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 cet exercice en français avec du LaTeX. 
                        Si nécessaire, utilise du code Python pour effectuer les calculs complexes.
                        Présente ta solution de façon claire et espacée."""
                    ],
                    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:
                        if hasattr(part, 'thought') and part.thought:
                            if mode != "thinking":
                                yield 'data: ' + json.dumps({"mode": "thinking"}) + '\n\n'
                                mode = "thinking"
                        elif hasattr(part, 'executable_code') and part.executable_code:
                            if mode != "executing_code":
                                yield 'data: ' + json.dumps({"mode": "executing_code"}) + '\n\n'
                                mode = "executing_code"
                            code_block_open = "```python\n"
                            code_block_close = "\n```"
                            yield 'data: ' + json.dumps({"content": code_block_open + part.executable_code.code + code_block_close}) + '\n\n'
                        elif hasattr(part, 'code_execution_result') and part.code_execution_result:
                            if mode != "code_result":
                                yield 'data: ' + json.dumps({"mode": "code_result"}) + '\n\n'
                                mode = "code_result"
                            result_block_open = "Résultat d'exécution:\n```\n"
                            result_block_close = "\n```"
                            yield 'data: ' + json.dumps({"content": result_block_open + part.code_execution_result.output + result_block_close}) + '\n\n'
                        else:
                            if mode != "answering":
                                yield 'data: ' + json.dumps({"mode": "answering"}) + '\n\n'
                                mode = "answering"
                            if hasattr(part, 'text') and part.text:
                                yield 'data: ' + json.dumps({"content": part.text}) + '\n\n'

            except Exception as e:
                print(f"Error during generation: {e}")
                yield '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 cet exercice en français avec du LaTeX.
                        Si nécessaire, utilise du code Python pour effectuer les calculs complexes.
                        Présente ta solution de façon claire et espacée."""
                    ],
                    config=types.GenerateContentConfig(
                        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 'data: ' + json.dumps({"mode": "thinking"}) + '\n\n'
                                mode = "thinking"
                        elif hasattr(part, 'executable_code') and part.executable_code:
                            if mode != "executing_code":
                                yield 'data: ' + json.dumps({"mode": "executing_code"}) + '\n\n'
                                mode = "executing_code"
                            code_block_open = "```python\n"
                            code_block_close = "\n```"
                            yield 'data: ' + json.dumps({"content": code_block_open + part.executable_code.code + code_block_close}) + '\n\n'
                        elif hasattr(part, 'code_execution_result') and part.code_execution_result:
                            if mode != "code_result":
                                yield 'data: ' + json.dumps({"mode": "code_result"}) + '\n\n'
                                mode = "code_result"
                            result_block_open = "Résultat d'exécution:\n```\n"
                            result_block_close = "\n```"
                            yield 'data: ' + json.dumps({"content": result_block_open + part.code_execution_result.output + result_block_close}) + '\n\n'
                        else:
                            if mode != "answering":
                                yield 'data: ' + json.dumps({"mode": "answering"}) + '\n\n'
                                mode = "answering"
                            if hasattr(part, 'text') and part.text:
                                yield 'data: ' + json.dumps({"content": part.text}) + '\n\n'

            except Exception as e:
                print(f"Error during generation: {e}")
                yield '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)