app.py

import gradio as gr
import random
import time
from transformers import pipeline,AutoModelForSeq2SeqLM,AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")

context=""

def generate_answer(question):
  prompt = question +". \nAnswer this question given context in next line if answer is present in context otherwise say I don't know about that. Context: \n "+context
  inputs = tokenizer(prompt , return_tensors="pt")
  outputs = model.generate(**inputs)
  return (tokenizer.batch_decode(outputs, skip_special_tokens=True))

def upload_file(file):
    global context
    with open(file.name, encoding="utf-8") as f:
      context = f.read()

with gr.Blocks() as demo:
    file_output = gr.File()
    upload_button = gr.UploadButton("Click to Upload a File", file_types=["txt", "pdf"])
    upload_button.upload(upload_file, upload_button, file_output)
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.ClearButton([msg, chatbot,upload_button])

    def respond(message, chat_history):
        ans=generate_answer(message)

        chat_history.append((message, f"\n {ans} "))
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])

    with gr.Row(visible=True) as button_row:
        upvote_btn = gr.Button(value="👍  Upvote", interactive=True)
        downvote_btn = gr.Button(value="👎  Downvote", interactive=True)

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