File size: 1,681 Bytes
dca9040 e1d8f0e baaa1a2 dca9040 baaa1a2 02c107a e1d8f0e 02c107a 27909e7 e1d8f0e 02c107a baaa1a2 02c107a baaa1a2 dca9040 baaa1a2 |
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 46 47 48 49 50 51 52 53 54 |
# import gdown
# import torch
# # https://drive.google.com/drive/folders/17G-ejd4scK1DYko5k0ssXZjEy7P-ClI6
# import streamlit as st
# import gdown
# import torch
# from transformers import pipeline
# # Function to download the model from Google Drive
# def download_file_from_drive(file_id, output_path):
# url = f'https://drive.google.com/uc?id={file_id}'
# gdown.download(url, output_path, quiet=False)
# # Replace 'YOUR_FILE_ID' with the actual file ID of your model
# file_id = '1A2B3C4D5E6F7G8H9I0J'
import streamlit as st
import requests
import torch
from transformers import pipeline
from transformers import T5ForConditionalGeneration, T5Tokenizer
# Replace with your Hugging Face model repository path
model_repo_path = 'AbdurRehman313/T5_samsum_model_files'
# Load the model and tokenizer
model = T5ForConditionalGeneration.from_pretrained(model_repo_path)
tokenizer = T5Tokenizer.from_pretrained(model_repo_path)
# Initialize the summarization pipeline
summarizer = pipeline('summarization', model=model,tokenizer=tokenizer)
# Streamlit app layout
st.title("Text Summarization App")
# User input
text_input = st.text_area("Enter text to summarize", height=300)
# Summarize the text
if st.button("Summarize"):
if text_input:
with st.spinner("Generating summary..."):
try:
summary = summarizer(text_input, max_length=150, min_length=30, do_sample=False)
st.subheader("Summary")
st.write(summary[0]['summary_text'])
except Exception as e:
st.error(f"Error during summarization: {e}")
else:
st.warning("Please enter some text to summarize.") |