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from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
import torch
import gradio as gr
import os
import csv
from gradio import inputs, outputs
from datetime import datetime
import fastapi
from typing import List, Dict
import httpx
import pandas as pd
import datasets as ds
UseMemory=True
HF_TOKEN=os.environ.get("HF_TOKEN")
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
def store_message(name: str, message: str, outputfileName: str):
basedir = os.path.dirname(__file__)
savePath = outputfileName
file_exists = exists(savePath)
if (file_exists==False):
with open(outputfileName, "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:
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
mname = "facebook/blenderbot-400M-distill"
model = BlenderbotForConditionalGeneration.from_pretrained(mname)
tokenizer = BlenderbotTokenizer.from_pretrained(mname)
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]
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 get_base(filename):
basedir = os.path.dirname(__file__)
loadPath = basedir + "\\" + filename
return loadPath
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 = 'ChatbotMemory.csv'
df = store_message(message, response, outputfileName) # Save to dataset
basedir = get_base(outputfileName)
return history, df, basedir
with gr.Blocks() as demo:
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])
demo.launch(debug=True, show_error=True) |