import pandas as pd
import gradio as gr
from textblob import TextBlob
import re

def preprocess_text(text):
    if not isinstance(text, str):
        return ""
    text = text.lower()
    text = re.sub(r'http\S+|www\S+|https\S+', '', text, flags=re.MULTILINE)
    text = re.sub(r'[^\w\s]', '', text)
    return text.strip()

def predict_sentiment(user_input):
    cleaned_text = preprocess_text(user_input)
    if not cleaned_text:
        return "Please enter actual text"
    
    # TextBlob analysis
    blob = TextBlob(cleaned_text)
    polarity = blob.sentiment.polarity
    
    # Classification based on polarity
    if polarity > 0.1:
        return "Positive"
    elif polarity < -0.1:
        return "Negative"
    else:
        return "Neutral"

def get_dataset_info():
    try:
        df = pd.read_csv('sentiment_analysis.csv')
        summary = f"Dataset loaded! Total rows: {len(df)}. Columns: {', '.join(df.columns)}"
        return summary
    except:
        return "error."

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    with gr.Row():
        with gr.Column():
            input_text = gr.Textbox(
                label="Input Text", 
                placeholder="Type anything",
                lines=4
            )
            submit_btn = gr.Button("Analyze Setiment")
        
        with gr.Column():
            output_label = gr.Label(label="Predicted result")

    submit_btn.click(fn=predict_sentiment, inputs=input_text, outputs=output_label)
    
if __name__ == "__main__":
    demo.launch()