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import gradio as gr
import boto3
from botocore.exceptions import ClientError
import requests
import json
from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
def get_secret():
secret_name = "NYTimesArticleAPI"
region_name = "ap-south-1"
session = boto3.session.Session()
client = session.client(
service_name='secretsmanager',
region_name=region_name
)
try:
get_secret_value_response = client.get_secret_value(
SecretId=secret_name
)
except ClientError as e:
raise e
secret = get_secret_value_response['SecretString']
secret_dict = json.loads(secret)
return secret_dict
def get_api():
api_key_dict = get_secret()
api_key_value = api_key_dict['ny_times_article_api']
return api_key_value
def get_abstracts(query):
api_key = get_api()
url = f'https://api.nytimes.com/svc/search/v2/articlesearch.json?q={query}&fq=source:("The New York Times")&api-key={api_key}'
response = requests.get(url).json()
abstracts = []
docs = response.get('response', {}).get('docs', [])
for doc in docs:
abstract = doc.get('abstract', '')
if abstract:
abstracts.append(abstract)
return abstracts
def summarizer(query):
abstracts = get_abstracts(query)
input_text = ' '.join(abstracts)
tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_billsum_model")
inputs = tokenizer(input_text, return_tensors="tf").input_ids
model = TFAutoModelForSeq2SeqLM.from_pretrained("stevhliu/my_awesome_billsum_model", from_pt=True)
outputs = model.generate(inputs, max_length=100, do_sample=False)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
return abstracts, summary
iface = gr.Interface(
fn=summarizer,
inputs=gr.inputs.Textbox(placeholder="Enter your query"),
# outputs=gr.outputs.Textbox(),
outputs=[
gr.outputs.Textbox(label="Abstracts"),
gr.outputs.Textbox(label="Summary")
],
title="New York Times Articles Summarizer",
description="This summarizer actually does not yet summarize New York Times articles because of certain limitations. Type in something like 'Manipur' or 'Novak Djokovic' you will get a summary of that topic. What actually happens is that the query goes through the API. The abstract of article's content is added or concatenated, and then a text of considerable length is generated. That text is then summarized. So, this is an article summarizer but summarizes only abstracts of a particular article, ensuring that readers get the essence of a topic. This is a successful implementation of a pretrained T5 Transformer model."
)
if __name__ == "__main__":
iface.launch()
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