Spaces:
Sleeping
Sleeping
File size: 2,613 Bytes
476b9b5 6037530 476b9b5 4f93309 476b9b5 5fc6e34 476b9b5 4f93309 476b9b5 4f93309 476b9b5 4f93309 476b9b5 4f93309 476b9b5 4f93309 |
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 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import os
import gradio as gr
import requests
from newspaper import Article
import time
from langdetect import detect
import nltk
# Download the 'punkt' tokenizer
nltk.download('punkt')
# Load environment variables from .env file
API_KEY = os.getenv('API_KEY')
print(f"API_KEY: {'Loaded' if API_KEY else 'Not Loaded'}")
# Ensure the API key is loaded
if not API_KEY:
raise ValueError("API_KEY is missing. Please set it in the secret variables in the Hugging Face Spaces settings.")
# Define the Hugging Face API URL
API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
# Set up headers for the API request
headers = {"Authorization": f"Bearer {API_KEY}"}
# Function to query the Hugging Face API
def query(payload):
for _ in range(5): # Try 5 times
response = requests.post(API_URL, headers=headers, json=payload)
result = response.json()
if 'error' in result and 'currently loading' in result['error']:
time.sleep(5) # Wait for 5 seconds before retrying
else:
return result
return {"error": "Model is still loading. Please try again later."}
# Function to summarize text
def summarize(text, minL=20, maxL=300):
output = query({
"inputs": text,
"parameters": {
"min_length": minL,
"max_length": maxL
}
})
if "error" in output:
return f"Error: {output['error']}"
if not isinstance(output, list) or not output:
return "Error: Unexpected response format."
if "summary_text" not in output[0]:
return "Error: 'summary_text' key not found in the response."
return output[0]['summary_text']
def Choices(choice, input_text, minL, maxL):
if choice == "URL":
try:
article = Article(input_text, language="en")
article.download()
article.parse()
article.nlp()
text = article.text
except Exception as e:
return f"Error: Unable to fetch article. {str(e)}"
else:
text = input_text
return summarize(text, minL, maxL)
# Create Gradio interface
demo = gr.Interface(
fn=Choices,
inputs=[
gr.Radio(choices=["URL", "Paragraph"], label="Input Type", value="URL"),
gr.Textbox(lines=10, placeholder="Enter text here..."),
gr.Number(value=20, label="Minimum Length"),
gr.Number(value=300, label="Maximum Length"),
],
outputs="textbox",
title="Text Summarizer",
description="Enter text to summarize using Hugging Face BART model."
)
# Launch the interface
demo.launch()
|