Update app.py

app.py

@@ -1,8 +1,64 @@
 import gradio as gr
+import pandas as pd
+import numpy as np
+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")
+
+def sample_model(df, regressor, scale=None):
+    X = df.drop("rate",axis=1)
+    y = df["rate"]
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=1)
+    scaled_X_train, scaled_X_test = X_train, X_test
+    if scale != None:
+        scaler = scale
+        scaled_X_train = pd.DataFrame(scaler.fit_transform(X_train), columns = X_train.columns)
+        scaled_X_test = pd.DataFrame(scaler.transform(X_test),columns = X_test.columns)
+    
+    model = regressor
+    model.fit(scaled_X_train, y_train)
+    y_pred = model.predict(scaled_X_test)
+    
+    rmse = np.sqrt(mean_squared_error(y_test, y_pred))
+    
+    return model, scaled_X_train, scaled_X_test, y_train, y_test 
+
+
+def user_interaction(comment, model):
+    
+    negative_score = analyzer.polarity_scores(comment)["neg"]
+    neutral_score = analyzer.polarity_scores(comment)["neu"]
+    positive_score = analyzer.polarity_scores(comment)["pos"]
+    compound_score = analyzer.polarity_scores(comment)["compound"]
+    rate_pred = model.predict([[negative_score, neutral_score, positive_score, compound_score]])
     
+    print(f"\nYour Comment: {comment}\n")
+    print("*"*10 + "Analysis of the Comment" + "*"*10)
+    print("-"*10 + f"Negativity Score: {negative_score:.2f}" + "-"*10)
+    print("-"*10 + f"Neutrality Score: {neutral_score:.2f}" + "-"*10)
+    print("-"*10 + f"Positivity Score: {positive_score:.2f}" + "-"*10)
+    print("-"*10 + f"Compound Score: {compound_score:.2f}" + "-"*10)
+    print("*"*43)
+    print("\nThe estimated rating this comment can give")
+    print("*"*20 + str(round(rate_pred[0], 2)) + "*"*20)
+
+
+def take_input(model):
+    comment = input("Thanks for your interest and taking your time.\n"+
+                    "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")
+    if (detect(comment) != "en") or (len(comment) < 20):
+        print("Sorry, your comment does not meet the requirements.\n")
+        take_input(model)
+    else:
+        user_interaction(comment, model)
+
+cons_tuned_svr, _, _, _, _ = sample_model(data, SVR(C=3, kernel="rbf", tol=0.001))
+
+
 
-with gr.Blocks() as user:
-    gr.Markdown("# AIN311 Project P05 - MOOC Recommendation")
-    print("Hello")
-iface = gr.Interface(fn=user, inputs="text", outputs="text")
+iface = gr.Interface(fn=take_input(cons_tuned_svr), inputs="text", outputs="text")
 iface.launch()