Final deployment with cloud bypass

src/streamlit_app.py

@@ -13,85 +13,44 @@ st.set_page_config(page_title="Sentiment Analyzer AI | Bilingual Engine", page_i
 # --- PROFESSIONAL NEUMORPHIC / GLASS CSS ---
 st.markdown("""
     <style>
-    .stApp {
-        background: linear-gradient(135deg, #12141d 0%, #1a1c2c 100%);
-        color: #ffffff;
-    }
-    
-    div[data-baseweb="input"] {
-        background: rgba(255, 255, 255, 0.05) !important;
-        backdrop-filter: blur(10px) !important;
-        border-radius: 15px !important;
-        border: 1px solid rgba(255, 255, 255, 0.1) !important;
-        padding: 5px !important;
-    }
-
-    .glass-card {
-        background: rgba(255, 255, 255, 0.05);
-        backdrop-filter: blur(10px);
-        border-radius: 20px;
-        border: 1px solid rgba(255, 255, 255, 0.1);
-        padding: 30px;
-        margin-top: 20px;
-        margin-bottom: 25px;
-        transition: 0.4s ease;
-    }
-
-    .stButton>button {
-        background: linear-gradient(90deg, #4facfe 0%, #00f2fe 100%);
-        color: white;
-        border: none;
-        border-radius: 12px;
-        font-weight: 600;
-        letter-spacing: 0.5px;
-        height: 3rem;
-        transition: all 0.3s ease;
-    }
-    .stButton>button:hover {
-        box-shadow: 0 0 20px rgba(79, 172, 254, 0.4);
-        transform: scale(1.02);
-    }
-
+    .stApp { background: linear-gradient(135deg, #12141d 0%, #1a1c2c 100%); color: #ffffff; }
+    div[data-baseweb="input"] { background: rgba(255, 255, 255, 0.05) !important; backdrop-filter: blur(10px) !important; border-radius: 15px !important; border: 1px solid rgba(255, 255, 255, 0.1) !important; padding: 5px !important; }
+    .glass-card { background: rgba(255, 255, 255, 0.05); backdrop-filter: blur(10px); border-radius: 20px; border: 1px solid rgba(255, 255, 255, 0.1); padding: 30px; margin-top: 20px; margin-bottom: 25px; transition: 0.4s ease; }
+    .stButton>button { background: linear-gradient(90deg, #4facfe 0%, #00f2fe 100%); color: white; border: none; border-radius: 12px; font-weight: 600; letter-spacing: 0.5px; height: 3rem; transition: all 0.3s ease; }
+    .stButton>button:hover { box-shadow: 0 0 20px rgba(79, 172, 254, 0.4); transform: scale(1.02); }
     [data-testid="stMetricValue"] { color: #00f2fe; font-weight: 800; }
     </style>
     """, unsafe_allow_html=True)
 
 # --- CLOUD DATA LOGGING (GOOGLE SHEETS) ---
-# This manual approach bypasses the buggy Streamlit "auto-discovery"
 def get_connection():
-    # Use .get() so it doesn't crash if the vault is momentarily empty
-    json_secrets = st.secrets.get("GSHEETS_JSON")
-    sheet_url = st.secrets.get("GSHEETS_URL")
-    
-    if not json_secrets:
-        st.error("❌ 'GSHEETS_JSON' not found in Hugging Face Secrets!")
-        st.stop()
-    if not sheet_url:
-        st.error("❌ 'GSHEETS_URL' not found in Hugging Face Secrets!")
-        st.stop()
-
     try:
-        creds_dict = json.loads(json_secrets)
-        return st.connection("gsheets", type=GSheetsConnection, credentials=creds_dict), sheet_url
-    except Exception as e:
-        st.error(f"⚠️ JSON Parsing Error: {e}")
-        st.stop()
-
-        # 2. Convert string to a real Python dictionary
-        creds_dict = json.loads(json_secrets)
+        # Check if secrets exist to avoid the auto-crash you saw earlier
+        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.")
+            st.stop()
+            
+        json_secrets = st.secrets["GSHEETS_JSON"]
+        sheet_url = st.secrets["GSHEETS_URL"]
         
-        # 3. Create the connection manually
+        creds_dict = json.loads(json_secrets)
         return st.connection("gsheets", type=GSheetsConnection, credentials=creds_dict), sheet_url
     except Exception as e:
-        st.error(f"Vault Connection Error: {e}")
+        st.error(f"Waiting for Vault access... (Technical Error: {e})")
         st.stop()
 
-# Initialize connection and get URL
-conn, GSHEETS_URL = get_connection()
+# Initialize connection safely using Session State
+if 'conn' not in st.session_state:
+    conn, GSHEETS_URL = get_connection()
+    st.session_state.conn = conn
+    st.session_state.url = GSHEETS_URL
+else:
+    conn = st.session_state.conn
+    GSHEETS_URL = st.session_state.url
 
 def save_to_cloud(text, ai_label, ai_score, corrected_label=None):
     try:
-        # Read the current sheet (Worksheet name MUST be "Sheet1")
+        # Use Sheet1 as the target
         existing_data = conn.read(spreadsheet=GSHEETS_URL, worksheet="Sheet1", ttl=0)
         
         new_entry = pd.DataFrame([{
@@ -114,7 +73,6 @@ MODEL_PATH = "SumedhGajbhiye/Sentiment-Analyzer"
 
