refactor: Clean up migration script and improve readability by removing unnecessary imports and formatting

app/routers/investors.py

@@ -13,7 +13,6 @@ from schemas.router_schemas import (
     SectorMinimal,
 )
 from services.compatibility_score import (
-    calculate_project_investor_compatibility,
     _calculate_project_fund_compatibility,
     _calculate_project_investor_direct_compatibility,
 )
@@ -91,7 +90,9 @@ def read_investors(
                 selectinload(InvestorTable.portfolio_companies),
                 selectinload(InvestorTable.team_members),
                 selectinload(InvestorTable.sectors),
-                selectinload(InvestorTable.funds).selectinload(FundTable.investment_stages),
+                selectinload(InvestorTable.funds).selectinload(
+                    FundTable.investment_stages
+                ),
                 selectinload(InvestorTable.funds).selectinload(FundTable.sectors),
             )
             .all()
@@ -106,7 +107,9 @@ def read_investors(
                 selectinload(InvestorTable.portfolio_companies),
                 selectinload(InvestorTable.team_members),
                 selectinload(InvestorTable.sectors),
-                selectinload(InvestorTable.funds).selectinload(FundTable.investment_stages),
+                selectinload(InvestorTable.funds).selectinload(
+                    FundTable.investment_stages
+                ),
                 selectinload(InvestorTable.funds).selectinload(FundTable.sectors),
             )
             .offset(offset)
@@ -143,7 +146,9 @@ def read_investors(
                 # Get top 3 sectors from fund (id and name only) - sorted alphabetically
                 fund_sectors = [
                     SectorMinimal(id=sector.id, name=sector.name)
-                    for sector in sorted(fund.sectors[:3] if fund.sectors else [], key=lambda s: s.name)
+                    for sector in sorted(
+                        fund.sectors[:3] if fund.sectors else [], key=lambda s: s.name
+                    )
                 ]
 
                 investment_response = InvestmentResponse(
@@ -188,7 +193,7 @@ def read_investors(
     if project is not None:
         investment_responses.sort(key=lambda x: x.compatibility_score, reverse=True)
         # Apply pagination after sorting
-        investment_responses = investment_responses[offset:offset + page_size]
+        investment_responses = investment_responses[offset : offset + page_size]
 
     # Calculate total pages
     total_pages = (total_count + page_size - 1) // page_size
@@ -320,7 +325,9 @@ def filter_investors(
         # Get top 3 sectors from fund (id and name only) - sorted alphabetically
         fund_sectors = [
             SectorMinimal(id=sector.id, name=sector.name)
-            for sector in sorted(fund.sectors[:3] if fund.sectors else [], key=lambda s: s.name)
+            for sector in sorted(
+                fund.sectors[:3] if fund.sectors else [], key=lambda s: s.name
+            )
         ]
 
         investment_response = InvestmentResponse(
@@ -344,7 +351,7 @@ def filter_investors(
         investment_responses.sort(key=lambda x: x.compatibility_score, reverse=True)
         # Apply pagination after sorting
         offset = (page - 1) * page_size
-        investment_responses = investment_responses[offset:offset + page_size]
+        investment_responses = investment_responses[offset : offset + page_size]
 
