import streamlit as st
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load the model and tokenizer from Hugging Face
model_name = "google/flan-t5-large"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)

# Streamlit app title
st.title("AIBUDDY Chatbot")

# User input
input_text = st.text_area("Enter your query:", "Translate English to French: 'Hello, how are you?'")

if st.button("Generate"):
    # Tokenize input
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids

    # Generate output
    with st.spinner("Generating response..."):
        output = model.generate(input_ids, max_length=100, num_return_sequences=1)

    # Decode the output
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    
    # Display the response
    st.subheader("Response:")
    st.write(response)

# Run the app using the command: streamlit run your_script_name.py