fix: Err. 500 connexion supabase

.gitignore

@@ -1,5 +1,7 @@
 venv/ 
-__pycache__/ 
+__pycache__/
+.vscode/ 
+pyrightconfig.json
 # Ignorer tout le dossier des scripts sensibles
 app/scripts/
 force_admin.py

app/api/v1/investigate.py

@@ -13,6 +13,7 @@ from typing import Optional, List
 from datetime import datetime
 from sqlalchemy.orm import Session
 
+from app.database import engine
 from app.core.supabase_client import get_user_client
 from app.services.llm_service import llm_service
 from app.services.data_loader import get_companies_df
@@ -93,12 +94,20 @@ async def investigate_company(
     """
     logger.info(f"📋 Investigation request for company_id: {company_id}")
 
-    # ── 1. Retrieve enriched data from Supabase ────────────────────────────
-    client = get_user_client() # Public read
-    resp = client.table("enriched_companies").select("*").eq("company_id", company_id).execute()
-
-    # Note: If not in enriched_companies, we'll try to build it from basic tables
-    enriched = resp.data[0] if resp.data else {}
+    # ── 1. Retrieve enriched data from Supabase via SQL ─────────────────────
+    enriched = {}
+    try:
+        with engine.connect() as conn:
+            from sqlalchemy import text
+            result = conn.execute(
+                text("SELECT * FROM enriched_companies WHERE company_id = :cid"),
+                {"cid": company_id}
+            )
+            row = result.mappings().first()
+            if row:
+                enriched = dict(row)
+    except Exception as e:
+        logger.error(f"Error fetching enriched data: {e}")
     company_name = enriched.get("company_name", "")
     wilaya = enriched.get("wilaya", "")
     enrichment_data = enriched.get("data", {})

app/services/data_loader.py

@@ -3,7 +3,7 @@ import json
 import os
 from pathlib import Path
 from dotenv import load_dotenv
-from app.core.supabase_client import get_admin_client
+from app.database import engine
 from app.core.utils import clean_nans
 
 # Load environment variables
@@ -73,18 +73,20 @@ class DataLoader:
         if self._cached_df is not None and not force_refresh:
             return self._cached_df
 
-        print("Fetching companies from Supabase view 'companies_unified'...")
+        print("Fetching companies from Supabase view 'companies_unified' via SQL...")
         try:
-            supabase = get_admin_client()
-            # Use the unified view created in Supabase
-            response = supabase.table("companies_unified").select("*").execute()
+            # Fetch directly using SQLAlchemy engine
+            query = "SELECT * FROM companies_unified"
+            df = pd.read_sql(query, con=engine)
             
-            if not response.data:
+            if df.empty:
                 print("Warning: No data returned from Supabase view.")
                 return pd.DataFrame()
             
             # Clean NaNs and convert to DF
-            data = clean_nans(response.data)
+            # Clean NaNs
+            # Note: Pandas read_sql already handles many things, but clean_nans ensures JSON safety for API
+            data = clean_nans(df.to_dict(orient='records'))
             df = pd.DataFrame(data)
             
             # Add 'id' column for backward compatibility (1-indexed)
@@ -114,13 +116,15 @@ class DataLoader:
         Fetch a single company from Supabase by its normalized name.
         """
         try:
-            supabase = get_admin_client()
-            response = supabase.table("companies_unified")\
-                .select("*")\
-                .eq("name_normalized", company_name_normalized)\
-                .maybe_single()\
-                .execute()
-            return clean_nans(response.data) if response.data else None
+            query = "SELECT * FROM companies_unified WHERE name_normalized = %s"
+            # Use pandas read_sql with params or engine.connect()
+            with engine.connect() as conn:
+                from sqlalchemy import text
+                result = conn.execute(text("SELECT * FROM companies_unified WHERE name_normalized = :name"), {"name": company_name_normalized})
+                row = result.mappings().first()
+                if row:
+                    return clean_nans(dict(row))
+            return None
         except Exception as e:
             print(f"Error fetching specific company: {e}")
             return None