update

app.py

@@ -38,8 +38,8 @@ print(f"sentiment_toi length is {len(sentiment_toi)}")
 print(f"all_articles_toi length is {len(all_articles_toi['articles'])}")
 
 
-#Driver 
-def inference(newssource): #, date):
+#Driver positive
+def inference_pos(newssource): #, date):
   
   if newssource == "Times Of India":
     sentiment = sentiment_toi
@@ -54,7 +54,7 @@ def inference(newssource): #, date):
   url = ["<a href=" + entry['url'] + ' target="_blank">Click here for the original news article</a>' for entry in all_articles['articles']] 
   urlToImage = ["<img src= " + entry['urlToImage']+">" for entry in all_articles['articles']]
   
-  print("********************************************************")
+  print("********************* Positive News **************************")
   print(f"Newssource is - {newssource}")
   print(f"description length is - {len(description)}")
   print(f"content length is - {len(content)}")
@@ -65,20 +65,70 @@ def inference(newssource): #, date):
   dictnews = { 'description' : description, 'content' : content, 'url' : url, 'urlToImage' : urlToImage, 'sentiment' : sentiment}
 
   df  = pd.DataFrame.from_dict(dictnews)
-  #print("************** df start ****************")
-  #print(df)
-  #print(f"description : {description}")
-  print(f"content : {content}")
-  print(f"url : {url}")
-  print(f"urlToIm : {urlToImage}")
-  print(f"sentiment : {sentiment}")
+  df = df.loc[df['sentiment'] == 'Positive']
+
+  print(f"dataframe shape is :,{df.shape}")
+  return df
+
+#Driver - negative
+def inference_neg(newssource): #, date):
+  
+  if newssource == "Times Of India":
+    sentiment = sentiment_toi
+    all_articles = all_articles_toi
+  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 = ["<a href=" + entry['url'] + ' target="_blank">Click here for the original news article</a>' for entry in all_articles['articles']] 
+  urlToImage = ["<img src= " + entry['urlToImage']+">" for entry in all_articles['articles']]
+  
+  print("********************* Negative News ***********************")
+  print(f"Newssource is - {newssource}")
+  print(f"description length is - {len(description)}")
+  print(f"content length is - {len(content)}")
+  print(f"url length is - {len(url)}")
+  print(f"urlToImage length is - {len(urlToImage)}")
+  print(f"sentiment length is - {len(sentiment)}")
+  
+  dictnews = { 'description' : description, 'content' : content, 'url' : url, 'urlToImage' : urlToImage, 'sentiment' : sentiment}
+
+  df = pd.DataFrame.from_dict(dictnews)
+  df = df.loc[df['sentiment'] == 'Negative']
+  print(f"dataframe shape is :,{df.shape}")
+  return df 
+
+#Driver - neutral
+def inference_neut(newssource): #, date):
+  
+  if newssource == "Times Of India":
+    sentiment = sentiment_toi
+    all_articles = all_articles_toi
+  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 = ["<a href=" + entry['url'] + ' target="_blank">Click here for the original news article</a>' for entry in all_articles['articles']] 
+  urlToImage = ["<img src= " + entry['urlToImage']+">" for entry in all_articles['articles']]
+  
+  print("********************* Neutral News ***********************")
+  print(f"Newssource is - {newssource}")
+  print(f"description length is - {len(description)}")
+  print(f"content length is - {len(content)}")
+  print(f"url length is - {len(url)}")
+  print(f"urlToImage length is - {len(urlToImage)}")
+  print(f"sentiment length is - {len(sentiment)}")
   
-  print("************** df end ****************")
+  dictnews = { 'description' : description, 'content' : content, 'url' : url, 'urlToImage' : urlToImage, 'sentiment' : sentiment}
+
+  df = pd.DataFrame.from_dict(dictnews)
+  df = df.loc[df['sentiment'] == 'Neutral']
   print(f"dataframe shape is :,{df.shape}")
-  if dictnews['urlToImage'][0] is not None :
-    html_out = "<img src= " + dictnews['urlToImage'][0] + ">"
-    print(f"html_out is : {html_out}")
-  return df #, html_out
+  return df 
 
 
 #Gradio Blocks
@@ -97,8 +147,8 @@ with gr.Blocks() as demo:
     #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) #, out_news])
-  b2.click(fn=inference, inputs=in_newssource, outputs=out_dataframe) #, out_news])
+  b1.click(fn=inference_pos, inputs=in_newssource, outputs=out_dataframe) #, out_news])
+  b2.click(fn=inference_neg, inputs=in_newssource, outputs=out_dataframe) #, out_news])
+  b3.click(fn=inference_neut, inputs=in_newssource, outputs=out_dataframe) #, out_news])
   
 demo.launch(debug=True, show_error=True)