Update app.py

app.py

@@ -12,22 +12,21 @@ data = {
     'scores' :[[], [0.16746170342156613,0.15000902432939608,0.0793086259849024,0.0642684614359449,0.05274725840837433,0.051507427048382876,0.047404471182744455,0.047404471182744455,0.03655408024186657,0.035427310538133555]]
 }
 data2 = {
+    'Topic' : ["Topic2", "Topic2"]
     'Article' : ["1", "2"],
     'Link' : ['https://www.google.fr', 'https://www.google.fr']
 }
 df = pd.DataFrame(data)
 topics = df['Topic'].unique()
+df2 = pd.DataFrame(data2)
 
 def display_topics(topic):
     # Filter DataFrame based on the selected topic
     selected_data = df[df['Topic'] == topic]
+    selected_data2 = df2[df2['Topic'] == topic]
     # Display relevant articles
-    #articles = selected_data['Article'] 
-    #links = selected_data['Link']
-    articles = data2["Article"]
-    links = data2["Link"]
-    print(links)
-    print(articles)
+    articles = selected_data2['Article'] 
+    links = selected_data2['Link']
     nb_art = min(4, len(links))
     articles_ret = """## Most relevant articles  
       
@@ -35,15 +34,10 @@ def display_topics(topic):
     for i in range(nb_art):
         articles_ret += f""" * [{articles[i]}]({links[i]})  
     """
-    print(articles_ret)
-
     # Generate word cloud for keywords
     keywords = selected_data['keywords'][1]
-    print(keywords)
     freq = selected_data["scores"][1]
     keywords_wordcloud = dict()
-    print(freq)
-    print(keywords)
     for i, elem in enumerate(keywords):
         keywords_wordcloud[elem] = freq[i]
     wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(keywords_wordcloud)