import streamlit as st
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
# Load the model and tokenizer from your Hugging Face Hub repository
model_checkpoint = "abdulllah01/checkpoints"  # Replace with your actual checkpoint
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
# Create a pipeline for question answering

# Streamlit UI setup
st.title("Tech Support Bot")
st.write("Enter a context and ask a question related to Tech to get your problems solved!")

# Text area for context input
context = st.text_area("Context:", "")

# Text input for the question
question = st.text_input("Question:", "")
# Example input question and context (document) from your dataset

# Prepare the input text
input_text = f"question: {question} context: {context}"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# Generate the answer

if st.button("Get Answer"):
    if context and question:
        # Generate the answer using the pipeline
        output_ids = model.generate(input_ids)
        answer = tokenizer.decode(output_ids[0], skip_special_tokens=True)
        st.write("**Answer:**", answer)
    else:
        st.write("Please enter both context and question.")