update

app.py

@@ -17,6 +17,12 @@ today = str(date.today())
 print("******** Outside Inference function ********")
 print(f"HF_TOKEN is - {HF_TOKEN}")
 
+#top-headlines
+all_top_headlines = newsapi.get_top_headlines(country='in')
+sentiment_tophead = ['Negative' if classifier(entry['content'])[0]['label'] == 'LABEL_0' else 'Neutral' if classifier(entry['content'])[0]['label'] == 'LABEL_1' else 'Positive' for entry in all_top_headlines['articles']]
+print(f"sentiment_tophead length is {len(sentiment_tophead)}")
+print(f"all_top_headlines length is {len(all_top_headlines['articles'])}")
+
 #times of india
 all_articles_toi = newsapi.get_everything(sources='the-times-of-india',
                                     domains= 'http://timesofindia.indiatimes.com', #'timesofindia.indiatimes.com',
@@ -28,27 +34,6 @@ sentiment_toi = ['Negative' if classifier(entry['content'])[0]['label'] == 'LABE
 print(f"sentiment_toi length is {len(sentiment_toi)}")
 print(f"all_articles_toi length is {len(all_articles_toi['articles'])}")
 
-#the hindu
-all_articles_hindu = newsapi.get_everything(sources='the-hindu',
-                                    domains= 'http://www.thehindu.com', #'thehindu.com',
-                                    from_param=today,
-                                    to=today,
-                                    language='en',
-                                    sort_by='relevancy',)
-sentiment_hindu = ['Negative' if classifier(entry['content'])[0]['label'] == 'LABEL_0' else 'Neutral' if classifier(entry['content'])[0]['label'] == 'LABEL_1' else 'Positive' for entry in all_articles_hindu['articles']]
-print(f"sentiment_hindu length is {len(sentiment_hindu)}")
-print(f"all_articles_hindu length is {len(all_articles_hindu['articles'])}")
-
-#google news
-all_articles_google = newsapi.get_everything(sources='google-news-in',
-                                    domains = 'https://news.google.com', #'news.google.com',
-                                    from_param=today,
-                                    to=today,
-                                    language='en',
-                                    sort_by='relevancy',)
-sentiment_google = ['Negative' if classifier(entry['content'])[0]['label'] == 'LABEL_0' else 'Neutral' if classifier(entry['content'])[0]['label'] == 'LABEL_1' else 'Positive' for entry in all_articles_google['articles']]
-print(f"sentiment_google length is {len(sentiment_google)}")
-print(f"all_articles_google length is {len(all_articles_google['articles'])}")
 
 #Driver 
 def inference(newssource): #, date):
@@ -56,13 +41,10 @@ def inference(newssource): #, date):
   if newssource == "Times Of India":
     sentiment = sentiment_toi
     all_articles = all_articles_toi
-  elif newssource == "The Hindu":
-    sentiment = sentiment_hindu
-    all_articles = all_articles_hindu
-  elif newssource == "Google News":
-    sentiment = sentiment_google
-    all_articles = all_articles_google
- 
+  elif newssource == "Top Headlines":
+    sentiment = sentiment_tophead
+    all_articles = all_top_headlines
+  
   description = [entry['description'] for entry in all_articles['articles']]
   content = [entry['content'] for entry in all_articles['articles']]
   url = [entry['url'] for entry in all_articles['articles']]
@@ -91,7 +73,7 @@ def inference(newssource): #, date):
 #Gradio Blocks
 with gr.Blocks() as demo:
   with gr.Row():
-    in_newssource =  gr.Dropdown(["Google News", "The Hindu", "Times Of India"], label='Choose a News Outlet')
+    in_newssource =  gr.Dropdown(["Times Of India", "Top Headlines"], label='Choose a News Outlet')
     #in_date = gr.Textbox(visible = False, value = today)
 
   with gr.Row():