NegiGPT / app.py
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Create app.py
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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="πŸ‘ Upvote", interactive=True)
downvote_btn = gr.Button(value="πŸ‘Ž Downvote", interactive=True)
demo.queue()
demo.launch(debug=True)