app.py

from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
import torch
import gradio as gr
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

#fastapi is where its at:  share your app, share your api
import fastapi

from typing import List, Dict
import httpx
import pandas as pd
import datasets as ds

# -------------------------------------------- For Memory - you will need to set up a dataset and HF_TOKEN ---------
#UseMemory=False
UseMemory=True


DATASET_REPO_URL="https://huggingface.co/datasets/awacke1/ChatbotMemory.csv"
DATASET_REPO_ID="awacke1/ChatbotMemory.csv"
DATA_FILENAME="ChatbotMemory.csv"
DATA_FILE=os.path.join("data", DATA_FILENAME)
HF_TOKEN=os.environ.get("HF_TOKEN")

if UseMemory: 
   try:
      hf_hub_download(
      repo_id=DATASET_REPO_ID,
      filename=DATA_FILENAME,
      cache_dir=DATA_DIRNAME,
      force_filename=DATA_FILENAME
      )
   except:
      print("file not found")
      repo = Repository(
      local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
      )

def get_df(name: str):
    dataset = load_dataset(str, split="train")
    return dataset
    
#def store_message(name: str, message: str) -> str:
def store_message(name: str, message: str):
    if name and message:
        with open(DATA_FILE, "a") as csvfile:
            writer = csv.DictWriter(csvfile, fieldnames=[ "time", "message", "name", ])
            writer.writerow(
                {"time": str(datetime.now()), "message": message.strip(), "name": name.strip()  }
            )
        #repo.git_pull(rebase=True)
        commit_url = repo.push_to_hub()

        # test api retrieval of any dataset that is saved, then return it...
        # app = FastAPI()
        # see: https://gradio.app/sharing_your_app/#api-page

        # f=get_df(DATASET_REPO_ID)
        # print(f)
    #return commit_url
    return ""
# ----------------------------------------------- For Memory
    
mname = "facebook/blenderbot-400M-distill"
model = BlenderbotForConditionalGeneration.from_pretrained(mname)
tokenizer = BlenderbotTokenizer.from_pretrained(mname)

def take_last_tokens(inputs, note_history, history):
    """Filter the last 128 tokens"""
    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.
    """Add a note to the historical information"""
    note_history.append(note)
    note_history = '</s> <s>'.join(note_history)
    return [note_history]

title = "💬ChatBack🧠💾"
description = """Chatbot With persistent memory dataset allowing multiagent system AI to access a shared dataset as memory pool with stored interactions. 
 Current Best SOTA Chatbot:  https://huggingface.co/facebook/blenderbot-400M-distill?text=Hey+my+name+is+ChatBack%21+Are+you+ready+to+rock%3F  """

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: 
        store_message(message, response) # Save to dataset  -- uncomment with code above, create a dataset to store and add your HF_TOKEN from profile to this repo to use.
        df = ds.load_dataset("awacke1/ChatbotMemory.csv")
        df = df["train"].to_pandas()
        df = df.sort_values(by="time",ascending=False)
        #df.sort_index(axis=1, ascending=False)
    return history, df
    #return df
    #return history,  df

    
#gr.Interface(
#    fn=chat,
#    theme="huggingface",
#    css=".footer {display:none !important}",
#    inputs=["text", "state"],
#    #outputs=["chatbot", "state", "text"],
#    outputs=["chatbot", "state", "dataframe"],
#    title=title,
#    allow_flagging="never",
#    description=f"Gradio chatbot backed by memory in a dataset repository.",
#    article=f"The memory dataset for saves is [{DATASET_REPO_URL}]({DATASET_REPO_URL}) And here: https://huggingface.co/spaces/awacke1/DatasetAnalyzer  Code and datasets on chat are here  hf  tk: https://paperswithcode.com/datasets?q=chat&v=lst&o=newest"
#    ).launch(debug=True)



with gr.Blocks() as demo:
  gr.Markdown("<h1><center>🍰Gradio chatbot backed by memory in a dataset repository.🎨</center></h1>")
  #gr.Markdown("The memory dataset for saves is [{DATASET_REPO_URL}]({DATASET_REPO_URL}) And here: https://huggingface.co/spaces/awacke1/DatasetAnalyzer  Code and datasets on chat are here  hf  tk: https://paperswithcode.com/datasets?q=chat&v=lst&o=newest")
  
  with gr.Row():
    t1 = gr.Textbox(lines=1, default="", label="Chat Text:")
    b1 = gr.Button("Send Message")
    
  with gr.Row(): # inputs and buttons
    s1 = gr.State([])
    s2 = gr.Markdown()
  with gr.Row():
    df1 = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate")
    #chatoutput = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate", datatype = ["markdown", "markdown"], headers=['url', 'prompt'])
    
  b1.click(fn=chat, inputs=[t1, s1], outputs=[s1, df1]) 

demo.launch(debug=True, show_error=True)