Update app.py

app.py

@@ -1,11 +1,19 @@
 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)
@@ -58,10 +66,16 @@ cons_tuned_svr, _, _, _, _ = sample_model(data, SVR(C=3, kernel="rbf", tol=0.001
 with gr.Blocks() as demo:
     gr.Markdown("# AIN311 Project P05 - MOOC Recommendation")
     gr.Markdown("## Generating a Rating from User Comment")
+    """
+    # Hello World!
+    Start typing below to see the output.
+    """
     with gr.Column():
-        gr.Markdown("### Thanks for your interest and taking your time." +
-                                   "#### Tell us about your personal experience enrolling in this course. Was it the right match for you?\n"+
-                                   "#### (Note: Comment should be written in English and be longer than 20 characters)\n")
+        gr.Markdown("""
+                    ### Thanks for your interest and taking your time.
+                    #### Tell us about your personal experience enrolling in this course. Was it the right match for you?
+                    #### (Note: Comment should be written in English and be longer than 20 characters)
+                    """)
         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")