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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import os
from zipfile import ZipFile
# Streamlit UI for uploading model
st.title("Text Summarizer")
uploaded_file = st.file_uploader("Upload the saved model.zip file", type="zip")
if uploaded_file is not None:
# Extract the uploaded zip file
with ZipFile(uploaded_file, 'r') as zip_ref:
zip_ref.extractall("model_directory")
# Load the model from the extracted directory
try:
model_path = "model_directory"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
st.success("Model loaded successfully!")
except Exception as e:
st.error(f"Failed to load model: {e}")
# Text area for input
text = st.text_area("Enter the text to generate its Summary:")
# Configuration for generation
generation_config = {'max_length': 100, 'do_sample': True, 'temperature': 0.7}
if text:
try:
# Encode input
inputs_encoded = tokenizer(text, return_tensors='pt')
# Generate output
with torch.no_grad():
model_output = model.generate(inputs_encoded["input_ids"], **generation_config)[0]
# Decode output
output = tokenizer.decode(model_output, skip_special_tokens=True)
# Display results
with st.expander("Output", expanded=True):
st.write(output)
except Exception as e:
st.error(f"An error occurred during summarization: {e}") |