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mixtral-diffusion
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import os
import random
from huggingface_hub import InferenceClient
import gradio as gr
#from utils import parse_action, parse_file_content, read_python_module_structure
from datetime import datetime
from PIL import Image
import agent
from models import models
import urllib.request
import uuid
import requests
import io
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/stabilityai/sdxl-turbo")

VERBOSE = True
MAX_HISTORY = 10000
#MODEL = "gpt-3.5-turbo"  # "gpt-4"
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,
):
    print(f'history :: {history}')
    prompt=format_prompt(in_prompt,history)
    seed = random.randint(1,1111111111111111)
    print (seed)
    generate_kwargs = dict(
        temperature=1.0,
        max_new_tokens=1048,
        top_p=0.99,
        repetition_penalty=1.0,
        do_sample=True,
        seed=seed,
    )

    
    content = agent.GENERATE_PROMPT + prompt

    print(content)
    
    #formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
    #formatted_prompt = format_prompt(f'{content}', history)

    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 (history)
    #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-mixtral-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>""")
    #chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
    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")

        
    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.launch()
'''
gr.ChatInterface(
    fn=run,
    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
    title="Mixtral 46.7B\nMicro-Agent\nInternet Search <br> development test",
    examples=examples,
    concurrency_limit=20,
).launch(show_api=False)
'''