app.py

import gradio as gr
#import urllib.request
#import requests
#import bs4
#import lxml
import os
#import subprocess
from huggingface_hub import InferenceClient,HfApi
import random
import json
import datetime
#from query import tasks
from agent import (
    PREFIX,
    COMPRESS_DATA_PROMPT,
    COMPRESS_DATA_PROMPT_SMALL,
    LOG_PROMPT,
    LOG_RESPONSE,
)
api=HfApi()



client = InferenceClient(
    "mistralai/Mixtral-8x7B-Instruct-v0.1"
)

def parse_action(string: str):
    print("PARSING:")
    print(string)
    assert string.startswith("action:")
    idx = string.find("action_input=")
    print(idx)
    if idx == -1:
        print ("idx == -1")
        print (string[8:])
        return string[8:], None

    print ("last return:")
    print (string[8 : idx - 1])
    print (string[idx + 13 :].strip("'").strip('"'))
    return string[8 : idx - 1], string[idx + 13 :].strip("'").strip('"')



VERBOSE = True
MAX_HISTORY = 100
MAX_DATA = 20000

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(
    prompt_template,
    stop_tokens,
    max_tokens,
    seed,
    **prompt_kwargs,
):
    print(seed)
    timestamp=datetime.datetime.now()
    
    generate_kwargs = dict(
        temperature=0.9,
        max_new_tokens=max_tokens,
        top_p=0.95,
        repetition_penalty=1.0,
        do_sample=True,
        seed=seed,
    )
    
    content = PREFIX.format(
        timestamp=timestamp,
        purpose="Compile the provided data"
    ) + prompt_template.format(**prompt_kwargs)
    if VERBOSE:
        print(LOG_PROMPT.format(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
        #yield resp

    if VERBOSE:
        print(LOG_RESPONSE.format(resp))
    return resp

def compress_data(c, history):
    seed=random.randint(1,1000000000)
    
    print (c)
    #tot=len(purpose)
    #print(tot)
    divr=int(c)/MAX_DATA
    divi=int(divr)+1 if divr != int(divr) else int(divr)
    chunk = int(int(c)/divr)
    print(f'chunk:: {chunk}')
    print(f'divr:: {divr}')
    print (f'divi:: {divi}')
    out = []
    #out=""
    s=0
    e=chunk
    print(f'e:: {e}')
    new_history=""
    #task = f'Compile this data to fulfill the task: {task}, and complete the purpose: {purpose}\n'
    for z in range(divi):
        print(f's:e :: {s}:{e}')
        
        hist = history[s:e]
        
        resp = run_gpt(
            COMPRESS_DATA_PROMPT_SMALL,
            stop_tokens=["observation:", "task:", "action:", "thought:"],
            max_tokens=4096,
            seed=seed,
            purpose="Compress data",
            knowledge=new_history,
            history=hist,
        )
        new_history = resp
        print (resp)
        out+=resp
        e=e+chunk
        s=s+chunk
    
    resp = run_gpt(
        COMPRESS_DATA_PROMPT,
        stop_tokens=["observation:", "task:", "action:", "thought:"],
        max_tokens=8192,
        seed=seed,
        purpose="Compile report",
        knowledge=new_history,
        history="All data has been recieved.",
    )
    print ("final" + resp)
    history = "observation: {}\n".format(resp)
    return history



def summarize(inp,file=None):
    print(inp)
    out = str(inp)
    rl = len(out)
    print(f'rl:: {rl}')
    c=0
    for i in str(out):
        if i == " " or i=="," or i=="\n":
            c +=1
    print (f'c:: {c}')
    
    rawp = compress_data(c,out)    
    
    print (rawp)
    print (f'out:: {out}')
    #history += "observation: the search results are:\n {}\n".format(out)
    task = "complete?"
    return rawp        
#################################


examples =[
    "what are todays breaking news stories?",
    "find the most popular model that I can use to generate an image by providing a text prompt",
    "return the top 10 models that I can use to identify objects in images",
    "which models have the most likes from each category?"
]


app = gr.ChatInterface(
    fn=summarize,
    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
    title="<center>Mixtral 8x7B TLDR",
    description="<center>Summarize Lengthy data with<br>Mixtral 8x7B",
    examples=examples,
    concurrency_limit=20,
)

'''
with gr.Blocks() as app:
    with gr.Row():
        inp_query=gr.Textbox()
        models_dd=gr.Dropdown(choices=[m for m in return_list],interactive=True)
    with gr.Row():
        button=gr.Button()
        stop_button=gr.Button("Stop")
    text=gr.JSON()
    inp_query.change(search_models,inp_query,models_dd)
    go=button.click(test_fn,None,text)
    stop_button.click(None,None,None,cancels=[go])
'''
app.launch(server_port=7860,show_api=False)