Update app.py

app.py

@@ -11,9 +11,7 @@ warnings.filterwarnings("ignore", category=UserWarning)
 
 REPO_ID = "Pooja001/SpeechEmotionNet"
 
-# ---------------------------
-# Streamlit UI Settings
-# ---------------------------
+
 st.set_page_config(page_title="SpeechEmotionNet — SER", layout="wide")
 
 st.markdown("""
@@ -35,12 +33,8 @@ st.markdown("""
 </style>
 """, unsafe_allow_html=True)
 
-st.title("🎤 SpeechEmotionNet — Speech Emotion Recognition")
-
+st.title(" SpeechEmotionNet — Speech Emotion Recognition")
 
-# ============================
-# 📌 Load model + assets
-# ============================
 @st.cache_resource
 def load_assets():
     model_path = hf_hub_download(repo_id=REPO_ID, filename="SpeechEmotionNet_best.keras")
@@ -56,10 +50,6 @@ def load_assets():
 
 model, scaler, encoder = load_assets()
 
-
-# ============================
-# 📌 Feature Extraction
-# ============================
 def extract_features_from_array(y, sr=16000):
     y = librosa.util.normalize(y)
 
@@ -70,10 +60,6 @@ def extract_features_from_array(y, sr=16000):
 
     return np.hstack([mfccs, chroma, contrast, zcr])
 
-
-# ============================
-# 📌 Tabs Layout
-# ============================
 tab_pred, tab_explain, tab_eval = st.tabs([
     "Prediction",
     "Explainability (SHAP)",
@@ -81,9 +67,7 @@ tab_pred, tab_explain, tab_eval = st.tabs([
 ])
 
 
-# ========================================================
-# 🔮 PREDICTION TAB
-# ========================================================
+# Prediction
 with tab_pred:
 
     uploaded_file = st.file_uploader("Upload a WAV file", type=["wav"])
@@ -127,10 +111,7 @@ with tab_pred:
         st.markdown(href, unsafe_allow_html=True)
 
 
-
-# ========================================================
-# 🔍 SHAP EXPLAINABILITY TAB (KernelExplainer Fix)
-# ========================================================
+# SHAP - explainability
 with tab_explain:
 
     st.subheader("SHAP Explainability for Uploaded Audio")
@@ -151,7 +132,6 @@ with tab_explain:
 
         st.info(f"SHAP explanation for: {pred_label.upper()}")
 
-        # LATE IMPORT → prevents HuggingFace runtime timeout
         import shap
 
         # Background sample
@@ -189,11 +169,7 @@ with tab_explain:
         ax.set_title("Top 10 Influential Features")
         st.pyplot(fig, use_container_width=True)
 
-
-
-# ========================================================
-# 📊 EVALUATION TAB
-# ========================================================
+# Evaluation
 with tab_eval:
 
     st.subheader("Model Evaluation Metrics")