     # Calculate total pages
     total_pages = (total_count + page_size - 1) // page_size

app/services/compatibility_score.py

@@ -117,41 +117,41 @@ def _calculate_project_fund_compatibility(
     # 2. Sector Overlap (30 points)
     sector_score = 0
     if project.sector and fund.sectors:
-        project_sectors = [s for s in project.sector if hasattr(s, 'name')]
+        project_sectors = [s for s in project.sector if hasattr(s, "name")]
-        fund_sectors = [s for s in fund.sectors if hasattr(s, 'name')]
+        fund_sectors = [s for s in fund.sectors if hasattr(s, "name")]
-        
+
         if project_sectors and fund_sectors:
             # Use fuzzy matching to account for similar but not identical sector names
             match_count = 0
             total_matches = 0
-            
+
             for proj_sector in project_sectors:
                 best_match_score = 0
                 proj_name = proj_sector.name.lower().strip()
-                
+
                 for fund_sector in fund_sectors:
                     fund_name = fund_sector.name.lower().strip()
-                    
+
                     # Exact match
                     if proj_name == fund_name:
                         best_match_score = 1.0
                         break
-                    
+
                     # Fuzzy match using sequence matcher
                     similarity = SequenceMatcher(None, proj_name, fund_name).ratio()
-                    
+
                     # Also check if one contains the other (substring match)
                     if proj_name in fund_name or fund_name in proj_name:
                         similarity = max(similarity, 0.8)
-                    
+
                     best_match_score = max(best_match_score, similarity)
-                
+
                 # Count matches with threshold
                 # Perfect match (1.0), strong match (>0.75), partial match (>0.6)
                 if best_match_score >= 0.6:
                     total_matches += best_match_score
                     match_count += 1
-            
+
             if match_count > 0:
                 # Calculate overlap ratio based on fuzzy matches
                 overlap_ratio = total_matches / len(project_sectors)
@@ -246,40 +246,40 @@ def _calculate_project_investor_direct_compatibility(
     # 2. Sector Overlap (30 points)
     sector_score = 0
     if project.sector and investor.sectors:
-        project_sectors = [s for s in project.sector if hasattr(s, 'name')]
+        project_sectors = [s for s in project.sector if hasattr(s, "name")]
-        investor_sectors = [s for s in investor.sectors if hasattr(s, 'name')]
+        investor_sectors = [s for s in investor.sectors if hasattr(s, "name")]
-        
+
         if project_sectors and investor_sectors:
             # Use fuzzy matching to account for similar but not identical sector names
             match_count = 0
             total_matches = 0
-            
+
             for proj_sector in project_sectors:
                 best_match_score = 0
                 proj_name = proj_sector.name.lower().strip()
-                
+
                 for inv_sector in investor_sectors:
                     inv_name = inv_sector.name.lower().strip()
-                    
+
                     # Exact match
                     if proj_name == inv_name:
                         best_match_score = 1.0
                         break
-                    
+
                     # Fuzzy match using sequence matcher
                     similarity = SequenceMatcher(None, proj_name, inv_name).ratio()
-                    
+
                     # Also check if one contains the other (substring match)
                     if proj_name in inv_name or inv_name in proj_name:
                         similarity = max(similarity, 0.8)
-                    
+
                     best_match_score = max(best_match_score, similarity)
-                
+
                 # Count matches with threshold
                 if best_match_score >= 0.6:
                     total_matches += best_match_score
                     match_count += 1
-            
+
             if match_count > 0:
                 # Calculate overlap ratio based on fuzzy matches
                 overlap_ratio = total_matches / len(project_sectors)
@@ -384,43 +384,70 @@ def _check_geographic_overlap(location1: str, location2: str) -> bool:
     # Normalize inputs
     loc1 = location1.lower().strip()
     loc2 = location2.lower().strip()
-    
+
     # Common geographic groupings with broader regional mappings
     geo_groups = [
         # North America
         ["usa", "us", "united states", "america", "u.s.", "u.s.a"],
         ["canada", "canadian"],
         ["mexico", "mexican"],
-        
         # Europe and countries
-        ["europe", "european", "eu", "germany", "france", "uk", "united kingdom", 
+        [
-         "britain", "spain", "italy", "netherlands", "belgium", "sweden", "denmark",
+            "europe",
-         "norway", "finland", "poland", "portugal", "austria", "switzerland", 
+            "european",
-         "ireland", "greece", "czech", "romania"],
+            "eu",
-        
+            "germany",
+            "france",
+            "uk",
+            "united kingdom",
+            "britain",
+            "spain",
+            "italy",
+            "netherlands",
+            "belgium",
+            "sweden",
+            "denmark",
+            "norway",
+            "finland",
+            "poland",
+            "portugal",
+            "austria",
+            "switzerland",
+            "ireland",
+            "greece",
+            "czech",
+            "romania",
+        ],
         # UK specific
         ["uk", "united kingdom", "britain", "england", "scotland", "wales", "london"],
-        
         # US states
         ["california", "ca", "san francisco", "los angeles", "silicon valley"],
         ["new york", "ny", "nyc"],
         ["texas", "tx"],
         ["massachusetts", "ma", "boston"],
         ["washington", "seattle"],
-        
         # Asia
-        ["asia", "asian", "china", "japan", "korea", "singapore", "hong kong", 
+        [
-         "india", "indonesia", "thailand", "vietnam", "malaysia", "philippines"],
+            "asia",
-        
+            "asian",
+            "china",
+            "japan",
+            "korea",
+            "singapore",
+            "hong kong",
+            "india",
+            "indonesia",
+            "thailand",
+            "vietnam",
+            "malaysia",
+            "philippines",
+        ],
         # Middle East
         ["middle east", "israel", "uae", "dubai", "saudi arabia"],
-        
         # Latin America
         ["latin america", "brazil", "argentina", "chile", "colombia", "mexico"],
-        
         # Africa
         ["africa", "african", "south africa", "nigeria", "kenya", "egypt"],
-        
         # Oceania
         ["australia", "australian", "new zealand"],
     ]
@@ -431,7 +458,7 @@ def _check_geographic_overlap(location1: str, location2: str) -> bool:
         found_in_2 = any(term in loc2 for term in group)
         if found_in_1 and found_in_2:
             return True
-    
+
     # Check for direct substring match (one contains the other)
     if loc1 in loc2 or loc2 in loc1:
         return True

