import io
import gc
import base64
import torch
import gradio as gr
import tempfile
import hashlib
import os

from fastapi import FastAPI
from io import BytesIO
from PIL import Image

# Function to encode a file to Base64
def encode_file_to_base64(file_path):
    with open(file_path, "rb") as file:
        # Encode the data to Base64
        file_base64 = base64.b64encode(file.read())
        return file_base64

def update_edition_api(_: gr.Blocks, app: FastAPI, controller):
    @app.post("/cogvideox_fun/update_edition")
    def _update_edition_api(
        datas: dict,
    ):
        edition = datas.get('edition', 'v2')

        try:
            controller.update_edition(
                edition
            )
            comment = "Success"
        except Exception as e:
            torch.cuda.empty_cache()
            comment = f"Error. error information is {str(e)}"

        return {"message": comment}

def update_diffusion_transformer_api(_: gr.Blocks, app: FastAPI, controller):
    @app.post("/cogvideox_fun/update_diffusion_transformer")
    def _update_diffusion_transformer_api(
        datas: dict,
    ):
        diffusion_transformer_path = datas.get('diffusion_transformer_path', 'none')

        try:
            controller.update_diffusion_transformer(
                diffusion_transformer_path
            )
            comment = "Success"
        except Exception as e:
            torch.cuda.empty_cache()
            comment = f"Error. error information is {str(e)}"

        return {"message": comment}

def save_base64_video(base64_string):
    video_data = base64.b64decode(base64_string)

    md5_hash = hashlib.md5(video_data).hexdigest()
    filename = f"{md5_hash}.mp4"  
    
    temp_dir = tempfile.gettempdir()
    file_path = os.path.join(temp_dir, filename)

    with open(file_path, 'wb') as video_file:
        video_file.write(video_data)

    return file_path

def infer_forward_api(_: gr.Blocks, app: FastAPI, controller):
    @app.post("/cogvideox_fun/infer_forward")
    def _infer_forward_api(
        datas: dict,
    ):
        base_model_path = datas.get('base_model_path', 'none')
        lora_model_path = datas.get('lora_model_path', 'none')
        lora_alpha_slider = datas.get('lora_alpha_slider', 0.55)
        prompt_textbox = datas.get('prompt_textbox', None)
        negative_prompt_textbox = datas.get('negative_prompt_textbox', 'The video is not of a high quality, it has a low resolution. Watermark present in each frame. Strange motion trajectory. ')
        sampler_dropdown = datas.get('sampler_dropdown', 'Euler')
        sample_step_slider = datas.get('sample_step_slider', 30)
        resize_method = datas.get('resize_method', "Generate by")
        width_slider = datas.get('width_slider', 672)
        height_slider = datas.get('height_slider', 384)
        base_resolution = datas.get('base_resolution', 512)
        is_image = datas.get('is_image', False)
        generation_method = datas.get('generation_method', False)
        length_slider = datas.get('length_slider', 49)
        overlap_video_length = datas.get('overlap_video_length', 4)
        partial_video_length = datas.get('partial_video_length', 72)
        cfg_scale_slider = datas.get('cfg_scale_slider', 6)
        start_image = datas.get('start_image', None)
        end_image = datas.get('end_image', None)
        validation_video = datas.get('validation_video', None)
        denoise_strength = datas.get('denoise_strength', 0.70)
        seed_textbox = datas.get("seed_textbox", 43)

        generation_method = "Image Generation" if is_image else generation_method

        if start_image is not None:
            start_image = base64.b64decode(start_image)
            start_image = [Image.open(BytesIO(start_image))]
        
        if end_image is not None:
            end_image = base64.b64decode(end_image)
            end_image = [Image.open(BytesIO(end_image))]

        if validation_video is not None:
            validation_video = save_base64_video(validation_video)

        try:
            save_sample_path, comment = controller.generate(
                "",
                base_model_path,
                lora_model_path, 
                lora_alpha_slider,
                prompt_textbox, 
                negative_prompt_textbox, 
                sampler_dropdown, 
                sample_step_slider, 
                resize_method,
                width_slider, 
                height_slider, 
                base_resolution,
                generation_method,
                length_slider, 
                overlap_video_length, 
                partial_video_length, 
                cfg_scale_slider, 
                start_image,
                end_image,
                validation_video,
                denoise_strength,
                seed_textbox,
                is_api = True,
            )
        except Exception as e:
            gc.collect()
            torch.cuda.empty_cache()
            torch.cuda.ipc_collect()
            save_sample_path = ""
            comment = f"Error. error information is {str(e)}"
            return {"message": comment}
        
        if save_sample_path != "":
            return {"message": comment, "save_sample_path": save_sample_path, "base64_encoding": encode_file_to_base64(save_sample_path)}
        else:
            return {"message": comment, "save_sample_path": save_sample_path}