Spaces:
Runtime error
Runtime error
File size: 3,754 Bytes
5558e45 da2c9bd 14637b5 78e167d 14637b5 78e167d 14637b5 da2c9bd 14637b5 da2c9bd beba0cd 4df79e9 3da5b25 2ede8ce 4df79e9 da2c9bd fd0291a 732a250 fd0291a da2c9bd 3c9ea56 ab2e1ab ef0c21a ab2e1ab 14637b5 fc0ac32 14637b5 fc0ac32 ab2e1ab 4df79e9 fc0ac32 3c9ea56 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
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() |