Spaces:
Sleeping
Sleeping
File size: 1,372 Bytes
fb4a3c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
import streamlit as st
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
# Load the fine-tuned T5 model and tokenizer
model_path = "Neupane9Sujal/Text_Summarization"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Function to generate summaries
def generate_summary(text):
# Tokenize input text
inputs = tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True).to(device)
#st.write(inputs.shape)
# Generate summary
summary_ids = model.generate(inputs, num_beams=4, max_length=264, early_stopping=True)
summary = tokenizer.decode(summary_ids.squeeze(), skip_special_tokens=True)
return summary
# Streamlit app
def main():
st.title("Text Summarization")
# User input
user_input = st.text_area("Enter the text to summarize")
# Generate summary button
if st.button("Generate Summary"):
if user_input.strip() == "":
st.warning("Please enter some text.")
else:
# Generate summary
summary = generate_summary(user_input)
# Display summary
st.subheader("Summary")
st.write(summary)
if __name__ == "__main__":
main()
|