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| 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"] | |
| 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]]) | |
| 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") | |
| 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? | |
| ##### (Note: Comment should be written in English and be longer than 20 characters) | |
| """) | |
| 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") | |
| button.click(fn=take_input, inputs=input_comment, outputs=[negscore, neuscore, posscore, compscore, rating]) | |
| demo.launch() |