Delete app.py

app.py

@@ -1,69 +0,0 @@
-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()