import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM


st.title("Generating Response with HuggingFace Models")
st.markdown("## Model: `facebook/blenderbot-400M-distill`")


with st.spinner("Getting this ready for you.."):
    model_name = "facebook/blenderbot-400M-distill"
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)



def get_response(input_text):

    # Tokenize the input text and history
    inputs = tokenizer.encode_plus(input_text, return_tensors="pt")

    # Generate the response from the model
    outputs = model.generate(**inputs)

    # Decode the response
    response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()

    return response

user_input = st.text_area("Enter your query here...")

if st.button("Get Response") and user_input:
    with st.spinner("Generating Response..."):
        answer = get_response(user_input)
    if answer is not None:
        st.success('Great! Response generated successfully')
        st.write(answer)