app.py

import gradio as gr
import joblib
import shap
import numpy as np
import matplotlib.pyplot as plt
import tempfile
import os

# ---------------------------------------------------------
# Load both models and vectorizers
# ---------------------------------------------------------
english_model = joblib.load("models/english_model.pkl")
english_vec   = joblib.load("models/english_vectorizer.pkl")

persian_model = joblib.load("models/persian_model.pkl")
persian_vec   = joblib.load("models/persian_vectorizer.pkl")

class_names = ["Negative", "Neutral", "Positive"]

# ---------------------------------------------------------
# Prediction + Interpretability Function
# ---------------------------------------------------------
def predict_sentiment(text, language):
    if not text.strip():
        return "Please enter text!", None

    if language == "English":
        model, vec = english_model, english_vec
    else:
        model, vec = persian_model, persian_vec

    X = vec.transform([text])
    probs = model.predict_proba(X)[0]
    pred_idx = np.argmax(probs)
    label = class_names[pred_idx]

    # --- SHAP interpretability ---
    explainer = shap.LinearExplainer(model, vec.transform([text]))
    shap_vals = explainer(X)
    shap_values = shap_vals.values[0][:, pred_idx]
    feature_names = vec.get_feature_names_out()

    top_idx = np.argsort(-abs(shap_values))[:10]
    tokens = [feature_names[i] for i in top_idx]
    impacts = [shap_values[i] for i in top_idx]

    # Save temporary bar chart
    fig, ax = plt.subplots(figsize=(6, 3))
    colors = ["crimson" if v > 0 else "steelblue" for v in impacts]
    ax.barh(tokens, impacts, color=colors)
    ax.invert_yaxis()
    ax.set_title(f"Top Words driving {label} prediction")
    tmp_path = tempfile.mktemp(suffix=".png")
    plt.tight_layout()
    plt.savefig(tmp_path)
    plt.close(fig)

    explanation = f"""
**Predicted Sentiment:** {label}\n
**Confidence:** {probs[pred_idx]:.2f}\n
**Top Influential Words:**\n
{', '.join(tokens)}
"""
    return explanation, tmp_path

# ---------------------------------------------------------
# Gradio UI
# ---------------------------------------------------------
iface = gr.Interface(
    fn=predict_sentiment,
    inputs=[
        gr.Textbox(lines=3, label="Enter comment"),
        gr.Radio(["English", "Persian"], label="Choose Dataset/Language")
    ],
    outputs=[
        gr.Markdown(label="Prediction + Interpretation"),
        gr.Image(label="Top Word Contributions")
    ],
    title="🌍 Multi-Lingual Sentiment Analysis (English + Persian)",
    description="Select a language, type a comment, and see both the prediction and SHAP interpretability."
)

iface.launch()