Final Secret Bypass Fix

src/streamlit_app.py

@@ -57,42 +57,49 @@ st.markdown("""
     """, unsafe_allow_html=True)
 
 # --- CLOUD DATA LOGGING (GOOGLE SHEETS) ---
-# Use .get() to prevent the "StreamlitSecretNotFoundError" crash
-gsheets_json = st.secrets.get("GSHEETS_JSON")
-sheet_url = st.secrets.get("GSHEETS_URL")
-
-if gsheets_json and sheet_url:
+# This manual approach bypasses the buggy Streamlit "auto-discovery"
+def get_connection():
     try:
-        creds = json.loads(gsheets_json)
-        conn = st.connection("gsheets", type=GSheetsConnection, credentials=creds)
+        # 1. Pull the raw strings from the Hugging Face vault
+        json_secrets = st.secrets.get("GSHEETS_JSON")
+        sheet_url = st.secrets.get("GSHEETS_URL")
+        
+        if not json_secrets or not sheet_url:
+            st.warning("🚀 System Initializing... Waiting for Cloud Vault Access.")
+            st.info("Check your HF Settings: Ensure secrets are named GSHEETS_JSON and GSHEETS_URL.")
+            st.stop()
+
+        # 2. Convert string to a real Python dictionary
+        creds_dict = json.loads(json_secrets)
+        
+        # 3. Create the connection manually
+        return st.connection("gsheets", type=GSheetsConnection, credentials=creds_dict), sheet_url
     except Exception as e:
-        st.error(f"Configuration Error: {e}")
+        st.error(f"Vault Connection Error: {e}")
         st.stop()
-else:
-    # This replaces the red crash box with a clean instructions message
-    st.warning("🚀 System Initializing... Please ensure GSHEETS_JSON and GSHEETS_URL are set in Hugging Face Settings.")
-    st.info("If you just added them, please wait 30 seconds and refresh.")
-    st.stop()
+
+# Initialize connection and get URL
+conn, GSHEETS_URL = get_connection()
 
 def save_to_cloud(text, ai_label, ai_score, corrected_label=None):
     try:
-        # Pull fresh data to append to
-        existing_data = conn.read(spreadsheet=sheet_url, worksheet="Sheet1", ttl=0)
-    except:
-        existing_data = pd.DataFrame()
-
-    needs_review = "YES" if 0.33 <= ai_score <= 0.65 else "NO"
-    new_entry = pd.DataFrame([{
-        "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-        "Text": text,
-        "AI_Label": ai_label,
-        "Confidence": f"{ai_score:.2%}",
-        "Needs_Review": needs_review,
-        "Correction": corrected_label if corrected_label else "N/A"
-    }])
-
-    updated_df = pd.concat([existing_data, new_entry], ignore_index=True)
-    conn.update(spreadsheet=sheet_url, worksheet="Sheet1", data=updated_df)
+        # Read the current sheet (Worksheet name MUST be "Sheet1")
+        existing_data = conn.read(spreadsheet=GSHEETS_URL, worksheet="Sheet1", ttl=0)
+        
+        new_entry = pd.DataFrame([{
+            "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            "Text": text,
+            "AI_Label": ai_label,
+            "Confidence": f"{ai_score:.2%}",
+            "Correction": corrected_label if corrected_label else "N/A"
+        }])
+
+        updated_df = pd.concat([existing_data, new_entry], ignore_index=True)
+        conn.update(spreadsheet=GSHEETS_URL, worksheet="Sheet1", data=updated_df)
+        return True
+    except Exception as e:
+        st.error(f"Cloud Save Failed: {e}")
+        return False
 
 # --- MODEL ENGINE ---
 MODEL_PATH = "SumedhGajbhiye/Sentiment-Analyzer"