app.py

import gradio as gr
import requests
import os
from huggingface_hub import InferenceClient,HfApi
import random
import json
import datetime
import uuid
import yt_dlp
import cv2
import whisper

from agent import (
    PREFIX,
    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)

sizes = list(whisper._MODELS.keys())
langs = ["none"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
current_size = "base"
loaded_model = whisper.load_model(current_size)

VERBOSE = True
MAX_HISTORY = 100
MAX_DATA = 20000

def dl(inp,img):
    uid=uuid.uuid4()
    fps="Error"
    out = None
    out_file=[]
    if img == None and inp !="":
        try:
            inp_out=inp.replace("https://","")
            inp_out=inp_out.replace("/","_").replace(".","_").replace("=","_").replace("?","_")
            if "twitter" in inp:
                os.system(f'yt-dlp "{inp}" --extractor-arg "twitter:api=syndication" --trim-filenames 160 -o "{uid}/{inp_out}.mp4" -S res,mp4 --recode mp4')  
            else:
                os.system(f'yt-dlp "{inp}" --trim-filenames 160 -o "{uid}/{inp_out}.mp4" -S res,mp4 --recode mp4')  
                
            out = f"{uid}/{inp_out}.mp4"
            capture = cv2.VideoCapture(out)
            fps = capture.get(cv2.CAP_PROP_FPS)
            capture.release()
        except Exception as e:
            print(e)
            out = None
    elif img !=None and inp == "":
        capture = cv2.VideoCapture(img)
        fps = capture.get(cv2.CAP_PROP_FPS)
        capture.release()
        out = f"{img}"
    return out
  
def csv(segments):
    output = ""
    for segment in segments:
      output += f"{segment['start']},{segment['end']},{segment['text']}\n"
    return output
def transcribe(path,lang,size):
    #if size != current_size:
    loaded_model = whisper.load_model(size)
    current_size = size
    results = loaded_model.transcribe(path, language=lang)
    subs = ".csv"
    if subs == "None":
      return results["text"]
    elif subs == ".csv":
      return csv(results["segments"])


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 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=[]
    error_box=""
    json_out=""
    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("""<center><h1>Mixtral 8x7B TLDR Summarizer + Web</h1><h3>Summarize Data of unlimited length</h3>""")
    with gr.Row():
        with gr.Column():
            with gr.Row():
                inp_url = gr.Textbox(label="Video URL")
                url_btn = gr.Button("Load Video")
            vid = gr.Video()
        trans_btn=gr.Button("Transcribe")
        trans = gr.Textbox(interactive=True)
    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():
        sz = gr.Dropdown(label="Model Size", choices=sizes, value='base')
        lang = gr.Dropdown(label="Language (Optional)", choices=langs, value="en")
    json_out=gr.JSON()
    e_box=gr.Textbox()
    #text=gr.JSON()
    #inp_query.change(search_models,inp_query,models_dd)

    url_btn.click(dl,[inp_url,vid],vid)
    trans_btn.click(transcribe,[vid,lang,sz],trans)
    clear_btn.click(clear_fn,None,[prompt,chatbot])
    go=button.click(summarize,[prompt,chatbot,report_check,sum_mem_check,trans],[prompt,chatbot,e_box,json_out])
    stop_button.click(None,None,None,cancels=[go])
app.queue(default_concurrency_limit=20).launch(show_api=False)