Floke
df80e37ffe
Überarbeite den Prompt für die Generierung der Atomic Opener. Die Opener werden nun auf genau zwei Sätze begrenzt, um Prägnanz zu gewährleisten. Der Ton ist faktenbasierter und leitet direkter zu den operativen Herausforderungen über, anstatt generische Lobhudelei zu verwenden.
95 lines
4.1 KiB
Python
95 lines
4.1 KiB
Python
import os
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import sys
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import argparse
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import logging
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from typing import Any # Hinzugefügt
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# Add the company-explorer directory to the Python path
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
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from backend.database import get_db, Company, Industry, Persona # Added Industry and Persona for full context
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from backend.services.classification import ClassificationService
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from backend.lib.logging_setup import setup_logging
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# --- CONFIGURATION ---
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# Setup logging to be very verbose for this script
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setup_logging()
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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def run_debug_analysis(company_identifier: Any, is_id: bool):
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"""
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Runs the full classification and enrichment process for a single company
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in the foreground and prints detailed results.
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"""
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logger.info(f"--- Starting Interactive Debug for Company: {company_identifier} (by {'ID' if is_id else 'Name'}) ---")
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db_session = next(get_db())
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try:
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# 1. Fetch the company
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if is_id:
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company = db_session.query(Company).filter(Company.id == company_identifier).first()
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else:
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company = db_session.query(Company).filter(Company.name == company_identifier).first()
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if not company:
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logger.error(f"Company with {'ID' if is_id else 'Name'} {company_identifier} not found.")
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# If by name, suggest similar names
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if not is_id:
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all_company_names = db_session.query(Company.name).limit(20).all()
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print("\nAvailable Company Names (first 20):")
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for (name,) in all_company_names:
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print(f"- {name}")
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return
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logger.info(f"Found Company: {company.name} (ID: {company.id})")
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# --- PRE-ANALYSIS STATE ---
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print("\n--- METRICS BEFORE ---")
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print(f"Calculated: {company.calculated_metric_value} {company.calculated_metric_unit}")
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print(f"Standardized: {company.standardized_metric_value} {company.standardized_metric_unit}")
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print(f"Opener 1 (Infra): {company.ai_opener}")
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print(f"Opener 2 (Ops): {company.ai_opener_secondary}")
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print("----------------------\n")
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# 2. Instantiate the service
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classifier = ClassificationService()
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# 3. RUN THE CORE LOGIC
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# This will now print all the detailed logs we added
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updated_company = classifier.classify_company_potential(company, db_session)
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# --- POST-ANALYSIS STATE ---
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print("\n--- METRICS AFTER ---")
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print(f"Industry (AI): {updated_company.industry_ai}")
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print(f"Metric Source: {updated_company.metric_source}")
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print(f"Proof Text: {updated_company.metric_proof_text}")
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print(f"Calculated: {updated_company.calculated_metric_value} {updated_company.calculated_metric_unit}")
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print(f"Standardized: {updated_company.standardized_metric_value} {updated_company.standardized_metric_unit}")
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print(f"\nOpener 1 (Infra): {updated_company.ai_opener}")
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print(f"Opener 2 (Ops): {updated_company.ai_opener_secondary}")
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print("---------------------")
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logger.info(f"--- Interactive Debug Finished for Company: {company.name} (ID: {company.id}) ---")
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except Exception as e:
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logger.error(f"An error occurred during analysis: {e}", exc_info=True)
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finally:
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db_session.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run a single company analysis for debugging.")
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parser.add_argument("--id", type=int, help="The ID of the company to analyze.")
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parser.add_argument("--company-name", type=str, help="The name of the company to analyze.")
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args = parser.parse_args()
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if args.id and args.company_name:
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parser.error("Please provide either --id or --company-name, not both.")
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elif args.id:
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run_debug_analysis(args.id, is_id=True)
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elif args.company_name:
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run_debug_analysis(args.company_name, is_id=False)
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else:
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parser.error("Please provide either --id or --company-name.")
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