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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.") | |