bugfix

app.py

@@ -112,30 +112,30 @@ def main(button, choose_context):
     df_topic_keywords["Topics"] = topics
     df_topic_keywords
 
-    # # Define function to predict topic for a given text document.
-    # nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])
-    # def predict_topic(text, nlp=nlp):
-    #     global sent_to_words
-    #     global lemmatization
-    #     # Step 1: Clean with simple_preprocess
-    #     mytext_2 = list(sent_to_words(text))
-    #     # Step 2: Lemmatize
-    #     mytext_3 = lemmatization(mytext_2, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])
-    #     # Step 3: Vectorize transform
-    #     mytext_4 = vectorizer.transform(mytext_3)
-    #     # Step 4: LDA Transform
-    #     topic_probability_scores = best_lda_model.transform(mytext_4)
-    #     topic = df_topic_keywords.iloc[np.argmax(topic_probability_scores), 1:14].values.tolist()
+    # Define function to predict topic for a given text document.
+    nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])
+    def predict_topic(text, nlp=nlp):
+        global sent_to_words
+        global lemmatization
+        # Step 1: Clean with simple_preprocess
+        mytext_2 = list(sent_to_words(text))
+        # Step 2: Lemmatize
+        mytext_3 = lemmatization(mytext_2, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])
+        # Step 3: Vectorize transform
+        mytext_4 = vectorizer.transform(mytext_3)
+        # Step 4: LDA Transform
+        topic_probability_scores = best_lda_model.transform(mytext_4)
+        topic = df_topic_keywords.iloc[np.argmax(topic_probability_scores), 1:14].values.tolist()
         
-    #     # Step 5: Infer Topic
-    #     infer_topic = df_topic_keywords.iloc[np.argmax(topic_probability_scores), -1]
+        # Step 5: Infer Topic
+        infer_topic = df_topic_keywords.iloc[np.argmax(topic_probability_scores), -1]
         
-    #     #topic_guess = df_topic_keywords.iloc[np.argmax(topic_probability_scores), Topics]
-    #     return infer_topic, topic, topic_probability_scores
+        #topic_guess = df_topic_keywords.iloc[np.argmax(topic_probability_scores), Topics]
+        return infer_topic, topic, topic_probability_scores
 
-    # # Predict the topic
-    # mytext = ["This is a test of a random topic where I talk about politics"]
-    # infer_topic, topic, prob_scores = predict_topic(text = mytext)
+    # Predict the topic
+    mytext = ["This is a test of a random topic where I talk about politics"]
+    infer_topic, topic, prob_scores = predict_topic(text = mytext)
 
     def apply_predict_topic(text):
         text = [text]
@@ -193,8 +193,8 @@ with gr.Blocks() as demo:
         choices=['comment', 'sup comment', 'sup comment + comment'], value='sup comment'
     )
     plot = gr.Plot(label="Plot")
-    button.change(main, inputs=[button, choose_context], outputs=[plot])
-    demo.load(main, inputs=[button], outputs=[plot])
+    button.change(main, inputs=[choose_context], outputs=[plot])
+    demo.load(main, inputs=[choose_context], outputs=[plot])
 
 
 # iface = gr.Interface(fn=greet, inputs="text", outputs="text")