app.py

import gradio as gr
import os 
import json 
import requests


#Chatbot2
from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
import torch
from datasets import load_dataset
# PersistDataset -----
import os
import csv
from gradio import inputs, outputs
import huggingface_hub
from huggingface_hub import Repository, hf_hub_download, upload_file
from datetime import datetime
import fastapi
from typing import List, Dict
import httpx
import pandas as pd
import datasets as ds

#Chatbot2 constants
title = """<h1 align="center">💬ChatGPT ChatBack🧠💾</h1>"""
#description = """Chatbot With persistent memory dataset allowing multiagent system AI to access a shared dataset as memory pool with stored interactions.  """
UseMemory=True
HF_TOKEN=os.environ.get("HF_TOKEN")

#ChatGPT info
API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
OPENAI_API_KEY= os.environ["HF_TOKEN"] # Add a token to this space .  Then copy it to the repository secret in this spaces settings panel.  os.environ reads from there.
# Keys for Open AI ChatGPT API usage are created from here: https://platform.openai.com/account/api-keys
description = """

Chatbot With persistent memory dataset allowing multiagent system AI to access a shared dataset as memory pool with stored interactions. 


## ChatGPT Datasets 📚
- WebText
- Common Crawl
- BooksCorpus
- English Wikipedia
- Toronto Books Corpus
- OpenWebText

## ChatGPT Datasets - Details 📚
- **WebText:** A dataset of web pages crawled from domains on the Alexa top 5,000 list. This dataset was used to pretrain GPT-2.
  - [WebText: A Large-Scale Unsupervised Text Corpus by Radford et al.](https://paperswithcode.com/dataset/webtext)
- **Common Crawl:** A dataset of web pages from a variety of domains, which is updated regularly. This dataset was used to pretrain GPT-3.
  - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/common-crawl) by Brown et al.
- **BooksCorpus:** A dataset of over 11,000 books from a variety of genres.
  - [Scalable Methods for 8 Billion Token Language Modeling](https://paperswithcode.com/dataset/bookcorpus) by Zhu et al.
- **English Wikipedia:** A dump of the English-language Wikipedia as of 2018, with articles from 2001-2017.
  - [Improving Language Understanding by Generative Pre-Training](https://huggingface.co/spaces/awacke1/WikipediaUltimateAISearch?logs=build) Space for Wikipedia Search
- **Toronto Books Corpus:** A dataset of over 7,000 books from a variety of genres, collected by the University of Toronto.
  - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://paperswithcode.com/dataset/bookcorpus) by Schwenk and Douze.
- **OpenWebText:** A dataset of web pages that were filtered to remove content that was likely to be low-quality or spammy. This dataset was used to pretrain GPT-3.
  - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al.
"""

#ChatGPT predict
def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  #repetition_penalty, top_k

    # 1. Set up a payload
    payload = {
    "model": "gpt-3.5-turbo",
    "messages": [{"role": "user", "content": f"{inputs}"}],
    "temperature" : 1.0,
    "top_p":1.0,
    "n" : 1,
    "stream": True,
    "presence_penalty":0,
    "frequency_penalty":0,
    }

    # 2. Define your headers and add a key from https://platform.openai.com/account/api-keys
    headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {OPENAI_API_KEY}"
    }

    # 3. Create a chat counter loop that feeds [Predict next best anything based on last input and attention with memory defined by introspective attention over time]
    print(f"chat_counter - {chat_counter}")
    if chat_counter != 0 :
        messages=[]
        for data in chatbot:
          temp1 = {}
          temp1["role"] = "user" 
          temp1["content"] = data[0] 
          temp2 = {}
          temp2["role"] = "assistant" 
          temp2["content"] = data[1]
          messages.append(temp1)
          messages.append(temp2)
        temp3 = {}
        temp3["role"] = "user" 
        temp3["content"] = inputs
        messages.append(temp3)
        #messages
        payload = {
        "model": "gpt-3.5-turbo",
        "messages": messages, #[{"role": "user", "content": f"{inputs}"}],
        "temperature" : temperature, #1.0,
        "top_p": top_p, #1.0,
        "n" : 1,
        "stream": True,
        "presence_penalty":0,
        "frequency_penalty":0,
        }
    chat_counter+=1

    # 4. POST it to OPENAI API
    history.append(inputs)
    print(f"payload is - {payload}")
    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
    #response = requests.post(API_URL, headers=headers, json=payload, stream=True)
    token_counter = 0 
    partial_words = "" 

