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()