app.py

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")
history = []

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)
    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)
    task=None
    #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)
    history.append((purpose,out_prompt))
    yield (history,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)
        yield (history,out)
    else:
        yield ([(purpose,"an Error occured")],None)

################################################
style="""
.top_head{
    background: no-repeat;
    background-image: url(https://huggingface.co/spaces/johann22/chat-diffusion/resolve/main/image.png);
    background-position-y: bottom;
    height: 200px;
}
.top_h1{
    color:blue;
}

"""
with gr.Blocks(css=style) as iface:
    gr.HTML("""<div class="top_head"><center><h1 class="top_h1">Mixtral Chat Diffusion</h1><br><h3 class="top_h1">This chatbot will generate images</h3></center></div?""")
    #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(show_copy_button=True, layout='panel')
            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],[chatbot,sumbox])
    sub_e = msg.submit(run, [msg, chatbot,model_drop], [chatbot,sumbox])
    stop_b.click(None,None,None, cancels=[sub_b,sub_e])
iface.launch()