import gradio as gr
import pandas as pd
import numpy as np
from langdetect import detect
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)

    
def take_input(comment):
    
    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?
                    """)
        input_comment = gr.Textbox(placeholder="Write your comment here...", show_label = False)
        button = gr.Button("What is the Rating I Have Given? Click me to Learn")
        with gr.Row():
            gr.Markdown("#### Generated Rating from Your Comment")
            rating = gr.Number()
            with gr.Column():
                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")
        
    button.click(fn=take_input, inputs=input_comment, outputs=[negscore, neuscore, posscore, compscore, rating])

demo.launch()