app.py

# ============================================================
# 🌍 Multi-Lingual Sentiment Analysis (English + Persian)
# Simple & Clean Deployment (No SHAP Visualization)
# ============================================================

import gradio as gr
import joblib
import numpy as np

# ------------------------------------------------------------
# 1️⃣ Load Pretrained Models and Vectorizers
# ------------------------------------------------------------
try:
    english_model = joblib.load("best_model_english.pkl")
    english_vectorizer = joblib.load("tfidf_vectorizer_english.pkl")

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

    print("✅ Models and vectorizers loaded successfully!")
except Exception as e:
    raise RuntimeError(f"❌ Error loading models: {e}")

# Define class labels
english_labels = ["Negative", "Neutral", "Positive"]
persian_labels = ["منفی", "خنثی", "مثبت"]

# ------------------------------------------------------------
# 2️⃣ Prediction Function (No SHAP)
# ------------------------------------------------------------
def predict_sentiment(text, language):
    if not text.strip():
        return "⚠️ Please enter a comment to analyze."

    try:
        if language == "English":
            model, vectorizer, labels = english_model, english_vectorizer, english_labels
        else:
            model, vectorizer, labels = persian_model, persian_vectorizer, persian_labels

        X_input = vectorizer.transform([text])
        probs = model.predict_proba(X_input)[0]
        pred_idx = np.argmax(probs)
        pred_class = labels[pred_idx]
        conf = probs[pred_idx]

        # Output explanation
        explanation = f"""
### 🧠 Predicted Sentiment: **{pred_class}**  
**Confidence:** {conf:.2f}
"""
        return explanation

    except Exception as e:
        return f"❌ Error during prediction: {str(e)}"

# ------------------------------------------------------------
# 3️⃣ Gradio Interface
# ------------------------------------------------------------
title = "🌐 Multi-Lingual Sentiment Analysis (English + Persian)"
description = """
Select a language, type a comment, and get the predicted sentiment instantly.
"""

examples = [
    ["I love this product! Highly recommend.", "English"],
    ["Bad experience ever, totally disappointed.", "English"],
    ["The service was okay, nothing special.", "English"],
    ["این محصول فوق‌العاده است", "Persian"],
    ["تجربه‌ی بدی بود، ناراضی‌ام", "Persian"],
    ["کیفیتش متوسط بود", "Persian"]
]

demo = gr.Interface(
    fn=predict_sentiment,
    inputs=[
        gr.Textbox(lines=3, label="Enter your comment"),
        gr.Radio(["English", "Persian"], label="Choose Dataset/Language", value="English")
    ],
    outputs=gr.Markdown(label="Prediction"),
    title=title,
    description=description,
    examples=examples,
    theme="gradio/soft"
)

if __name__ == "__main__":
    demo.launch()