Spaces:
Running
Running
| import json | |
| # Remove direct model/util imports if calling analyze_text | |
| # from model_setup import zero_shot, ner | |
| # from utils import parse_entities | |
| # from config import CATEGORY_KEYWORDS | |
| # Import the centralized analysis function | |
| from nlp_service import analyze_text | |
| def lambda_handler(event, context): | |
| # ... (Keep body parsing logic) ... | |
| body_str = event.get("body", "{}") | |
| try: | |
| body = json.loads(body_str) | |
| except json.JSONDecodeError: | |
| print(f"Error decoding JSON body: {body_str}") | |
| return { | |
| "statusCode": 400, | |
| "body": json.dumps({"error": "Invalid JSON in request body"}) | |
| } | |
| text = body.get("text", "") | |
| if not text: | |
| return { | |
| "statusCode": 400, | |
| "body": json.dumps({"error": "Missing 'text' field in request body"}) | |
| } | |
| print(f"Processing text via nlp_service: {text}") # Log input | |
| # Call the centralized NLP service function | |
| try: | |
| analysis_result = analyze_text(text) | |
| status = analysis_result.get("status") | |
| if status == "failed": | |
| print(f"NLP analysis failed: {analysis_result.get('message')}") | |
| # Return 400 for input errors, 500 for internal NLP errors? | |
| # Let's return 400 if it's a known failure from analyze_text | |
| return { | |
| "statusCode": 400, | |
| "body": json.dumps(analysis_result) | |
| } | |
| elif status == "fallback_required": | |
| print(f"NLP analysis requires fallback: {analysis_result.get('message')}") | |
| # Return 200 but indicate fallback needed | |
| return { | |
| "statusCode": 200, | |
| "body": json.dumps(analysis_result) | |
| } | |
| elif status == "success": | |
| print(f"NLP analysis successful: {analysis_result}") | |
| # Return the successful analysis result | |
| return { | |
| "statusCode": 200, | |
| "body": json.dumps(analysis_result) # Already contains status | |
| } | |
| else: | |
| # Should not happen if analyze_text always returns a status | |
| print(f"Error: Unknown status from analyze_text: {status}") | |
| return { | |
| "statusCode": 500, | |
| "body": json.dumps({"error": "Internal server error: Unexpected NLP response"}) | |
| } | |
| except Exception as e: | |
| print(f"Error calling analyze_text from handler: {e}") | |
| import traceback | |
| traceback.print_exc() | |
| return { | |
| "statusCode": 500, | |
| "body": json.dumps({"error": "Internal server error during NLP processing", "details": str(e)}) | |
| } | |
| # Example event structure (for local testing if needed) | |
| if __name__ == '__main__': | |
| # ... (Keep example test cases, they should still work) ... | |
| example_event = { | |
| "body": json.dumps({ | |
| "text": "spent 5 eur on coffee" | |
| }) | |
| } | |
| context = {} | |
| response = lambda_handler(example_event, context) | |
| print("\n--- Lambda Response ---") | |
| # The body is already a JSON string containing the result from analyze_text | |
| print(json.dumps(json.loads(response['body']), indent=2)) | |
| example_event_query = { | |
| "body": json.dumps({ | |
| "text": "how much did I spend last month" | |
| }) | |
| } | |
| response_query = lambda_handler(example_event_query, context) | |
| print("\n--- Lambda Response (Query) ---") | |
| print(json.dumps(json.loads(response_query['body']), indent=2)) | |
| example_event_income = { | |
| "body": json.dumps({ | |
| "text": "salary credited 50000" | |
| }) | |
| } | |
| response_income = lambda_handler(example_event_income, context) | |
| print("\n--- Lambda Response (Income) ---") | |
| print(json.dumps(json.loads(response_income['body']), indent=2)) |