update

app.py

@@ -17,32 +17,33 @@ today = str(date.today())
 print(f"HF_TOKEN is - {HF_TOKEN}")
 
 #times of india
-all_articles = newsapi.get_everything(sources='the-times-of-india',
+all_articles_toi = newsapi.get_everything(sources='the-times-of-india',
                                     domains='timesofindia.indiatimes.com',
                                     from_param=today,
                                     to=today,
                                     language='en',
                                     sort_by='relevancy',)
-sentiment_toi = ['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['articles']]
+sentiment_toi = ['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_toi['articles']]
 print(f"sentiment_toi length is {len(sentiment_toi)}")
+
 #the hindu
-all_articles = newsapi.get_everything(sources='the-hindu',
+all_articles_hindu = newsapi.get_everything(sources='the-hindu',
                                     domains='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['articles']]
-print(f"sentiment_toi length is {len(sentiment_hindu)}")
+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)}")
 
 #google news
-all_articles = newsapi.get_everything(sources='google-news-in',
+all_articles_google = newsapi.get_everything(sources='google-news-in',
                                     domains='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['articles']]
+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)}")
 
 #Driver 
@@ -50,10 +51,13 @@ 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
  
   description = [entry['description'] for entry in all_articles['articles']]
   content = [entry['content'] for entry in all_articles['articles']]
@@ -66,12 +70,13 @@ def inference(newssource): #, date):
   print(f"urlToImage length is {len(urlToImage)}")
   print(f"sentiment length is {len(sentiment)}")
   
-  dictnews = { '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']],
-            'urlToImage' : [entry['urlToImage'] for entry in all_articles['articles']],
-            'sentiment' : sentiment,
-            }
+  dictnews = { 'description' : description, 'content' : content, 'url' : url, 'urlToImage' : urlToImage, 'sentiment' : sentiment,}
+  #dictnews = { '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']],
+  #          'urlToImage' : [entry['urlToImage'] for entry in all_articles['articles']],
+  #          'sentiment' : sentiment,
+  #          }
   df  = pd.DataFrame.from_dict(dictnews)
   html_out = "<img src= " + dictnews['urlToImage'][0] + ">"
   return df, html_out