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import gradio as gr | |
import pandas as pd | |
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer | |
from sklearn.model_selection import train_test_split | |
from sklearn.svm import SVR | |
data = pd.read_csv("modeled_data.csv") | |
analyzer = SentimentIntensityAnalyzer() | |
def sample_model(df, regressor): | |
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) | |
model = regressor | |
model.fit(X_train, y_train) | |
return model | |
def calculate_sentiments(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) | |
def take_input(comment): | |
cons_tuned_svr = sample_model(data, SVR(C=3, kernel="rbf", tol=0.001)) | |
return calculate_sentiments(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? | |
""") | |
input_comment = gr.Textbox(placeholder="Write your comment here...", show_label = False, lines=2) | |
button = gr.Button("What is the Rating I Have Given? Click me to Learn", variant="secondary").style(full_width=True) | |
with gr.Row(): | |
with gr.Column(): | |
gr.Markdown("#### Generated Rating from Your Comment") | |
rating = gr.Number().style(show_label=False) | |
with gr.Column(): | |
gr.Markdown("#### Sentiment Scores of Your Comment") | |
with gr.Row(): | |
negscore = gr.Number(label="Negativity Score") | |
neuscore = gr.Number(label="Neutrality Score") | |
posscore = gr.Number(label="Positivity Score") | |
compscore = gr.Number(label="Compound Score") | |
gr.Markdown("Example comments has taken from https://www.udemy.com/course/statistics-for-data-science-and-business-analysis/") | |
gr.Examples( | |
[["Not really a basic course. Goes too fast and sometimes the explanations aren't clear. The solutions to the exercises are not explained, and should be. Not much better than the other course I started and didn't finish.(3)"], | |
["Presentation style was bad. Too much detail on the simpler topics, and glossed over some of the more complicated ones.(2)"], | |
["This course is the worst Course i have ever watched (1)"], | |
["The course is really great! The didatic in explain all concepts and pratical examples are amazing. Better than brazilian universities.(4)"], | |
["The best!!!!!!!!!!(5)"], | |
["Excelent description and view. The exercises were prepared very carefully. I suggest it.(5)"], | |
["Yes, This course is a very good way to update/ refresh statistical knowledge (2)"], | |
["explaination is good but practical examples are not so good (2.5)"], | |
["Thanks for content. Good to know and understand the things easily. (3.5)"], | |
["good (3)"], | |
["The course navigation is very bad ..It is very tedious to navigate the course.(5)"]], | |
[input_comment], | |
[[negscore, neuscore, posscore, compscore, rating]], | |
fn=take_input | |
) | |
button.click(fn=take_input, inputs=input_comment, outputs=[negscore, neuscore, posscore, compscore, rating]) | |
demo.launch() |