    # 5. Iterate through response lines and structure readable response
    # TODO - make this parse out markdown so we can have similar interface
    counter=0
    for chunk in response.iter_lines():
        #Skipping first chunk
        if counter == 0:
          counter+=1
          continue
        #counter+=1
        # check whether each line is non-empty
        if chunk.decode() :
          chunk = chunk.decode()
          # decode each line as response data is in bytes
          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
              #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
              #  break
              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
              if token_counter == 0:
                history.append(" " + partial_words)
              else:
                history[-1] = partial_words
              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
              token_counter+=1
              yield chat, history, chat_counter  # resembles {chatbot: chat, state: history}  

def take_last_tokens(inputs, note_history, history):
    if inputs['input_ids'].shape[1] > 128:
        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()])
        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()])
        note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
        history = history[1:]
    return inputs, note_history, history
    
def add_note_to_history(note, note_history):# good example of non async since we wait around til we know it went okay.
    note_history.append(note)
    note_history = '</s> <s>'.join(note_history)
    return [note_history]  
        
# ChatGPT clear
def reset_textbox():
    return gr.update(value='')

#Chatbot2 Save Results
def SaveResult(text, outputfileName):
    basedir = os.path.dirname(__file__)
    savePath = outputfileName
    print("Saving: " + text + " to " + savePath)
    from os.path import exists
    file_exists = exists(savePath)
    if file_exists:
        with open(outputfileName, "a") as f: #append
            f.write(str(text.replace("\n","  ")))
            f.write('\n')
    else:
        with open(outputfileName, "w") as f: #write
            f.write(str("time, message, text\n")) # one time only to get column headers for CSV file
            f.write(str(text.replace("\n","  ")))
            f.write('\n')
    return

#Chatbot2 Store Message
def store_message(name: str, message: str, outputfileName: str):
    basedir = os.path.dirname(__file__)
    savePath = outputfileName
    
    # if file doesnt exist, create it with labels
    from os.path import exists
    file_exists = exists(savePath)
    
    if (file_exists==False):
        with open(savePath, "w") as f: #write
            f.write(str("time, message, text\n")) # one time only to get column headers for CSV file
            if name and message:
                writer = csv.DictWriter(f, fieldnames=["time", "message", "name"])
                writer.writerow(
                    {"time": str(datetime.now()), "message": message.strip(), "name": name.strip()  }
                )
        df = pd.read_csv(savePath)
        df = df.sort_values(df.columns[0],ascending=False)
    else:
        if name and message:
            with open(savePath, "a") as csvfile:
                writer = csv.DictWriter(csvfile, fieldnames=[ "time", "message", "name", ])
                writer.writerow(
                    {"time": str(datetime.now()), "message": message.strip(), "name": name.strip()  }
                )
        df = pd.read_csv(savePath)
        df = df.sort_values(df.columns[0],ascending=False)
    return df

#Chatbot2 get base directory of saves
def get_base(filename): 
        basedir = os.path.dirname(__file__)
        print(basedir)
        loadPath = basedir + filename 
        print(loadPath)
        return loadPath

#Chatbot2 - History
def chat(message, history):
    history = history or []
    if history: 
        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
    else:
        history_useful = []
    history_useful = add_note_to_history(message, history_useful)
    inputs = tokenizer(history_useful, return_tensors="pt")
    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
    reply_ids = model.generate(**inputs)
    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
    history_useful = add_note_to_history(response, history_useful)
    list_history = history_useful[0].split('</s> <s>')
    history.append((list_history[-2], list_history[-1]))  
    df=pd.DataFrame()
    if UseMemory: 
        outputfileName = 'ChatbotMemory3.csv' # Test first time file create
        df = store_message(message, response, outputfileName) # Save to dataset
        basedir = get_base(outputfileName)
    return history, df, basedir


# 6. Use Gradio to pull it all together
with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;}   #chatbot {height: 520px; overflow: auto;}""") as demo:
                   
    gr.HTML(title)

    # Chat bot memory - dataframe
    gr.Markdown("<h1><center>🍰Gradio chatbot backed by dataframe CSV memory🎨</center></h1>")
    with gr.Row():
       t1 = gr.Textbox(lines=1, default="", label="Chat Text:")
       b1 = gr.Button("🍰 Respond and Retrieve Messages")
    with gr.Row(): # inputs and buttons
       s1 = gr.State([])
       df1 = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate")
    with gr.Row(): # inputs and buttons
       file = gr.File(label="File")
       s2 = gr.Markdown()
    b1.click(fn=chat, inputs=[t1, s1], outputs=[s1, df1, file]) 

                    
    with gr.Column(elem_id = "col_container"):
        chatbot = gr.Chatbot(elem_id='chatbot')
        inputs = gr.Textbox(placeholder= "There is only one real true reward in life and this is existence or nonexistence.  Everything else is a corollary.", label= "Type an input and press Enter") #t
        state = gr.State([]) 
        gpt = gr.Button()
    
        with gr.Accordion("Parameters", open=False):
            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
            chat_counter = gr.Number(value=0, visible=False, precision=0)

    inputs.submit( predict, [inputs, top_p, temperature,chat_counter, chatbot, state], [chatbot, state, chat_counter],)
                    
    gpt.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
    gpt.click(reset_textbox, [], [inputs])                  
    inputs.submit(reset_textbox, [], [inputs])

    # Show ChatGPT Datasets information
    gr.Markdown(description)

    # Kickoff
    demo.queue().launch(debug=True)