini-2.5-pro"

@app.route('/')
def index():
    logging.info("Rendering index.html")
    return render_template('index.html')

@app.route('/api/francais', methods=['POST'])
def gpt_francais():
    """Handles French questions with streaming enabled by default."""
    logging.info("Received request at /api/francais")
    french_prompt = request.form.get('sujet', '').strip()
    choix = request.form.get('choix', '').strip()
    style = request.form.get('style', '').strip()
    use_deepthink_str = request.form.get('use_deepthink', 'false')
    use_deepthink = use_deepthink_str.lower() == 'true'

    logging.info(f"Received data: french_prompt='{french_prompt}', choix='{choix}', style='{style}', use_deepthink='{use_deepthink}'")

    if not french_prompt:
        logging.warning("French prompt is empty.")
        return Response(f"data: {json.dumps({'type': 'error', 'content': 'Erreur'})}\n\n",
                        mimetype='text/event-stream'), 400

    # Sélectionner le modèle
    model_to_use = DEEPTHINK_MODEL_NAME if use_deepthink else STANDARD_MODEL_NAME
    logging.info(f"Using model: {model_to_use}")

    # Charger l'instruction système
    try:
        system_instruction = load_prompt()
    except Exception:
        logging.error("Erreur")
        system_instruction = "Tu es un assistant spécialisé en français."

    # Construire le prompt utilisateur
    user_prompt = f"Sujet: {french_prompt}\nType: {choix}\nStyle: {style}"

    # Configuration pour cette requête
    config = types.GenerateContentConfig(
        system_instruction=system_instruction,
        temperature=1,
        max_output_tokens=8192
    )

    # Ajouter la configuration de pensée pour DeepThink
    if use_deepthink:
        config.thinking_config = types.ThinkingConfig(
            include_thoughts=True
        )

    def generate_stream():
        try:
            thoughts = ""
            answer = ""

            for chunk in client.models.generate_content_stream(
                model=model_to_use,
                contents=[user_prompt],
                config=config
            ):
                for part in chunk.candidates[0].content.parts:
                    if not part.text:
                        continue
                    elif hasattr(part, 'thought') and part.thought:
                        if not thoughts:
                            yield f"data: {json.dumps({'type': 'thoughts_start'})}\n\n"
                        thoughts += part.text
                        yield f"data: {json.dumps({'type': 'thought', 'content': part.text})}\n\n"
                    else:
                        if not answer:
                            yield f"data: {json.dumps({'type': 'answer_start'})}\n\n"
                        answer += part.text
                        yield f"data: {json.dumps({'type': 'answer', 'content': part.text})}\n\n"

            yield f"data: {json.dumps({'type': 'done'})}\n\n"

        except Exception:
            logging.error("Erreur")
            yield f"data: {json.dumps({'type': 'error', 'content': 'Erreur'})}\n\n"

    return Response(generate_stream(), mimetype='text/event-stream')


@app.route('/api/etude-texte', methods=['POST'])
def gpt_francais_cc():
    """Analyse d'images avec streaming imposé."""
    if 'images' not in request.files:
        return Response(f"data: {json.dumps({'type': 'error', 'content': 'Erreur'})}\n\n",
                        mimetype='text/event-stream'), 400

    images = request.files.getlist('images')
    if not images:
        return Response(f"data: {json.dumps({'type': 'error', 'content': 'Erreur'})}\n\n",
                        mimetype='text/event-stream'), 400

    def generate_image_analysis():
        try:
            # Charger l'instruction système
            try:
                system_instruction = load_prompt()
            except Exception:
                logging.error("Erreur")
                system_instruction = "Tu es un assistant spécialisé dans l'analyse de textes et de documents."

            content = ["Réponds aux questions présentes dans les images."]

            for image in images:
                if image.filename:
                    img_data = image.read()
                    img_part = types.Part.from_bytes(
                        data=img_data,
                        mime_type=image.content_type or 'image/jpeg'
                    )
                    content.append(img_part)

            config = types.GenerateContentConfig(
                system_instruction=system_instruction,
                temperature=0.7,
                max_output_tokens=4096
            )

            for chunk in client.models.generate_content_stream(
                model=STANDARD_MODEL_NAME,
                contents=content,
                config=config
            ):
                for part in chunk.candidates[0].content.parts:
                    if part.text:
                        yield f"data: {json.dumps({'type': 'content', 'content': part.text})}\n\n"

            yield f"data: {json.dumps({'type': 'done'})}\n\n"

        except Exception:
            logging.error("Erreur")
            yield f"data: {json.dumps({'type': 'error', 'content': 'Erreur'})}\n\n"

    return Response(generate_image_analysis(), mimetype='text/event-stream')


if __name__ == '__main__':
    logging.info("Starting the Flask app with new Gemini SDK...")
    app.run(debug=True)