app.py

import gradio as gr
import joblib

# Load model and vectorizer
model = joblib.load("sentiment_model.pkl")
tfidf = joblib.load("tfidf_vectorizer.pkl")

def predict_sentiment(text):
    if not text.strip():
        return "Please enter some text."
    
    # Transform the text using the same TF-IDF vectorizer
    vector = tfidf.transform([text])
    
    # Predict sentiment
    prediction = model.predict(vector)[0]
    
    # Optional: make prediction readable
    if prediction == 1:
        label = "😊 Positive"
    
    elif prediction == 0 :
        label = "😠 Negative"
    
        
    
    
    return label

# Gradio Interface
iface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
    outputs="text",
    title="Sentiment Classifier",
    description="Predicts whether a sentence is positive, neutral, or negative using an XGBoost model."
)

iface.launch()