from flask import Blueprint, Response, jsonify, request
from controllers.gdrive_controller import GDriveController
from controllers.glb_controller import GlbController
from controllers.render_controller import RenderController
from controllers.suggest_controller import SuggestController

gdrive_bp = Blueprint('gdrive', __name__)
glb_bp = Blueprint('glb', __name__)
render_bp = Blueprint('render', __name__)
suggest_bp = Blueprint('suggest', __name__)

gdrive_controller = GDriveController()
glb_controller = GlbController()
render_controller = RenderController()
suggest_controller = SuggestController()

def setup_routes(app):
    @app.route('/glb', methods=['GET'])
    # Image to model
    def get_glb() -> Response:
        hf_token = request.args.get('hf_token', '', type=str)
        base_image_url = request.args.get('base_image_url', type=str)
        ss_guidance_strength = request.args.get('ss_guidance_strength', 7.5, type=float)
        ss_sampling_steps = request.args.get('ss_sampling_steps', 12, type=int)
        slat_guidance_strength = request.args.get('slat_guidance_strength', 3, type=float)
        slat_sampling_steps = request.args.get('slat_sampling_steps', 12, type=int)
        multiimage_algo = request.args.get('multiimage_algo', 'stochastic', type=str)
        mesh_simplify = request.args.get('mesh_simplify', 0.95, type=bool)
        texture_size = request.args.get('texture_size', 1024, type=int)

        return glb_controller.get_glb(hf_token, base_image_url, ss_guidance_strength,
                                  ss_sampling_steps, slat_guidance_strength,
                                  slat_sampling_steps, multiimage_algo,
                                  mesh_simplify, texture_size)
    
    # Suggest
    @app.route('/suggest', methods=['GET'])
    def get_suggest() -> Response:
        hf_token = request.args.get('hf_token', '', type=str)
        message = request.args.get('message', 'Hello!!', type=str)
        system_prompt = request.args.get('system_prompt', 'You are a helpful assistant. First recognize the user request and then reply carefully with thinking.', type=str)
        temperature = request.args.get('temperature', 0.7, type=float)
        max_new_tokens = request.args.get('max_new_tokens', 12000, type=int)
        top_k_sampling = request.args.get('top_k_sampling', 50, type=int)
        repetition_penalty = request.args.get('repetition_penalty', 1.1, type=float)
        top_p_sampling = request.args.get('top_p_sampling', 0.95, type=float)

        return suggest_controller.get_suggest(hf_token, message, system_prompt,
                                              temperature, max_new_tokens,
                                              top_k_sampling, repetition_penalty,
                                              top_p_sampling)

    # Upload to Google Drive
    @app.route('/upload', methods=['POST'])
    def post_gdrive() -> Response:
        local_file_path = request.form.get('local_file_path', '', type=str)
        folder_id = request.form.get('folder_id', '', type=str)

        return gdrive_controller.post_gdrive(local_file_path, folder_id)
    
    # Text + Sketch to image
    @app.route('/render', methods=['POST'])
    def post_render() -> Response:
        # Vérifier si un fichier est envoyé
        if 'image' not in request.files:
            return jsonify({"error": "Aucune image envoyée"}), 400
        
        hf_token = request.form.get('hf_token', '', type=str)
        base_image = request.files.get('image')
        prompt = request.form.get('prompt', 'A realistic photo of a building', type=str)
        negative_prompt = request.form.get('negative_prompt', 'sketch, drawing', type=str)
        instruct_query = request.form.get('instruct_query', 'use everything from the image', type=str)
        scale = request.form.get('scale', 0.8, type=float)
        nfaa_negative_prompt = request.form.get('nfaa_negative_prompt', 'naked, bikini, skimpy, scanty, bare skin, lingerie, swimsuit, exposed, see-through', type=str)

        return render_controller.post_render(hf_token, base_image, prompt, negative_prompt,
                                            instruct_query, scale, nfaa_negative_prompt)