Update app.py

app.py

@@ -1,15 +1,25 @@
 from flask import Flask, request, jsonify
 from simple_salesforce import Salesforce
 import pandas as pd
-from datetime import datetime
+from datetime import datetime, timedelta
 import logging
 from sklearn.ensemble import IsolationForest
 from transformers import pipeline
 import torch
 import os
+import time
+import requests
+from requests.exceptions import Timeout
 
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+# Configure logging to a file and console for better debugging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler('app.log'),  # Log to a file for persistence
+        logging.StreamHandler()          # Log to console for real-time visibility
+    ]
+)
 
 # Initialize Flask app
 app = Flask(__name__)
@@ -20,32 +30,64 @@ SF_PASSWORD = os.getenv('SF_PASSWORD', 'your_salesforce_password')
 SF_SECURITY_TOKEN = os.getenv('SF_SECURITY_TOKEN', 'your_security_token')
 SF_INSTANCE_URL = os.getenv('SF_INSTANCE_URL', 'https://login.salesforce.com')
 
-# Connect to Salesforce
-try:
-    sf = Salesforce(
-        username=SF_USERNAME,
-        password=SF_PASSWORD,
-        security_token=SF_SECURITY_TOKEN,
-        instance_url=SF_INSTANCE_URL
-    )
-    logging.info("Connected to Salesforce successfully")
-except Exception as e:
-    logging.error(f"Failed to connect to Salesforce: {str(e)}")
-    raise e
-
-# Preload Hugging Face summarization model
-logging.info("Preloading Hugging Face model...")
-try:
-    device = 0 if torch.cuda.is_available() else -1
-    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", device=device)
-    logging.info(f"Hugging Face model preloaded successfully on device: {'GPU' if device == 0 else 'CPU'}")
-except Exception as e:
-    logging.error(f"Failed to preload model: {str(e)}")
-    raise e
+# Global variable for Salesforce connection
+sf = None
+
+# Global variable for Hugging Face model (lazy initialization)
+summarizer = None
+
+# Health check endpoint to confirm the app is running
+@app.route('/health', methods=['GET'])
+def health_check():
+    return jsonify({"status": "App is running"}), 200
+
+# Connect to Salesforce with a timeout
+def connect_to_salesforce():
+    global sf
+    logging.info("Attempting to connect to Salesforce...")
+    start_time = time.time()
+    try:
+        # Use a timeout to prevent hanging
+        session = requests.Session()
+        adapter = requests.adapters.HTTPAdapter(max_retries=3)
+        session.mount('https://', adapter)
+        session.request('GET', SF_INSTANCE_URL, timeout=10)  # Test connectivity
+
+        sf = Salesforce(
+            username=SF_USERNAME,
+            password=SF_PASSWORD,
+            security_token=SF_SECURITY_TOKEN,
+            instance_url=SF_INSTANCE_URL,
+            session=session
+        )
+        logging.info(f"Connected to Salesforce successfully in {time.time() - start_time:.2f} seconds")
+    except Timeout:
+        logging.error("Salesforce connection timed out after 10 seconds")
+        sf = None
+    except Exception as e:
+        logging.error(f"Failed to connect to Salesforce: {str(e)}")
+        sf = None
+
+# Lazy load the Hugging Face model
+def load_huggingface_model():
+    global summarizer
+    if summarizer is None:
+        logging.info("Loading Hugging Face model...")
+        start_time = time.time()
+        try:
+            device = 0 if torch.cuda.is_available() else -1
+            summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", device=device)
+            logging.info(f"Hugging Face model loaded successfully in {time.time() - start_time:.2f} seconds on device: {'GPU' if device == 0 else 'CPU'}")
+        except Exception as e:
+            logging.error(f"Failed to load Hugging Face model: {str(e)}")
+            summarizer = None
 
 # Fetch SmartLog records from Salesforce
 def fetch_smartlog_records(lab_site, start_date, end_date, equipment_type):
+    if sf is None:
+        raise Exception("Salesforce connection not established")
     try:
+        logging.info("Fetching SmartLog records from Salesforce...")
         query = "SELECT Device_Id__c, Log_Type__c, Status__c, Timestamp__c, Usage_Hours__c, Downtime__c, AMC_Date__c FROM SmartLog__c WHERE "
         conditions = []
         params = {}
@@ -86,6 +128,7 @@ def fetch_smartlog_records(lab_site, start_date, end_date, equipment_type):
         df = pd.DataFrame(data)
         df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')
         df['amc_date'] = pd.to_datetime(df['amc_date'], errors='coerce')
+        logging.info(f"Fetched {len(df)} SmartLog records")
         return df
     except Exception as e:
         logging.error(f"Failed to fetch SmartLog records: {str(e)}")
@@ -93,6 +136,9 @@ def fetch_smartlog_records(lab_site, start_date, end_date, equipment_type):
 
 # Format summary prompt and generate report
 def summarize_logs(df):
+    load_huggingface_model()
+    if summarizer is None:
+        return "Failed to load summarization model."
     try:
         total_devices = df["device_id"].nunique()
         most_used = df.groupby("device_id")["usage_hours"].sum().idxmax() if not df.empty else "N/A"
@@ -153,6 +199,9 @@ def check_amc_reminders(df, current_date):
 
 # Dashboard Insights
 def generate_dashboard_insights(df):
+    load_huggingface_model()
+    if summarizer is None:
+        return "Failed to load summarization model."
     try:
         total_devices = df["device_id"].nunique()
         avg_usage = df["usage_hours"].mean() if "usage_hours" in df.columns else 0
@@ -185,6 +234,9 @@ def create_usage_chart_data(df):
 @app.route('/process_logs', methods=['POST'])
 def process_logs():
     try:
+        if sf is None:
+            return jsonify({"error": "Salesforce connection not established. Check server logs for details."}), 500
+
         data = request.get_json()
         lab_site = data.get('lab_site')
         start_date = data.get('start_date')
@@ -245,4 +297,8 @@ def process_logs():
         return jsonify({"error": str(e)}), 500
 
 if __name__ == "__main__":
+    logging.info("Starting Flask application...")
+    start_time = time.time()
+    connect_to_salesforce()  # Attempt to connect to Salesforce at startup
+    logging.info(f"Flask application startup completed in {time.time() - start_time:.2f} seconds")
     app.run(host="0.0.0.0", port=5000, debug=True)