Fix: Add secret-loading safety net and fix indentation

src/streamlit_app.py

@@ -25,21 +25,25 @@ st.markdown("""
 # --- CLOUD DATA LOGGING (GOOGLE SHEETS) ---
 def get_connection():
     try:
-        # Check if secrets exist to avoid the auto-crash you saw earlier
+        # Step 1: Wait a moment for the HF Vault to mount
         if "GSHEETS_JSON" not in st.secrets or "GSHEETS_URL" not in st.secrets:
-            st.warning("🚀 System Initializing... Please ensure GSHEETS_JSON is set in HF Settings.")
+            # Instead of crashing, we show a friendly wait message
+            st.warning("🚀 System Initializing... Connecting to Cloud Vault.")
             st.stop()
             
+        # Step 2: Retrieve verified secrets
         json_secrets = st.secrets["GSHEETS_JSON"]
         sheet_url = st.secrets["GSHEETS_URL"]
         
+        # Step 3: Parse and connect
         creds_dict = json.loads(json_secrets)
         return st.connection("gsheets", type=GSheetsConnection, credentials=creds_dict), sheet_url
     except Exception as e:
         st.error(f"Waiting for Vault access... (Technical Error: {e})")
         st.stop()
 
-# Initialize connection safely using Session State
+# --- INITIALIZATION ---
+# Use session state so we don't re-connect on every click
 if 'conn' not in st.session_state:
     conn, GSHEETS_URL = get_connection()
     st.session_state.conn = conn
@@ -50,7 +54,7 @@ else:
 
 def save_to_cloud(text, ai_label, ai_score, corrected_label=None):
     try:
-        # Use Sheet1 as the target
+        # worksheet name must match your Google Sheet tab exactly
         existing_data = conn.read(spreadsheet=GSHEETS_URL, worksheet="Sheet1", ttl=0)
         
         new_entry = pd.DataFrame([{
@@ -75,24 +79,24 @@ MODEL_PATH = "SumedhGajbhiye/Sentiment-Analyzer"
 def load_engine(path):
     return pipeline("sentiment-analysis", model=path, tokenizer=path)
 
-# --- HEADER SECTION ---
+# --- UI LAYOUT ---
 col_h1, col_h2 = st.columns([3, 1])
 with col_h1:
     st.title("Sentiment Analyzer")
     st.caption("Advanced Bilingual Sentiment Analysis for English, Hindi & Hinglish")
 
-# --- SIDEBAR HUD ---
+# Sidebar Stats
 with st.sidebar:
     st.markdown("### 🛠️ ENGINE STATUS")
     try:
         df_log = conn.read(spreadsheet=GSHEETS_URL, worksheet="Sheet1", ttl=0)
-        st.metric("Total Ingested", len(df_log)) # Shows count from your sheet
+        st.metric("Total Ingested", len(df_log))
         st.divider()
         st.download_button("📤 Export Dataset", df_log.to_csv(index=False), "engine_feedback.csv", "text/csv")
     except:
         st.info("Engine is connecting to cloud...")
 
-# --- ANALYSIS INTERFACE ---
+# Main Logic
 classifier = load_engine(MODEL_PATH)
 
 if classifier: