Modified app.py Replaced AWS Secretes Manager code. Used Hugging Face Secrets variable

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import requests
+import json
+from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
+import os
+
+def get_api():
+ api_key = os.getenv("NYT_ARTICLE_API")
+ if api_key is None:
+ raise ValueError("NYT_ARTICLE_API environment variable not set.")
+ return api_key
+
+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(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()