Update app.py

app.py

@@ -1,89 +1,65 @@
 # ============================================================
 # 🌍 Multi-Lingual Sentiment Analysis (English + Persian)
-# With SHAP Interpretability
+# Simple & Clean Deployment (No SHAP Visualization)
 # ============================================================
 
 import gradio as gr
 import joblib
 import numpy as np
-import shap
-import matplotlib.pyplot as plt
-import os
 
 # ------------------------------------------------------------
 # 1️⃣ Load Pretrained Models and Vectorizers
 # ------------------------------------------------------------
-english_model = joblib.load("best_model.pkl")
-english_vectorizer = joblib.load("tfidf_vectorizer.pkl")
+try:
+    english_model = joblib.load("best_model.pkl")
+    english_vectorizer = joblib.load("tfidf_vectorizer.pkl")
 
-persian_model = joblib.load("logistic_regression.pkl")
-persian_vectorizer = joblib.load("tfidf_vectorizer_persian.pkl")
+    persian_model = joblib.load("logistic_regression.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 SHAP Visualization Function
-# ------------------------------------------------------------
-def get_shap_plot(model, vectorizer, text, class_index, class_name):
-    X_input = vectorizer.transform([text])
-    explainer = shap.Explainer(model, vectorizer.transform([" ".join(text.split()[:50])]))
-    shap_values = explainer(X_input)
-    shap_for_class = shap_values.values[0][:, class_index]
-    feature_names = np.array(vectorizer.get_feature_names_out())
-
-    top_idx = np.argsort(-np.abs(shap_for_class))[:10]
-    top_words = feature_names[top_idx]
-    top_impacts = shap_for_class[top_idx]
-
-    plt.figure(figsize=(6, 3))
-    colors = ["crimson" if v > 0 else "steelblue" for v in top_impacts]
-    plt.barh(top_words, top_impacts, color=colors)
-    plt.title(f"Top Words driving {class_name} prediction")
-    plt.xlabel("SHAP Value (Impact)")
-    plt.gca().invert_yaxis()
-    plt.tight_layout()
-    plt.savefig("shap_plot.png", bbox_inches='tight')
-    plt.close()
-
-    return top_words.tolist(), "shap_plot.png"
-
-# ------------------------------------------------------------
-# 3️⃣ Prediction + Interpretability Function
+# 2️⃣ Prediction Function (No SHAP)
 # ------------------------------------------------------------
 def predict_sentiment(text, language):
     if not text.strip():
-        return "Please enter a comment.", None
-
-    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]
-
-    # SHAP interpretation
-    top_words, shap_plot = get_shap_plot(model, vectorizer, text, pred_idx, pred_class)
-
-    # Final output
-    explanation = f"""
-**Predicted Sentiment:** {pred_class}  
-**Confidence:** {conf:.2f}  
-**Top Influential Words:** {', '.join(top_words)}
+        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, shap_plot
+        return explanation
+
+    except Exception as e:
+        return f"❌ Error during prediction: {str(e)}"
 
 # ------------------------------------------------------------
-# 4️⃣ Gradio Interface
+# 3️⃣ Gradio Interface
 # ------------------------------------------------------------
 title = "🌐 Multi-Lingual Sentiment Analysis (English + Persian)"
 description = """
-Select a language, type a comment, and see both the sentiment prediction and SHAP interpretability.
+Select a language, type a comment, and get the predicted sentiment instantly.
 """
 
 examples = [
@@ -98,16 +74,14 @@ examples = [
 demo = gr.Interface(
     fn=predict_sentiment,
     inputs=[
-        gr.Textbox(lines=3, label="Enter comment"),
+        gr.Textbox(lines=3, label="Enter your comment"),
         gr.Radio(["English", "Persian"], label="Choose Dataset/Language", value="English")
     ],
-    outputs=[
-        gr.Markdown(label="Prediction & Explanation"),
-        gr.Image(label="Top Word Contributions")
-    ],
+    outputs=gr.Markdown(label="Prediction"),
     title=title,
     description=description,
     examples=examples,
+    theme="gradio/soft"
 )
 
 if __name__ == "__main__":