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 pypdf import PdfReader import uuid #from query import tasks from agent import ( PREFIX, SAVE_MEMORY, COMPRESS_DATA_PROMPT, COMPRESS_DATA_PROMPT_SMALL, LOG_PROMPT, LOG_RESPONSE, ) client = InferenceClient( "mistralai/Mixtral-8x7B-Instruct-v0.1" ) reponame="Omnibus/tmp" save_data=f'https://huggingface.co/datasets/{reponame}/raw/main/' #token_self = os.environ['HF_TOKEN'] #api=HfApi(token=token_self) VERBOSE = True MAX_HISTORY = 100 MAX_DATA = 20000 def format_prompt(message, history): prompt = "" for user_prompt, bot_response in history: prompt += f"[INST] {user_prompt} [/INST]" prompt += f" {bot_response} " 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 and complete the users task" ) + 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, instruct, 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=8192, seed=seed, direction=instruct, knowledge="", history=hist, ) out.append(resp) #new_history = resp print (resp) #out+=resp e=e+chunk s=s+chunk return out def compress_data_og(c, instruct, 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, stop_tokens=["observation:", "task:", "action:", "thought:"], max_tokens=8192, seed=seed, direction=instruct, 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, direction=instruct, knowledge=new_history, history="All data has been recieved.", )''' print ("final" + resp) #history = "observation: {}\n".format(resp) return resp def summarize(inp,history,report_check,sum_mem_check,data=None): json_box=[] if inp == "": inp = "Process this data" history.clear() history = [(inp,"Working on it...")] yield "",history,error_box,json_box if data != "Error" and data != "" and data != None: print(inp) out = str(data) rl = len(out) print(f'rl:: {rl}') c=1 for i in str(out): if i == " " or i=="," or i=="\n": c +=1 print (f'c:: {c}') if sum_mem_check=="Memory": #save_memory(inp,out) rawp = "Complete" if sum_mem_check=="Summarize": json_out = compress_data(c,inp,out) out = str(json_out) if report_check: rl = len(out) print(f'rl:: {rl}') c=1 for i in str(out): if i == " " or i=="," or i=="\n": c +=1 print (f'c2:: {c}') rawp = compress_data_og(c,inp,out) else: rawp = out else: rawp = "Provide a valid data source" history.clear() history.append((inp,rawp)) yield "", history,error_box,json_out ################################# def clear_fn(): return "",[(None,None)] with gr.Blocks() as app: gr.HTML("""

Mixtral 8x7B TLDR Summarizer + Web

Summarize Data of unlimited length

""") chatbot = gr.Chatbot(label="Mixtral 8x7B Chatbot",show_copy_button=True) with gr.Row(): with gr.Column(scale=3): prompt=gr.Textbox(label = "Instructions (optional)") with gr.Column(scale=1): report_check=gr.Checkbox(label="Return Report", value=True) sum_mem_check=gr.Radio(label="Output",choices=["Summary","Memory"]) button=gr.Button() #models_dd=gr.Dropdown(choices=[m for m in return_list],interactive=True) with gr.Row(): stop_button=gr.Button("Stop") clear_btn = gr.Button("Clear") with gr.Row(): with gr.Tab("Text"): data=gr.Textbox(label="Input Data (paste text)", lines=6) with gr.Tab("File"): file=gr.Files(label="Input File(s) (.pdf .txt)") with gr.Tab("Raw HTML"): url = gr.Textbox(label="URL") with gr.Tab("PDF URL"): pdf_url = gr.Textbox(label="PDF URL") with gr.Tab("PDF Batch"): pdf_batch = gr.Textbox(label="PDF URL Batch (comma separated)") json_out=gr.JSON() e_box=gr.Textbox() #text=gr.JSON() #inp_query.change(search_models,inp_query,models_dd) clear_btn.click(clear_fn,None,[prompt,chatbot]) go=button.click(summarize,[prompt,chatbot,report_check,sum_mem_check,data,file,url,pdf_url,pdf_batch],[prompt,chatbot,e_box,json_out]) stop_button.click(None,None,None,cancels=[go]) app.queue(default_concurrency_limit=20).launch(show_api=False)