Update app.py

app.py

@@ -2,9 +2,14 @@ from flask import Flask, request, jsonify
 from transformers import pipeline
 from simple_salesforce import Salesforce
 import datetime
+import logging
 
 app = Flask(__name__)
 
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
 # Hardcode Salesforce credentials
 SF_USERNAME = "app@coach.com"
 SF_PASSWORD = "Geetha@1"
@@ -15,7 +20,7 @@ SF_DOMAIN = "login"
 try:
     generator = pipeline("text-generation", model="distilgpt2")
 except Exception as e:
-    print(f"Error initializing Hugging Face model: {str(e)}")
+    logger.error(f"Error initializing Hugging Face model: {str(e)}")
     generator = None
 
 # Initialize Salesforce connection with hardcoded credentials
@@ -27,12 +32,13 @@ try:
         domain=SF_DOMAIN
     )
 except Exception as e:
-    print(f"Error connecting to Salesforce: {str(e)}")
+    logger.error(f"Error connecting to Salesforce: {str(e)}")
     sf = None
 
 # Add a default route for the root URL
 @app.route('/')
 def home():
+    logger.info("Received request to root URL")
     return jsonify({
         "status": "running",
         "message": "This is the AI Coach App backend API. Use the /generate-ai-data endpoint to generate AI coaching data and reports.",
@@ -71,14 +77,18 @@ def generate_ai_data():
     - AI_Coaching_Data__c: Daily_Checklist__c, Suggested_Tips__c, Risk_Alerts__c, Upcoming_Milestones__c, Performance_Trends__c, Generated_Date__c
     - Report_Download__c: Report_Type__c, Report_Data__c, Download_Link__c, Generated_Date__c
     """
+    logger.info("Received request to /generate-ai-data endpoint")
+
     # Check if dependencies are initialized
     if generator is None:
+        logger.error("Hugging Face model failed to initialize")
         return jsonify({
             "status": "error",
             "message": "Hugging Face model failed to initialize."
         }), 500
 
     if sf is None:
+        logger.error("Salesforce connection failed")
         return jsonify({
             "status": "error",
             "message": "Salesforce connection failed. Check credentials in app.py."
@@ -88,6 +98,7 @@ def generate_ai_data():
         # Extract and validate inputs from the POST request
         data = request.get_json()
         if not data:
+            logger.error("No data provided in the request")
             return jsonify({
                 "status": "error",
                 "message": "No data provided in the request."
@@ -97,6 +108,7 @@ def generate_ai_data():
         required_fields = ['supervisor_id', 'project_id', 'supervisor_data', 'project_data']
         for field in required_fields:
             if field not in data or not data[field]:
+                logger.error(f"Missing or empty required field: {field}")
                 return jsonify({
                     "status": "error",
                     "message": f"Missing or empty required field: {field}"
@@ -111,6 +123,7 @@ def generate_ai_data():
         required_supervisor_fields = ['Role__c', 'Location__c']
         for field in required_supervisor_fields:
             if field not in supervisor_data or not supervisor_data[field]:
+                logger.error(f"Missing or empty field in supervisor_data: {field}")
                 return jsonify({
                     "status": "error",
                     "message": f"Missing or empty field in supervisor_data: {field}"
@@ -120,6 +133,7 @@ def generate_ai_data():
         required_project_fields = ['Name', 'Start_Date__c', 'End_Date__c', 'Milestones__c', 'Project_Schedule__c']
         for field in required_project_fields:
             if field not in project_data or not project_data[field]:
+                logger.error(f"Missing or empty field in project_data: {field}")
                 return jsonify({
                     "status": "error",
                     "message": f"Missing or empty field in project_data: {field}"
@@ -130,9 +144,10 @@ def generate_ai_data():
             checklists = sf.query(
                 f"SELECT Task_Name__c, Status__c FROM Daily_Checklist__c WHERE Project_ID__c = '{project_id}' AND Date__c = TODAY"
             )['records']
+            logger.info(f"Fetched {len(checklists)} checklists for project {project_id}")
         except Exception as e:
             checklists = []
-            print(f"Error querying Daily_Checklist__c: {str(e)}")
+            logger.warning(f"Error querying Daily_Checklist__c: {str(e)}")
 
         completed_tasks = [c['Task_Name__c'] for c in checklists if c.get('Status__c') == 'Completed']
         pending_tasks = [c['Task_Name__c'] for c in checklists if c.get('Status__c') == 'Pending']
@@ -151,14 +166,16 @@ def generate_ai_data():
         # Generate AI output
         try:
             ai_response = generator(prompt, max_length=500, num_return_sequences=1)[0]['generated_text']
+            logger.info("Successfully generated AI response")
         except Exception as e:
+            logger.error(f"Error generating AI response: {str(e)}")
             return jsonify({
                 "status": "error",
                 "message": f"Error generating AI response: {str(e)}"
             }), 500
 
         # Parse AI response (more dynamic parsing based on project progress)
-        today = datetime.datetime.now().strftime('%Y-%m-%d')
+        today = "2025-05-22"  # Hardcoded to current date (May 22, 2025)
         daily_checklist = (
             f"1. Review pending tasks from yesterday (General, Pending)\n"
             f"2. Conduct daily safety inspection for {project_data['Name']} (Safety, Pending)\n"
@@ -190,7 +207,9 @@ def generate_ai_data():
         }
         try:
             sf.AI_Coaching_Data__c.create(ai_data)
+            logger.info(f"Successfully saved AI data to AI_Coaching_Data__c for project {project_id}")
         except Exception as e:
+            logger.error(f"Error saving to AI_Coaching_Data__c: {str(e)}")
             return jsonify({
                 "status": "error",
                 "message": f"Error saving to AI_Coaching_Data__c: {str(e)}"
@@ -208,12 +227,15 @@ def generate_ai_data():
         }
         try:
             sf.Report_Download__c.create(report_data)
+            logger.info(f"Successfully saved report to Report_Download__c for project {project_id}")
         except Exception as e:
+            logger.error(f"Error saving to Report_Download__c: {str(e)}")
             return jsonify({
                 "status": "error",
                 "message": f"Error saving to Report_Download__c: {str(e)}"
             }), 500
 
+        logger.info("Successfully processed request to /generate-ai-data")
         return jsonify({
             "status": "success",
             "message": "AI data and report generated successfully",
@@ -222,6 +244,7 @@ def generate_ai_data():
         })
 
     except Exception as e:
+        logger.error(f"Error generating AI data: {str(e)}")
         return jsonify({
             "status": "error",
             "message": f"Error generating AI data: {str(e)}"