 @st.cache_resource
 def load_engine(path):
-    # Pulls directly from your HF Model Repo
     return pipeline("sentiment-analysis", model=path, tokenizer=path)
 
 # --- HEADER SECTION ---
@@ -127,9 +85,8 @@ with col_h1:
 with st.sidebar:
     st.markdown("### 🛠️ ENGINE STATUS")
     try:
-        sheet_url = st.secrets.get("GSHEETS_URL", "")
-        df_log = conn.read(spreadsheet=sheet_url, worksheet="Sheet1", ttl=0)
-        st.metric("Total Ingested", len(df_log))
+        df_log = conn.read(spreadsheet=GSHEETS_URL, worksheet="Sheet1", ttl=0)
+        st.metric("Total Ingested", len(df_log)) # Shows count from your sheet
         st.divider()
         st.download_button("📤 Export Dataset", df_log.to_csv(index=False), "engine_feedback.csv", "text/csv")
     except:
@@ -139,12 +96,7 @@ with st.sidebar:
 classifier = load_engine(MODEL_PATH)
 
 if classifier:
-    user_input = st.text_input(
-        "QUERY INPUT:", 
-        placeholder="Enter sentence for deep sentiment analysis...", 
-        key="main_input",
-        label_visibility="collapsed"
-    )
+    user_input = st.text_input("QUERY INPUT:", placeholder="Enter sentence...", key="main_input", label_visibility="collapsed")
 
     if user_input:
         with st.status("Initializing Neural Weights...", expanded=False) as status:
@@ -158,7 +110,6 @@ if classifier:
         emoji_map = {"Positive": "🟢", "Neutral": "🟡", "Negative": "🔴"}
         color = "#00ff88" if "POS" in label.upper() else "#ff4b4b" if "NEG" in label.upper() else "#ffaa00"
 
-        # --- RESULTS GLASS CARD ---
         st.markdown(f'''
             <div class="glass-card">
                 <h4 style="color: #888; margin:0;">CLASSIFICATION RESULT</h4>
@@ -167,29 +118,13 @@ if classifier:
             </div>
             ''', unsafe_allow_html=True)
 
-        # --- CHART & CORRECTION ROW ---
         col_chart, col_feed = st.columns([1, 2])
-
         with col_chart:
             fig = go.Figure(go.Indicator(
-                mode = "gauge+number",
-                value = score * 100,
-                domain = {'x': [0, 1], 'y': [0, 1]},
-                gauge = {
-                    'axis': {'range': [None, 100], 'tickcolor': "#888", 'tickwidth': 2},
-                    'bar': {'color': color},
-                    'bgcolor': "rgba(0,0,0,0)",
-                    'borderwidth': 2,
-                    'bordercolor': "rgba(255,255,255,0.1)",
-                }
+                mode = "gauge+number", value = score * 100,
+                gauge = {'axis': {'range': [None, 100]}, 'bar': {'color': color}, 'bgcolor': "rgba(0,0,0,0)"}
             ))
-            
-            fig.update_layout(
-                height=280, 
-                margin=dict(t=50, b=50, l=40, r=40), 
-                paper_bgcolor='rgba(0,0,0,0)', 
-                font={'color': "#fff", 'family': "Inter"}
-            )
+            fig.update_layout(height=280, margin=dict(t=50, b=50, l=40, r=40), paper_bgcolor='rgba(0,0,0,0)', font={'color': "#fff"})
             st.plotly_chart(fig, use_container_width=True)
 
         with col_feed:
@@ -197,18 +132,17 @@ if classifier:
             c1, c2 = st.columns(2)
             with c1:
                 if st.button("CONFIRM ACCURACY"):
-                    save_to_cloud(user_input, label, score)
-                    st.toast("Logic logged to cloud database.")
-                    time.sleep(1.0)
-                    st.rerun()
-            
+                    if save_to_cloud(user_input, label, score):
+                        st.toast("Logic logged to cloud database.")
+                        time.sleep(1.0)
+                        st.rerun()
             with c2:
                 correction = st.selectbox("OVERRIDE LABEL:", ["Positive", "Neutral", "Negative"])
                 if st.button("FORCE UPDATE ENGINE"):
-                    save_to_cloud(user_input, label, score, corrected_label=correction)
-                    st.toast(f"Engine forced to {correction}")
-                    time.sleep(1.0)
-                    st.rerun()
+                    if save_to_cloud(user_input, label, score, corrected_label=correction):
+                        st.toast(f"Engine forced to {correction}")
+                        time.sleep(1.0)
+                        st.rerun()
 
 # --- RECENT LOGS ---
 try: