Fix: replace GSheetsConnection with gspread for Docker compatibility

requirements.txt

@@ -4,8 +4,6 @@ torch
 torchvision
 safetensors
 pandas
-st-gsheets-connection
 plotly
-gspread        
-google-auth
-oauth2client
+gspread
+google-auth

streamlit_app.py

@@ -6,11 +6,12 @@ import time
 import plotly.graph_objects as go
 import json
 import os
-from streamlit_gsheets import GSheetsConnection
-
+import gspread
+from google.oauth2.service_account import Credentials
+ 
 # --- PAGE CONFIG ---
 st.set_page_config(page_title="Sentiment Analyzer AI | Bilingual Engine", page_icon="🌐", layout="wide")
-
+ 
 # --- PROFESSIONAL NEUMORPHIC / GLASS CSS ---
 st.markdown("""
     <style>
@@ -22,36 +23,41 @@ st.markdown("""
     [data-testid="stMetricValue"] { color: #00f2fe; font-weight: 800; }
     </style>
     """, unsafe_allow_html=True)
-
-# --- CLOUD DATA LOGGING (GOOGLE SHEETS) ---
-# ✅ FIX: Docker Spaces inject secrets as ENV VARS, not st.secrets
+ 
+# --- GOOGLE SHEETS CONNECTION (gspread - works in Docker) ---
 def get_connection():
     try:
-        # Read from environment variables (correct for Docker Spaces)
+        # Docker Spaces inject secrets as ENV VARS
         json_secrets = os.environ.get("GSHEETS_JSON")
         sheet_url = os.environ.get("GSHEETS_URL")
-
-        # Fallback: try st.secrets (works if running locally or Streamlit SDK Space)
+ 
+        # Fallback for local development
         if not json_secrets:
             json_secrets = st.secrets.get("GSHEETS_JSON")
         if not sheet_url:
             sheet_url = st.secrets.get("GSHEETS_URL")
-
+ 
         if not json_secrets or not sheet_url:
             st.error("❌ Secrets not found. Please add GSHEETS_JSON and GSHEETS_URL in Space Settings → Secrets.")
             st.stop()
-
+ 
         creds_dict = json.loads(json_secrets)
-        conn = st.connection("gsheets", type=GSheetsConnection, credentials=creds_dict)
-        return conn, sheet_url
-
+        scopes = [
+            "https://spreadsheets.google.com/feeds",
+            "https://www.googleapis.com/auth/drive"
+        ]
+        creds = Credentials.from_service_account_info(creds_dict, scopes=scopes)
+        client = gspread.authorize(creds)
+        sheet = client.open_by_url(sheet_url).worksheet("Sheet1")
+        return sheet, sheet_url
+ 
     except json.JSONDecodeError:
         st.error("❌ GSHEETS_JSON is not valid JSON. Please re-paste your service account key.")
         st.stop()
     except Exception as e:
         st.error(f"❌ Connection Failed: {e}")
         st.stop()
-
+ 
 # --- INITIALIZATION ---
 if 'conn' not in st.session_state or 'url' not in st.session_state:
     conn, GSHEETS_URL = get_connection()
@@ -60,75 +66,69 @@ if 'conn' not in st.session_state or 'url' not in st.session_state:
 else:
     conn = st.session_state.conn
     GSHEETS_URL = st.session_state.url
-
+ 
 # --- SAVE TO GOOGLE SHEETS ---
 def save_to_cloud(text, ai_label, ai_score, corrected_label=None):
     try:
-        existing_data = conn.read(spreadsheet=GSHEETS_URL, worksheet="Sheet1", ttl=0)
-
-        # ✅ FIX: Ensure columns exist even if sheet is empty
-        expected_cols = ["Timestamp", "Text", "AI_Label", "Confidence", "Correction"]
-        if existing_data.empty or list(existing_data.columns) != expected_cols:
-            existing_data = pd.DataFrame(columns=expected_cols)
-
-        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)
+        sheet = st.session_state.conn
+        new_row = [
+            datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            text,
+            ai_label,
+            f"{ai_score:.2%}",
+            corrected_label if corrected_label else "N/A"
+        ]
+        sheet.append_row(new_row)
         return True
     except Exception as e:
         st.error(f"Cloud Save Failed: {e}")
         return False
-
+ 
 # --- MODEL ENGINE ---
 MODEL_PATH = "SumedhGajbhiye/Sentiment-Analyzer"
-
+ 
 @st.cache_resource
 def load_engine(path):
     return pipeline("sentiment-analysis", model=path, tokenizer=path)
-
+ 
 # --- 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 STATS ---
 with st.sidebar:
     st.markdown("### 🛠️ ENGINE STATUS")
     try:
-        df_log = conn.read(spreadsheet=GSHEETS_URL, worksheet="Sheet1", ttl=0)
+        sheet = st.session_state.conn
+        all_rows = sheet.get_all_records()
+        df_log = pd.DataFrame(all_rows)
         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 Exception as e:
+    except Exception:
+        df_log = pd.DataFrame()
         st.info("Engine is connecting to cloud...")
-        df_log = pd.DataFrame()  # ✅ FIX: Prevent NameError later if sidebar fails
-
+ 
 # --- MAIN LOGIC ---
 classifier = load_engine(MODEL_PATH)
-
+ 
 if classifier:
     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:
             time.sleep(0.4)
             result = classifier(user_input)[0]
             status.update(label="Analysis Complete", state="complete", expanded=False)
-
+ 
         label = result['label']
         score = result['score']
-
+ 
         emoji_map = {"Positive": "🟢", "Neutral": "🟡", "Negative": "🔴"}
         color = "#00ff88" if "POS" in label.upper() else "#ff4b4b" if "NEG" in label.upper() else "#ffaa00"
-
+ 
         st.markdown(f'''
             <div class="glass-card">
                 <h4 style="color: #888; margin:0;">CLASSIFICATION RESULT</h4>
@@ -136,7 +136,7 @@ if classifier:
                 <p style="color: #aaa; margin-top: 10px;">Deep linguistic scan detected {label.lower()} intent with {score:.1%} confidence.</p>
             </div>
             ''', unsafe_allow_html=True)
-
+ 
         col_chart, col_feed = st.columns([1, 2])
         with col_chart:
             fig = go.Figure(go.Indicator(
@@ -145,7 +145,7 @@ if classifier:
             ))
             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:
             st.markdown("### ⚖️ HUMAN VERIFICATION")
             c1, c2 = st.columns(2)
@@ -162,11 +162,11 @@ if classifier:
                         st.toast(f"✅ Engine forced to {correction}")
                         time.sleep(1.0)
                         st.rerun()
-
+ 
 # --- RECENT LOGS ---
 try:
     if not df_log.empty:
         with st.expander("📂 VIEW SYSTEM LOGS"):
             st.dataframe(df_log.tail(10), use_container_width=True)
 except Exception:
-    pass  # ✅ FIX: Silent fail if df_log never loaded
+    pass