update

app.py

@@ -6,23 +6,49 @@ from newsapi import NewsApiClient
 from datetime import date, timedelta
 from transformers import pipeline
 
-#Model 2: Sentence Transformer
 HF_TOKEN = os.environ["newsapi"]
-#headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+# Initialization
+newsapi = NewsApiClient(api_key=HF_TOKEN)
 
 classifier = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment") 
 sentiment = ['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']]
-# Initialization
-newsapi = NewsApiClient(api_key=HF_TOKEN)
 
-# /v2/everything
-all_articles = newsapi.get_everything(#q='bitcoin',
-                                      sources='the-times-of-india',
+#Driver 
+def inference(newssource): #, date):
+  today = str(date.today())
+  all_articles = newsapi.get_everything(sources='the-times-of-india',
                                       domains='timesofindia.indiatimes.com',
-                                      from_param='2022-10-30',
-                                      to='2022-10-30',
+                                      from_param=today,
+                                      to=today,
                                       language='en',
                                       sort_by='relevancy',)
-                                      #page=2)
-all_articles['articles']
- 
+  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
+
+
+#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_date = gr.Textbox(visible = False, value = today)
+
+  with gr.Row():
+    b1 = gr.Button("Get Positive News")
+    b2 = gr.Button("Get Negative News")
+
+  with gr.Row():
+    #sample
+    out_news = gr.HTML(label="First News Link", show_label=True) 
+    out_dataframe = gr.Dataframe(wrap=True, datatype = ["str", "str", "markdown", "markdown", "str"])
+    
+  b1.click(fn=inference, inputs=in_newssource, outputs=[out_dataframe, out_news])
+  b2.click(fn=inference, inputs=in_newssource, outputs=out_dataframe)
+
+demo.launch(debug=True, show_error=True)