[2f988f42] fix(company-explorer): Implement robust quantitative potential and atomic opener generation\n\n- Refactored ClassificationService for two-stage metric extraction (direct area and proxy).- Enhanced MetricParser for targeted value matching and robust number parsing.- Implemented persona-specific 'Atomic Opener' generation using segmented pains.- Fixed logging configuration and Pydantic response models.- Added dedicated debugging script and updated documentation (GEMINI.md, MIGRATION_PLAN.md).

company-explorer/backend/scripts/debug_single_company.py

@@ -0,0 +1,72 @@
+import os
+import sys
+import argparse
+import logging
+
+# Add the backend directory to the Python path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from backend.database import get_db, Company
+from backend.services.classification import ClassificationService
+from backend.lib.logging_setup import setup_logging
+
+# --- CONFIGURATION ---
+# Setup logging to be very verbose for this script
+setup_logging()
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+def run_debug_analysis(company_id: int):
+    """
+    Runs the full classification and enrichment process for a single company
+    in the foreground and prints detailed results.
+    """
+    logger.info(f"--- Starting Interactive Debug for Company ID: {company_id} ---")
+    
+    db_session = next(get_db())
+    
+    try:
+        # 1. Fetch the company
+        company = db_session.query(Company).filter(Company.id == company_id).first()
+        if not company:
+            logger.error(f"Company with ID {company_id} not found.")
+            return
+
+        logger.info(f"Found Company: {company.name}")
+        
+        # --- PRE-ANALYSIS STATE ---
+        print("\n--- METRICS BEFORE ---")
+        print(f"Calculated: {company.calculated_metric_value} {company.calculated_metric_unit}")
+        print(f"Standardized: {company.standardized_metric_value} {company.standardized_metric_unit}")
+        print("----------------------\n")
+        
+        # 2. Instantiate the service
+        classifier = ClassificationService()
+        
+        # 3. RUN THE CORE LOGIC
+        # This will now print all the detailed logs we added
+        updated_company = classifier.classify_company_potential(company, db_session)
+        
+        # --- POST-ANALYSIS STATE ---
+        print("\n--- METRICS AFTER ---")
+        print(f"Industry (AI): {updated_company.industry_ai}")
+        print(f"Metric Source: {updated_company.metric_source}")
+        print(f"Proof Text: {updated_company.metric_proof_text}")
+        print(f"Calculated: {updated_company.calculated_metric_value} {updated_company.calculated_metric_unit}")
+        print(f"Standardized: {updated_company.standardized_metric_value} {updated_company.standardized_metric_unit}")
+        print(f"\nOpener 1 (Infra): {updated_company.ai_opener}")
+        print(f"Opener 2 (Ops): {updated_company.ai_opener_secondary}")
+        print("---------------------")
+        
+        logger.info(f"--- Interactive Debug Finished for Company ID: {company_id} ---")
+
+    finally:
+        db_session.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run a single company analysis for debugging.")
+    parser.add_argument("--id", type=int, default=1, help="The ID of the company to analyze.")
+    args = parser.parse_args()
+    
+    run_debug_analysis(args.id)