Update comment_analyzer.py

comment_analyzer.py

@@ -1,20 +1,17 @@
 import gradio as gr
 import pandas as pd
 import numpy as np
-
-#pip install langdetect
 from langdetect import detect
-
-#pip install vaderSentiment
 from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-
 from sklearn.model_selection import train_test_split
 from sklearn.svm import SVR
 from sklearn.metrics import mean_squared_error
 
+
 data = pd.read_csv("modeled_data.csv")
 analyzer = SentimentIntensityAnalyzer()
 
+
 def sample_model(df, regressor, scale=None):
     X = df.drop("rate",axis=1)
     y = df["rate"]
@@ -44,26 +41,14 @@ def user_interaction(comment, model):
     rate_pred = model.predict([[negative_score, neutral_score, positive_score, compound_score]])
     
     return round(negative_score,2), round(neutral_score,2), round(positive_score,2), round(compound_score,2), round(rate_pred[0],2)
-    """return (f"\nYour Comment: {comment}\n" + 
-           "*"*10 + "Analysis of the Comment" + "*"*10 + "\n" +
-           "-"*10 + f"Negativity Score: {negative_score:.2f}" + "-"*10 + "\n" +
-           "-"*10 + f"Neutrality Score: {neutral_score:.2f}" + "-"*10 + "\n" +
-           "-"*10 + f"Positivity Score: {positive_score:.2f}" + "-"*10 + "\n" +
-           "-"*10 + f"Compound Score: {compound_score:.2f}" + "-"*10 + "\n" +
-           "*"*43 + "\n"), ("\nThe estimated rating this comment can give" + "\n" + 
-           "*"*20 + str(round(rate_pred[0], 2)) + "*"*20 + "\n")"""
-
 
+    
 def take_input(comment):
-    #if (detect(comment) != "en") or (len(comment) < 20):
-     #   return "Sorry, your comment does not meet the requirements.\n", "Please check your comment"
-    #else:
+    
     cons_tuned_svr, _, _, _, _ = sample_model(data, SVR(C=3, kernel="rbf", tol=0.001))
     return user_interaction(comment, cons_tuned_svr)
 
 
-
-
 with gr.Blocks() as demo:
     gr.Markdown("# AIN311 Project P05 - MOOC Recommendation")
     gr.Markdown("## Generating a Rating from User Comment")
@@ -75,12 +60,14 @@ with gr.Blocks() as demo:
                     """)
         input_comment = gr.Textbox(placeholder="Write your comment here...")
         button = gr.Button("What is the Rating I Gave? Click me to Learn")
+        
         gr.Markdown("#### Sentiment Scores of Your Comment")
         negscore = gr.Number(label="Negativity Score")
         neuscore = gr.Number(label="Neutrality Score")
         posscore = gr.Number(label="Positivity Score")
         compscore = gr.Number(label="Compound Score")
-        rating = gr.Number(label="Generated Rating from Your Comment")        
+        rating = gr.Number(label="Generated Rating from Your Comment") 
+        
     button.click(fn=take_input, inputs=input_comment, outputs=[negscore, neuscore, posscore, compscore, rating])
 
 demo.launch()