app.py

# =====================================================
# 🌐 Multi-lingual Sentiment Analyzer (English + Persian)
# =====================================================

import gradio as gr
import joblib
import numpy as np
import shap
import os

# -----------------------------------------------------
# ✅ Load Models and Vectorizers
# -----------------------------------------------------
english_model = joblib.load("best_model_english")
english_vectorizer = joblib.load("tfidf_vectorizer_english")

persian_model = joblib.load("best_model_persian")
persian_vectorizer = joblib.load("tfidf_vectorizer_persian")


# -----------------------------------------------------
# ✅ Label mapping
# -----------------------------------------------------
label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}

# -----------------------------------------------------
# ✅ Prediction Function
# -----------------------------------------------------
def predict_sentiment(text, lang):
    if not text.strip():
        return "⚠️ Please enter some text.", "", "", ""
    
    # Select appropriate model and vectorizer
    if lang == "English":
        vectorizer = eng_vectorizer
        model = eng_model
    else:
        vectorizer = per_vectorizer
        model = per_model

    # Vectorize text
    X = vectorizer.transform([text])
    pred = model.predict(X)[0]
    probs = model.predict_proba(X)[0]
    conf = np.max(probs)
    sentiment = label_map.get(pred, "Unknown")

    # SHAP explanation
    try:
        explainer = shap.LinearExplainer(model, X, feature_dependence="independent")
        shap_values = explainer.shap_values(X)
        shap_html = shap.plots.text(explainer, X, display=False)
    except Exception:
        shap_html = "<p>⚠️ No explanation available for this input.</p>"

    return f"Prediction: {sentiment}", f"Confidence: {conf:.3f}", shap_html, ""

# -----------------------------------------------------
# ✅ Build Gradio Interface
# -----------------------------------------------------
with gr.Blocks(theme=gr.themes.Soft()) as app:
    gr.Markdown("<h2 style='text-align:center;'>🌍 Multi-lingual Sentiment (English + Persian)</h2>")
    
    with gr.Row():
        comment = gr.Textbox(label="Comment", placeholder="Type your comment here...")
        lang = gr.Radio(["English", "Persian"], label="Language", value="English")
    
    predict_btn = gr.Button("Predict", variant="primary")
    
    output1 = gr.Textbox(label="Prediction")
    output2 = gr.Textbox(label="Confidence")
    output3 = gr.HTML(label="Explanation")

    predict_btn.click(
        predict_sentiment,
        inputs=[comment, lang],
        outputs=[output1, output2, output3, gr.Textbox(visible=False)]
    )

# -----------------------------------------------------
# ✅ Launch App
# -----------------------------------------------------
if __name__ == "__main__":
    app.launch()