migrations/add_feedback_fixes_2025_01_07.py

@@ -0,0 +1,117 @@
+"""
+Migration: Add fields from feedback fixes
+Date: 2025-01-07
+
+Adds the following fields:
+- projects.is_archived (INTEGER, default 0)
+- companies.product_service (TEXT, nullable)
+- companies.clients (TEXT, nullable - stored as JSON string)
+- investor_members.linkedin (VARCHAR, nullable)
+"""
+
+import sys
+from pathlib import Path
+
+# Add parent directory to path to import app modules
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from sqlalchemy import text
+
+from app.db.db import engine
+
+
+def check_column_exists(conn, table_name, column_name):
+    """Check if a column exists in a table"""
+    result = conn.execute(text(f"PRAGMA table_info({table_name})"))
+    columns = [row[1] for row in result]
+    return column_name in columns
+
+
+def upgrade():
+    """Add new columns to tables"""
+    print("Running migration: Add feedback fixes fields")
+    print("=" * 60)
+
+    with engine.begin() as conn:  # Use begin() for transaction management
+        # 1. Add is_archived to projects table
+        print("\n1. Adding 'is_archived' column to projects table...")
+        if check_column_exists(conn, "projects", "is_archived"):
+            print("   ✓ Column 'is_archived' already exists. Skipping.")
+        else:
+            conn.execute(
+                text(
+                    "ALTER TABLE projects ADD COLUMN is_archived INTEGER DEFAULT 0 NOT NULL"
+                )
+            )
+            # Set default value for existing rows
+            conn.execute(
+                text("UPDATE projects SET is_archived = 0 WHERE is_archived IS NULL")
+            )
+            print("   ✓ Successfully added 'is_archived' column to projects table")
+
+        # 2. Add product_service to companies table
+        print("\n2. Adding 'product_service' column to companies table...")
+        if check_column_exists(conn, "companies", "product_service"):
+            print("   ✓ Column 'product_service' already exists. Skipping.")
+        else:
+            conn.execute(text("ALTER TABLE companies ADD COLUMN product_service TEXT"))
+            print("   ✓ Successfully added 'product_service' column to companies table")
+
+        # 3. Add clients to companies table
+        print("\n3. Adding 'clients' column to companies table...")
+        if check_column_exists(conn, "companies", "clients"):
+            print("   ✓ Column 'clients' already exists. Skipping.")
+        else:
+            conn.execute(text("ALTER TABLE companies ADD COLUMN clients TEXT"))
+            print("   ✓ Successfully added 'clients' column to companies table")
+
+        # 4. Add linkedin to investor_members table
+        print("\n4. Adding 'linkedin' column to investor_members table...")
+        if check_column_exists(conn, "investor_members", "linkedin"):
+            print("   ✓ Column 'linkedin' already exists. Skipping.")
+        else:
+            conn.execute(
+                text("ALTER TABLE investor_members ADD COLUMN linkedin VARCHAR")
+            )
+            print("   ✓ Successfully added 'linkedin' column to investor_members table")
+
+    print("\n" + "=" * 60)
+    print("Migration completed successfully!")
+
+
+def downgrade():
+    """Remove added columns from tables"""
+    print("Running downgrade: Remove feedback fixes fields")
+    print("=" * 60)
+
+    # Note: SQLite doesn't support DROP COLUMN directly
+    print("\nWarning: SQLite doesn't support DROP COLUMN directly.")
+    print("To remove these columns, you would need to:")
+    print("1. Create new tables without the columns")
+    print("2. Copy data from old tables to new tables")
+    print("3. Drop old tables and rename new tables")
+    print("\nColumns to remove:")
+    print("  - projects.is_archived")
+    print("  - companies.product_service")
+    print("  - companies.clients")
+    print("  - investor_members.linkedin")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Run database migration")
+    parser.add_argument(
+        "direction",
+        choices=["upgrade", "downgrade"],
+        default="upgrade",
+        nargs="?",
+        help="Migration direction (default: upgrade)",
+    )
+
+    args = parser.parse_args()
+
+    if args.direction == "upgrade":
+        upgrade()
+    else:
+        downgrade()