Update mtdna_backend.py

mtdna_backend.py

@@ -681,6 +681,50 @@ def load_user_usage():
 
 #     except Exception as e:
 #         print(f"❌ Failed to save user_usage_log.json to Google Drive: {e}")
+# def save_user_usage(usage_dict):
+#     try:
+#         creds_dict = json.loads(os.environ["GCP_CREDS_JSON"])
+#         scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
+#         creds = ServiceAccountCredentials.from_json_keyfile_dict(creds_dict, scope)
+#         client = gspread.authorize(creds)
+
+#         spreadsheet = client.open("user_usage_log")
+#         sheet = spreadsheet.sheet1
+
+#         # Step 1: Convert new usage to DataFrame
+#         df_new = pd.DataFrame(list(usage_dict.items()), columns=["email", "usage_count"])
+#         df_new["email"] = df_new["email"].str.strip().str.lower()
+
+#         # Step 2: Load existing data
+#         existing_data = sheet.get_all_values()
+#         print("🧪 Sheet existing_data:", existing_data)
+
+#         # Try to load old data
+#         if existing_data and len(existing_data[0]) >= 1:
+#             df_old = pd.DataFrame(existing_data[1:], columns=existing_data[0])
+
+#             # Fix missing columns
+#             if "email" not in df_old.columns:
+#                 df_old["email"] = ""
+#             if "usage_count" not in df_old.columns:
+#                 df_old["usage_count"] = 0
+
+#             df_old["email"] = df_old["email"].str.strip().str.lower()
+#             df_old["usage_count"] = pd.to_numeric(df_old["usage_count"], errors="coerce").fillna(0).astype(int)
+#         else:
+#             df_old = pd.DataFrame(columns=["email", "usage_count"])
+
+#         # Step 3: Merge
+#         df_combined = pd.concat([df_old, df_new], ignore_index=True)
+#         df_combined = df_combined.groupby("email", as_index=False).sum()
+
+#         # Step 4: Write back
+#         sheet.clear()
+#         sheet.update([df_combined.columns.tolist()] + df_combined.astype(str).values.tolist())
+#         print("✅ Saved user usage to user_usage_log sheet.")
+
+#     except Exception as e:
+#         print(f"❌ Failed to save user usage to Google Sheets: {e}")
 def save_user_usage(usage_dict):
     try:
         creds_dict = json.loads(os.environ["GCP_CREDS_JSON"])
@@ -691,36 +735,30 @@ def save_user_usage(usage_dict):
         spreadsheet = client.open("user_usage_log")
         sheet = spreadsheet.sheet1
 
-        # Step 1: Convert new usage to DataFrame
+        # Build new df
         df_new = pd.DataFrame(list(usage_dict.items()), columns=["email", "usage_count"])
         df_new["email"] = df_new["email"].str.strip().str.lower()
+        df_new["usage_count"] = pd.to_numeric(df_new["usage_count"], errors="coerce").fillna(0).astype(int)
 
-        # Step 2: Load existing data
+        # Read existing data
         existing_data = sheet.get_all_values()
-        print("🧪 Sheet existing_data:", existing_data)
-
-        # Try to load old data
-        if existing_data and len(existing_data[0]) >= 1:
+        if existing_data and len(existing_data[0]) >= 2:
             df_old = pd.DataFrame(existing_data[1:], columns=existing_data[0])
-
-            # Fix missing columns
-            if "email" not in df_old.columns:
-                df_old["email"] = ""
-            if "usage_count" not in df_old.columns:
-                df_old["usage_count"] = 0
-
             df_old["email"] = df_old["email"].str.strip().str.lower()
             df_old["usage_count"] = pd.to_numeric(df_old["usage_count"], errors="coerce").fillna(0).astype(int)
         else:
             df_old = pd.DataFrame(columns=["email", "usage_count"])
 
-        # Step 3: Merge
-        df_combined = pd.concat([df_old, df_new], ignore_index=True)
-        df_combined = df_combined.groupby("email", as_index=False).sum()
+        # ✅ Overwrite specific emails only
+        df_old = df_old.set_index("email")
+        for email, count in usage_dict.items():
+            email = email.strip().lower()
+            df_old.loc[email, "usage_count"] = count
+        df_old = df_old.reset_index()
 
-        # Step 4: Write back
+        # Save
         sheet.clear()
-        sheet.update([df_combined.columns.tolist()] + df_combined.astype(str).values.tolist())
+        sheet.update([df_old.columns.tolist()] + df_old.astype(str).values.tolist())
         print("✅ Saved user usage to user_usage_log sheet.")
 
     except Exception as e:
@@ -728,6 +766,7 @@ def save_user_usage(usage_dict):
 
 
 
+
 # def increment_usage(user_id, num_samples=1):
 #     usage = load_user_usage()
 #     if user_id not in usage: