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
    
    # 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

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)