import os
import random
from huggingface_hub import InferenceClient
import gradio as gr
from datetime import datetime
import agent
from models import models
import requests
import io
import uuid
base_url="https://johann22-chat-diffusion.hf.space/" 

loaded_model=[]
for i,model in enumerate(models):
    loaded_model.append(gr.load(f'models/{model}'))
print (loaded_model)

now = datetime.now()
date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")

client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
model = gr.load("models/dreamlike-art/dreamlike-photoreal-2.0")
history = []

def infer(txt):
    return (model(txt))

def format_prompt(message, history):
  prompt = "<s>"
  for user_prompt, bot_response in history:
    prompt += f"[INST] {user_prompt} [/INST]"
    prompt += f" {bot_response}</s> "
  prompt += f"[INST] {message} [/INST]"
  return prompt

def run_gpt(in_prompt,history):
    prompt=format_prompt(in_prompt,history)
    seed = random.randint(1,1111111111111111)
    print (seed)
    generate_kwargs = dict(
        temperature=1.0,
        max_new_tokens=256,
        top_p=0.99,
        repetition_penalty=1.0,
        do_sample=True,
        seed=seed,
    )
    content = agent.GENERATE_PROMPT + prompt
    #print(content)
    stream = client.text_generation(content, **generate_kwargs, stream=True, details=True, return_full_text=False)
    resp = ""
    for response in stream:
        resp += response.token.text
    return resp


def run(purpose,history,model_drop):
    
    #print(purpose)
    #print(hist)
    task=None
    directory="./"
    if history:
        history=str(history).strip("[]")
    if not history:
        history = ""

    #action_name, action_input = parse_action(line)
    out_prompt = run_gpt(
        purpose,
        history,
        
        )

    yield ("",[(purpose,out_prompt)],None)
    #out_img = infer(out_prompt)
    model=loaded_model[int(model_drop)]
    out_img=model(out_prompt)
    print(out_img)
    url=f'https://johann22-chat-diffusion.hf.space/file={out_img}'
    print(url)
    uid = uuid.uuid4()
    #urllib.request.urlretrieve(image, 'tmp.png')
    #out=Image.open('tmp.png')
    r = requests.get(url, stream=True)
    if r.status_code == 200:
        out = Image.open(io.BytesIO(r.content))
    yield ("",[(purpose,out_prompt)],out)
        #return ("", [(purpose,history)])




################################################

with gr.Blocks() as iface:
    gr.HTML("""<center><h1>Chat Diffusion</h1><br><h3>This chatbot will generate images</h3></center>""")
    with gr.Row():
        with gr.Column():
            chatbot=gr.Chatbot()
            msg = gr.Textbox()
            model_drop=gr.Dropdown(label="Diffusion Models", type="index", choices=[m for m in models], value=models[0])
    with gr.Row():
        submit_b = gr.Button()
        stop_b = gr.Button("Stop")
        clear = gr.ClearButton([msg, chatbot])
    
    sumbox=gr.Image(label="Image",type="filepath")

        
    sub_b = submit_b.click(run, [msg,chatbot,model_drop],[msg,chatbot,sumbox])
    sub_e = msg.submit(run, [msg, chatbot,model_drop], [msg, chatbot,sumbox])
    stop_b.click(None,None,None, cancels=[sub_b,sub_e])
iface.queue().launch(share=True,show_api=False)