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| 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, | |
| COMPRESS_DATA_PROMPT, | |
| COMPRESS_DATA_PROMPT_SMALL, | |
| LOG_PROMPT, | |
| LOG_RESPONSE, | |
| ) | |
| api=HfApi() | |
| client = InferenceClient( | |
| "mistralai/Mixtral-8x7B-Instruct-v0.1" | |
| ) | |
| def find_all(url): | |
| return_list=[] | |
| print (url) | |
| #if action_input in query.tasks: | |
| print (f"trying URL:: {url}") | |
| try: | |
| if url != "" and url != None: | |
| out = [] | |
| source = requests.get(url) | |
| #source = urllib.request.urlopen(url).read() | |
| soup = bs4.BeautifulSoup(source.content,'lxml') | |
| # title of the page | |
| print(soup.title) | |
| # get attributes: | |
| print(soup.title.name) | |
| # get values: | |
| print(soup.title.string) | |
| # beginning navigation: | |
| print(soup.title.parent.name) | |
| #rawp.append([tag.name for tag in soup.find_all()] ) | |
| print([tag.name for tag in soup.find_all()]) | |
| #rawp=(f'RAW TEXT RETURNED: {soup.text}') | |
| rawp=(f'RAW HTML RETURNED: {soup}') | |
| out.append(rawp) | |
| q=("a","p","span","content","article") | |
| for p in soup.find_all(q): | |
| out.append([{q:p.string,"parent":p.parent.name,"previous":[p.previous],"first-child":[b.name for b in p.children],"content":p}]) | |
| #print (f'OUT :: {out}') | |
| ''' | |
| c=0 | |
| out = str(out) | |
| rl = len(out) | |
| print(f'rl:: {rl}') | |
| #for ea in out: | |
| for i in str(out): | |
| if i == " " or i=="," or i=="\n": | |
| c +=1 | |
| print (f'c:: {c}') | |
| if rl > MAX_DATA: | |
| print("compressing...") | |
| rawp = compress_data(c,purpose,task,out) | |
| print (rawp) | |
| print (f'out:: {out}') | |
| ''' | |
| print(rawp) | |
| return True, rawp | |
| else: | |
| return False, "Enter Valid URL" | |
| except Exception as e: | |
| print (e) | |
| return False, f'Error: {e}' | |
| #else: | |
| # history = "observation: The search query I used did not return a valid response" | |
| return "MAIN", None, history, task | |
| def read_txt(txt_path): | |
| text="" | |
| with open(txt_path,"r") as f: | |
| text = f.read() | |
| f.close() | |
| print (text) | |
| return text | |
| def read_pdf(pdf_path): | |
| text="" | |
| reader = PdfReader(f'{pdf_path}') | |
| number_of_pages = len(reader.pages) | |
| for i in range(number_of_pages): | |
| page = reader.pages[i] | |
| text = f'{text}\n{page.extract_text()}' | |
| print (text) | |
| return text | |
| error_box=[] | |
| def read_pdf_online(url): | |
| uid=uuid.uuid4() | |
| print(f"reading {url}") | |
| response = requests.get(url, stream=True) | |
| print(response.status_code) | |
| text="" | |
| ################# | |
| ##################### | |
| try: | |
| if response.status_code == 200: | |
| with open("test.pdf", "wb") as f: | |
| f.write(response.content) | |
| #f.close() | |
| #out = Path("./data.pdf") | |
| #print (out) | |
| reader = PdfReader("test.pdf") | |
| number_of_pages = len(reader.pages) | |
| print(number_of_pages) | |
| for i in range(number_of_pages): | |
| page = reader.pages[i] | |
| text = f'{text}\n{page.extract_text()}' | |
| print(f"PDF_TEXT:: {text}") | |
| return text | |
| else: | |
| text = response.status_code | |
| error_box.append(url) | |
| print(text) | |
| return text | |
| except Exception as e: | |
| print (e) | |
| return e | |
| 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 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,data=None,files=None,url=None,pdf_url=None,pdf_batch=None): | |
| json_box=[] | |
| if inp == "": | |
| inp = "Process this data" | |
| history.clear() | |
| history = [(inp,"Working on it...")] | |
| yield "",history,error_box,json_box | |
| if pdf_batch.startswith("http"): | |
| c=0 | |
| data="" | |
| for i in str(pdf_batch): | |
| if i==",": | |
| c+=1 | |
| print (f'c:: {c}') | |
| try: | |
| for i in range(c+1): | |
| batch_url = pdf_batch.split(",",c)[i] | |
| bb = read_pdf_online(batch_url) | |
| data=f'{data}\nFile Name URL ({batch_url}):\n{bb}' | |
| except Exception as e: | |
| print(e) | |
| #data=f'{data}\nError reading URL ({batch_url})' | |
| if pdf_url.startswith("http"): | |
| print("PDF_URL") | |
| out = read_pdf_online(pdf_url) | |
| data=out | |
| if url.startswith("http"): | |
| val, out = find_all(url) | |
| if not val: | |
| data="Error" | |
| rawp = str(out) | |
| else: | |
| data=out | |
| if files: | |
| for i, file in enumerate(files): | |
| try: | |
| print (file) | |
| if file.endswith(".pdf"): | |
| zz=read_pdf(file) | |
| print (zz) | |
| data=f'{data}\nFile Name ({file}):\n{zz}' | |
| elif file.endswith(".txt"): | |
| zz=read_txt(file) | |
| print (zz) | |
| data=f'{data}\nFile Name ({file}):\n{zz}' | |
| except Exception as e: | |
| data=f'{data}\nError opening File Name ({file})' | |
| print (e) | |
| if data != "Error" and data != "": | |
| 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}') | |
| json_out = compress_data(c,inp,out) | |
| #json_box.append(json_out) | |
| #json_object = json.dumps(eval(json_out), indent=4) | |
| #json_box.append(json_out) | |
| print(f'JSON_BOX:: {json_out}') | |
| # Writing to sample.json | |
| #with open("tmp.json", "w") as outfile: | |
| # outfile.write(json_object) | |
| #outfile.close() | |
| #json_box.append(json_out) | |
| out = str(json_out) | |
| 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 = "Provide a valid data source" | |
| #print (rawp) | |
| #print (f'out:: {out}') | |
| #history += "observation: the search results are:\n {}\n".format(out) | |
| #task = "complete?" | |
| 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>""") | |
| chatbot = gr.Chatbot() | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| prompt=gr.Textbox(label = "Instructions (optional)") | |
| with gr.Column(scale=1): | |
| 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 (.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 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,data,file,url,pdf_url,pdf_batch],[prompt,chatbot,e_box,json_out]) | |
| stop_button.click(None,None,None,cancels=[go]) | |
| app.launch(server_port=7860,show_api=False) | |