| import json |
| import os |
| from openai import OpenAI |
| from airtable import Airtable |
|
|
| openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) |
|
|
| |
| airtable = Airtable( |
| os.getenv("AIRTABLE_BASE_ID", ""), |
| "SupplierEvaluations", |
| api_key=os.getenv("AIRTABLE_API_KEY", "") |
| ) |
|
|
| def evaluate_supplier(supplier_data): |
| """Uses LLM to evaluate supplier data.""" |
| prompt = f"Evaluate this supplier based on API support, blind dropshipping, return policy, and reliability: {supplier_data}" |
| response = openai_client.chat.completions.create( |
| model="gpt-4o", |
| messages=[ |
| {"role": "system", "content": "You are a critical supplier evaluator."}, |
| {"role": "user", "content": prompt} |
| ] |
| ) |
| return response.choices[0].message.content |
|
|
| def log_evaluation(supplier_name, evaluation): |
| """Logs the evaluation to Airtable.""" |
| try: |
| airtable.insert({'Supplier': supplier_name, 'Evaluation': evaluation}) |
| return json.dumps({"status": "success"}) |
| except Exception as e: |
| return json.dumps({"status": "error", "message": str(e)}) |
|
|