app.py

import os
os.system("pip install transformers")
os.system("pip install torch torchvision")
os.system("pip install sentencepiece")
os.system("pip install streamlit")

import streamlit as st
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load the T5 model and tokenizer
model = T5ForConditionalGeneration.from_pretrained("t5-base")
tokenizer = T5Tokenizer.from_pretrained("t5-base")

# Streamlit app title and description
st.title("Hugging Face T5-based Chatbot")
st.write("This chatbot can answer various questions and perform math calculations.")

# User input text box
user_input = st.text_input("Ask a question or type a math calculation:")

# Check if the user input is not empty
if user_input:
    # Add the '!' prefix to indicate it's a chatbot response
    input_text = "! " + user_input
    # Encode the user input
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    
    # Generate the response using the T5 model
    with st.spinner("Generating response..."):
        output = model.generate(input_ids)
    
    # Decode and beautify the response text
    response_text = tokenizer.decode(output[0], skip_special_tokens=True)
    st.info("Response:")
    st.success(response_text)