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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import tempfile
# Corrected model class name
model_name = "potsawee/t5-large-generation-squad-QuestionAnswer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
uploaded_file = st.file_uploader("Upload Document or Paragraph")
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
temp_file.write(uploaded_file.read())
# Close the file before reading its contents
temp_file.close()
with open(temp_file.name, 'r', encoding='utf-8') as file:
document_text = file.read()
st.success("Document uploaded successfully!")
else:
document_text = st.text_area("Enter Text (Optional)", height=200)
question = st.text_input("Ask a Question")
bouton_ok = st.button("Answer")
if bouton_ok:
# Improved prompt for better context
context = document_text if document_text else "Empty document."
inputs = tokenizer.encode(f"Question: {question} Context: {context}", return_tensors='pt', max_length=512, truncation=True)
outputs = model.generate(inputs, max_length=150, min_length=80, length_penalty=5, num_beams=2)
summary = tokenizer.decode(outputs[0])
st.text("Answer:")
st.text(summary)
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