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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() | |