Added NLP#2

app.py

@@ -232,46 +232,19 @@ def extract_expense():
                 extracted_text = "\n".join(extracted_lines)
                 main_amount_ocr = find_main_amount(ocr_result) # Keep OCR amount extraction
 
-            # --- NEW: Call NLP Function Directly ---
-            nlp_analysis_result = None
-            nlp_error = None
-            if extracted_text:
-                try:
-                    # Call the imported analysis function
-                    nlp_analysis_result = analyze_expense_text(extracted_text)
-                    print(f"NLP Service Analysis Result: {nlp_analysis_result}")
-                    # Check if the NLP analysis itself reported an error/failure
-                    if nlp_analysis_result.get("status") == "failed":
-                        nlp_error = nlp_analysis_result.get("message", "NLP processing failed")
-                        # Keep the result structure but note the failure
-                except Exception as nlp_e:
-                    nlp_error = f"Error calling NLP analysis function: {nlp_e}"
-                    print(f"Error calling NLP function: {nlp_error}")
-                    nlp_analysis_result = None # Ensure result is None on exception during call
-            else:
-                nlp_error = "No text extracted from image for NLP analysis."
-            # --- End NLP Call ---
-
-            # Construct the response
+            # --- REMOVED: NLP Call ---
+            # nlp_analysis_result = None
+            # nlp_error = None
+            # ... (removed NLP call logic) ...
+            # --- End Removed NLP Call ---
+
+            # Construct the response (only OCR results)
             response_data = {
                 "type": "photo",
                 "extracted_text": extracted_text,
                 "main_amount_ocr": main_amount_ocr, # Amount found by OCR regex logic
-                "nlp_analysis": nlp_analysis_result, # Include the full NLP analysis result (or None)
-                "nlp_error": nlp_error # Include any error from NLP call/processing
             }
 
-            # Optional: Add top-level convenience fields based on successful NLP analysis
-            if nlp_analysis_result and nlp_analysis_result.get("status") == "success":
-                if nlp_analysis_result.get("action") == "add_expense":
-                    response_data['confirmed_expense_details'] = nlp_analysis_result.get('details')
-                    response_data['confirmation_message'] = nlp_analysis_result.get('message')
-                elif nlp_analysis_result.get("action") == "query_expense":
-                    # Include query results if applicable (depends on nlp_service structure)
-                    response_data['query_message'] = nlp_analysis_result.get('message')
-                    response_data['query_criteria'] = nlp_analysis_result.get('criteria')
-                    response_data['query_results_count'] = nlp_analysis_result.get('results_count')
-
             return jsonify(response_data)
 
         except Exception as e:
@@ -285,6 +258,37 @@ def extract_expense():
 
     return jsonify({"error": "File processing failed"}), 500
 
+# --- NEW: NLP Message Endpoint ---
+@app.route('/message', methods=['POST'])
+def process_message():
+    data = request.get_json()
+    if not data or 'text' not in data:
+        return jsonify({"error": "Missing 'text' field in JSON payload"}), 400
+
+    text_message = data['text']
+    if not text_message:
+         return jsonify({"error": "'text' field cannot be empty"}), 400
+
+    nlp_analysis_result = None
+    nlp_error = None
+    try:
+        # Call the imported analysis function
+        nlp_analysis_result = analyze_expense_text(text_message)
+        print(f"NLP Service Analysis Result: {nlp_analysis_result}")
+        # Check if the NLP analysis itself reported an error/failure
+        if nlp_analysis_result.get("status") == "failed":
+            nlp_error = nlp_analysis_result.get("message", "NLP processing failed")
+            # Return the failure result from NLP service
+            return jsonify(nlp_analysis_result), 400 # Or 200 with error status? Let's use 200 for now.
+        
+        # Return the successful analysis result
+        return jsonify(nlp_analysis_result)
+
+    except Exception as nlp_e:
+        nlp_error = f"Error calling NLP analysis function: {nlp_e}"
+        print(f"Error calling NLP function: {nlp_error}")
+        return jsonify({"error": "An internal error occurred during NLP processing", "details": nlp_error}), 500
+
 # --- NEW: Health Check Endpoint ---
 @app.route('/health', methods=['GET'])
 def health_check():

nlp_service.py

@@ -673,6 +673,7 @@ def call_gemini_api(text, api_key):
     }
     # Construct the payload based on Gemini API requirements
     # This prompt asks Gemini to act like the existing NLP service
+    # Corrected indentation for the prompt string
     prompt = f"""Analyze the following text for expense tracking. Determine the intent ('add_expense' or 'query_expense') and extract relevant details.
 
 Text: "{text}"
@@ -721,6 +722,8 @@ Provide only the JSON output.
         logging.debug(f"Raw Gemini API response: {gemini_response_raw}")
 
         # --- Process gemini_response --- 
+        content = None # Initialize content to None
+        content_cleaned = None # Initialize content_cleaned to None
         # Extract the text content which should contain the JSON
         if 'candidates' in gemini_response_raw and len(gemini_response_raw['candidates']) > 0:
             content = gemini_response_raw['candidates'][0].get('content', {}).get('parts', [{}])[0].get('text')
@@ -738,10 +741,11 @@ Provide only the JSON output.
                         return parsed_result
                     else:
                         logging.warning("Gemini response parsed but lacks expected structure.")
+                        # Return info message if structure is wrong but content exists
                         return {"action": "info", "status": "success", "message": f"Gemini suggestion: {content_cleaned}"}
                 except json.JSONDecodeError as json_err:
                     logging.warning(f"Failed to decode JSON from Gemini response: {json_err}. Raw content: {content_cleaned}")
-                    # Return the raw text as a message if JSON parsing fails
+                    # Return the raw text as a message if JSON parsing fails but content exists
                     return {"action": "info", "status": "success", "message": f"Gemini suggestion: {content_cleaned}"}
             else:
                 logging.warning("No text content found in Gemini response candidates.")
@@ -763,7 +767,9 @@ Provide only the JSON output.
                 logging.error(f"Gemini API error response (non-JSON): {e.response.text}")
         return None
     except Exception as e:
-        logging.error(f"An unexpected error occurred during Gemini API call or processing: {e}")
+        # Include content_cleaned in the log if available during unexpected errors
+        error_context = f"Raw content (if available): {content_cleaned}" if content_cleaned else "No raw content parsed."
+        logging.error(f"An unexpected error occurred during Gemini API call or processing: {e}. {error_context}")
         return None