Update app.py

app.py

@@ -6,35 +6,54 @@ from simple_salesforce import Salesforce
 from transformers import pipeline
 from utils import fetch_salesforce_data, detect_anomalies, generate_pdf_report
 import os
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
 
 # Streamlit app configuration
-st.set_page_config(page_title="LabOps Dashboard", layout="wide")
+try:
+    st.set_page_config(page_title="LabOps Dashboard", layout="wide")
+    logger.info("Streamlit page configuration set successfully.")
+except Exception as e:
+    logger.error(f"Failed to set Streamlit page configuration: {e}")
+    raise
 
 # Cache Salesforce connection
 @st.cache_resource
 def init_salesforce():
+    logger.info("Initializing Salesforce connection...")
     try:
-        return Salesforce(
+        sf = Salesforce(
             username=os.getenv("SF_USERNAME", st.secrets.get("sf_username")),
             password=os.getenv("SF_PASSWORD", st.secrets.get("sf_password")),
             security_token=os.getenv("SF_SECURITY_TOKEN", st.secrets.get("sf_security_token"))
         )
+        logger.info("Salesforce connection initialized successfully.")
+        return sf
     except Exception as e:
-        st.error(f"Failed to connect to Salesforce: {e}")
+        logger.error(f"Failed to initialize Salesforce: {e}")
+        st.error(f"Cannot connect to Salesforce: {e}")
         return None
 
 # Cache Hugging Face model
 @st.cache_resource
 def init_anomaly_detector():
+    logger.info("Initializing anomaly detector...")
     try:
-        return pipeline(
+        # Use lighter model for Hugging Face Spaces
+        detector = pipeline(
             "text-classification",
-            model="distilbert-base-uncased",
-            tokenizer="distilbert-base-uncased",
+            model="prajjwal1/bert-tiny",
+            tokenizer="prajjwal1/bert-tiny",
             clean_up_tokenization_spaces=True
         )
+        logger.info("Anomaly detector initialized successfully.")
+        return detector
     except Exception as e:
-        st.error(f"Failed to initialize anomaly detector: {e}")
+        logger.error(f"Failed to initialize anomaly detector: {e}")
+        st.error(f"Cannot initialize anomaly detector: {e}")
         return None
 
 # Initialize connections
@@ -42,24 +61,33 @@ sf = init_salesforce()
 anomaly_detector = init_anomaly_detector()
 
 # Cache data fetching
-@st.cache_data(ttl=10)  # Cache for 10 seconds to meet refresh requirement
+@st.cache_data(ttl=10)
 def get_filtered_data(lab_site, equipment_type, date_start, date_end):
-    query = f"""
-        SELECT Equipment__c, Log_Timestamp__c, Status__c, Usage_Count__c, Lab__c, Equipment_Type__c
-        FROM SmartLog__c
-        WHERE Log_Timestamp__c >= {date_start.strftime('%Y-%m-%d')}
-        AND Log_Timestamp__c <= {date_end.strftime('%Y-%m-%d')}
-    """
-    if lab_site != "All":
-        query += f" AND Lab__c = '{lab_site}'"
-    if equipment_type != "All":
-        query += f" AND Equipment_Type__c = '{equipment_type}'"
-    query += " LIMIT 100"  # Reduced for faster load in Hugging Face Spaces
-    return fetch_salesforce_data(sf, query)
+    logger.info(f"Fetching data for lab: {lab_site}, equipment: {equipment_type}, date range: {date_start} to {date_end}")
+    try:
+        query = f"""
+            SELECT Equipment__c, Log_Timestamp__c, Status__c, Usage_Count__c, Lab__c, Equipment_Type__c
+            FROM SmartLog__c
+            WHERE Log_Timestamp__c >= {date_start.strftime('%Y-%m-%d')}
+            AND Log_Timestamp__c <= {date_end.strftime('%Y-%m-%d')}
+        """
+        if lab_site != "All":
+            query += f" AND Lab__c = '{lab_site}'"
+        if equipment_type != "All":
+            query += f" AND Equipment_Type__c = '{equipment_type}'"
+        query += " LIMIT 100"
+        data = fetch_salesforce_data(sf, query)
+        logger.info(f"Fetched {len(data)} records from Salesforce.")
+        return data
+    except Exception as e:
+        logger.error(f"Failed to fetch data: {e}")
+        return []
 
 def main():
+    logger.info("Starting main application...")
     if sf is None or anomaly_detector is None:
-        st.error("Cannot proceed without Salesforce connection or anomaly detector.")
+        st.error("Application cannot start due to initialization failures. Check logs for details.")
+        logger.error("Application initialization failed: Salesforce or anomaly detector not available.")
         return
 
     st.title("Multi-Device LabOps Dashboard")
@@ -73,9 +101,9 @@ def main():
     
     date_range = st.date_input("Date Range", [datetime.now() - timedelta(days=7), datetime.now()])
     
-    # Validate date range
     if len(date_range) != 2:
         st.warning("Please select a valid date range.")
+        logger.warning("Invalid date range selected.")
         return
     date_start, date_end = date_range
 
@@ -84,6 +112,7 @@ def main():
         data = get_filtered_data(lab_site, equipment_type, date_start, date_end)
     if not data:
         st.warning("No data available for the selected filters.")
+        logger.warning("No data returned for the selected filters.")
         return
     
     df = pd.DataFrame(data)
@@ -156,8 +185,15 @@ def main():
                 pdf_file = generate_pdf_report(df, lab_site, equipment_type, [date_start, date_end])
                 with open(pdf_file, "rb") as f:
                     st.download_button("Download PDF", f, file_name="LabOps_Report.pdf", mime="application/pdf")
+                logger.info("PDF report generated successfully.")
             except Exception as e:
                 st.error(f"Failed to generate PDF: {e}")
+                logger.error(f"Failed to generate PDF: {e}")
 
 if __name__ == "__main__":
-    main()
+    try:
+        logger.info("Application starting...")
+        main()
+    except Exception as e:
+        logger.error(f"Application failed to start: {e}")
+        raise