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import streamlit as st
import json
import requests
import os
API_TOKEN = os.environ.get("API_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://api-inference.huggingface.co/models/sabre-code/pegasus-large-cnn-dailymail"
def query(payload):
data = json.dumps(payload)
response = requests.request("POST", API_URL, headers=headers, data=data)
return json.loads(response.content.decode("utf-8"))
st.set_page_config(layout='wide')
st.title("Text Summarisation App PEGASUS-large")
st.subheader('Input text below to be summarised', divider='rainbow')
# Create a text input widget
text_input = st.text_area(label="Input Text", height=200)
generated_summary = ""
# Define a function to generate the summary
def generate_summary(text):
def query(payload):
data = json.dumps(payload)
response = requests.request("POST", API_URL, headers=headers, data=data)
return json.loads(response.content.decode("utf-8"))
data = query({"inputs": text})
# Return the generated summary
return data
# Add a button to trigger the generation of the summary
generate_button = st.button(label="Generate Summary")
if generate_button:
# Call the generate_summary function when the button is clicked
generated_summary = generate_summary(text_input)
#st.success("Summary Generated!")
# Display the generated summary
st.markdown("## Summary")
st.success(generated_summary) |