Update app.py

app.py

@@ -4,34 +4,19 @@ from simple_salesforce import Salesforce
 import datetime
 import os
 from dotenv import load_dotenv
-import logging
-
-# Configure logging for better debugging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
 
 # Load environment variables from .env file
 load_dotenv()
 
 # Initialize Hugging Face model
-try:
-    logger.info("Initializing distilgpt2 model...")
-    generator = pipeline("text-generation", model="distilgpt2")
-except Exception as e:
-    logger.error(f"Failed to initialize model: {str(e)}")
-    raise RuntimeError(f"Model initialization failed: {str(e)}. Ensure PyTorch or TensorFlow is installed.")
+generator = pipeline("text-generation", model="distilgpt2")
 
 # Initialize Salesforce connection using environment variables
-try:
-    logger.info("Connecting to Salesforce...")
-    sf = Salesforce(
-        username=os.getenv("SF_USERNAME"),
-        password=os.getenv("SF_PASSWORD"),
-        security_token=os.getenv("SF_SECURITY_TOKEN")
-    )
-except Exception as e:
-    logger.error(f"Salesforce connection failed: {str(e)}")
-    raise RuntimeError(f"Salesforce connection failed: {str(e)}")
+sf = Salesforce(
+    username=os.getenv("SF_USERNAME"),
+    password=os.getenv("SF_PASSWORD"),
+    security_token=os.getenv("SF_SECURITY_TOKEN")
+)
 
 def generate_ai_data(supervisor_id, project_id, supervisor_data, project_data):
     """
@@ -56,10 +41,10 @@ def generate_ai_data(supervisor_id, project_id, supervisor_data, project_data):
         )
 
         # Generate AI output
-        logger.info("Generating AI response...")
         ai_response = generator(prompt, max_length=500, num_return_sequences=1)[0]['generated_text']
 
         # Parse AI response (simplified parsing for this example)
+        # In a real scenario, you'd use more sophisticated NLP to extract structured data
         daily_checklist = (
             "1. Conduct safety inspection of site (Safety, Pending)\n"
             "2. Ensure team wears protective gear (Safety, Pending)\n"
@@ -75,7 +60,6 @@ def generate_ai_data(supervisor_id, project_id, supervisor_data, project_data):
         performance_trends = "Task completion rate: 75% this week (initial estimate)."
 
         # Save AI data to AI_Coaching_Data__c
-        logger.info("Saving AI data to Salesforce...")
         ai_data = {
             'Supervisor_ID__c': supervisor_id,
             'Project_ID__c': project_id,
@@ -89,7 +73,6 @@ def generate_ai_data(supervisor_id, project_id, supervisor_data, project_data):
         sf.AI_Coaching_Data__c.create(ai_data)
 
         # Generate a report for Report_Download__c
-        logger.info("Generating report for Salesforce...")
         report_data = {
             'Supervisor_ID__c': supervisor_id,
             'Project_ID__c': project_id,
@@ -100,7 +83,6 @@ def generate_ai_data(supervisor_id, project_id, supervisor_data, project_data):
         }
         sf.Report_Download__c.create(report_data)
 
-        logger.info("AI data and report generated successfully")
         return {
             "status": "success",
             "message": "AI data and report generated successfully",
@@ -109,14 +91,12 @@ def generate_ai_data(supervisor_id, project_id, supervisor_data, project_data):
         }
 
     except Exception as e:
-        logger.error(f"Error generating AI data: {str(e)}")
         return {
             "status": "error",
             "message": f"Error generating AI data: {str(e)}"
         }
 
 # Create Gradio interface
-logger.info("Setting up Gradio interface...")
 iface = gr.Interface(
     fn=generate_ai_data,
     inputs=[
@@ -132,5 +112,4 @@ iface = gr.Interface(
 
 # Launch the Gradio app
 if __name__ == "__main__":
-    logger.info("Launching Gradio app...")
     iface.launch(server_name="0.0.0.0", server_port=7860)