from flask import Flask, request, render_template, send_from_directory
import google.generativeai as genai
import os
from PIL import Image
import io
import subprocess
import uuid
import re
import tempfile

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'uploads'
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)

# Configuration de l'API Gemini
token = os.environ.get("TOKEN")
if not token:
    raise ValueError("La variable d'environnement TOKEN doit être définie.")

genai.configure(api_key=token)

generation_config = {
    "temperature": 1,
    "top_p": 0.95,
    "top_k": 64,
    "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"},
]

mm = """resous cet exercice. tu répondras en détaillant au maximum ton procédé de calcul. réponse attendue uniquement en Latex

"""
model = genai.GenerativeModel(
    model_name="gemini-1.5-pro",
    generation_config=generation_config,
    safety_settings=safety_settings,
)



@app.route("/", methods=["GET", "POST"])
def index():
    e = ""
    if request.method == "POST":
        if "image" not in request.files:
            e = "Aucune image sélectionnée."
        else:
            image_file = request.files["image"]
            try:
                with tempfile.NamedTemporaryFile(delete=False) as temp_img:
                    image_file.save(temp_img.name)
                    image = Image.open(temp_img.name)

                response = model.generate_content([mm, image])
                latex_code = response.text
                os.remove(temp_img.name)

                match = re.search(r"\\chemfig\{(.*?)\}", latex_code, re.DOTALL)
                if match:
                    chemfig_code = match.group(1)
                    try:
                        with tempfile.TemporaryDirectory() as tmpdirname:
                            svg_filename = generate_svg_from_chemfig(chemfig_code, tmpdirname)
                            e = f'<img src="/uploads/{svg_filename}" alt="Structure chimique">'
                    except Exception as svg_error:
                        e = f"Erreur lors de la génération de l'image SVG : {str(svg_error)}"
                elif any(keyword in latex_code.lower() for keyword in ['.pdf', '.png', '.jpg', '.jpeg', '.gif', '.bmp', '.svg']):
                    e = "Désolé, les images et les PDF ne sont pas encore pris en charge dans la réponse."
                else:
                    e = latex_code


            except Exception as img_err:
                e = f"Erreur lors du traitement de l'image: {str(img_err)}"

    return render_template("index.html", e=e)


def generate_svg_from_chemfig(chemfig_code, tmpdirname):
    unique_id = str(uuid.uuid4())
    tex_filename = os.path.join(tmpdirname, f"chem_{unique_id}.tex")
    pdf_filename = os.path.join(tmpdirname, f"chem_{unique_id}.pdf")
    svg_filename = f"chem_{unique_id}.svg"

    tex_content = f"""\\documentclass[margin=10pt]{{standalone}}
\\usepackage{{chemfig}}
\\begin{{document}}
\\chemfig{{{chemfig_code}}}
\\end{{document}}"""

    with open(tex_filename, "w") as tex_file:
        tex_file.write(tex_content)

    try:
        subprocess.run(["pdflatex", "-interaction=nonstopmode", tex_filename], check=True, cwd=tmpdirname)
        subprocess.run(["pdf2svg", pdf_filename, os.path.join(app.config['UPLOAD_FOLDER'], svg_filename)], check=True)
        return svg_filename
    except subprocess.CalledProcessError as e:
        raise Exception(f"Erreur lors de la compilation LaTeX ou de la conversion en SVG : {e}")


@app.route('/uploads/<filename>')
def uploaded_file(filename):
    return send_from_directory(app.config['UPLOAD_FOLDER'], filename)


if __name__ == "__main__":
    app.run(debug=True)