Update app.py

app.py

@@ -4,13 +4,11 @@ import plotly.express as px
 from sklearn.ensemble import IsolationForest
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
-from apscheduler.schedulers.background import BackgroundScheduler
 from transformers import pipeline
 import datetime
 import os
 import logging
 import re
-import atexit
 import numpy as np
 import torch
 import tempfile
@@ -50,9 +48,8 @@ except Exception as e:
     logger.error(f"Failed to initialize Hugging Face pipeline: {e}")
     summarizer = None
 
-# Global variables to store data
+# Global variable to store logs data
 logs = pd.DataFrame()
-amc_data = pd.DataFrame()
 
 # Load logs from uploaded CSV file
 def load_logs(logs_file):
@@ -84,41 +81,10 @@ def load_logs(logs_file):
         logger.error(f"Error loading logs: {e}")
         return pd.DataFrame()
 
-# Load AMC data from uploaded CSV file (optional)
-def load_amc_data(amc_file):
-    global amc_data
-    try:
-        if amc_file is None:
-            logger.info("No AMC data CSV file uploaded; proceeding without AMC data")
-            return pd.DataFrame()
-        # Read CSV from uploaded file
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as tmp:
-            tmp.write(amc_file.read())
-            tmp_path = tmp.name
-        amc_data = pd.read_csv(tmp_path)
-        os.unlink(tmp_path)  # Clean up temporary file
-
-        # Validate required columns
-        required_columns = ['device_id', 'expiry_date']
-        if not all(col in amc_data.columns for col in required_columns):
-            logger.error(f"Missing required columns in AMC data: {required_columns}")
-            return pd.DataFrame()
-
-        amc_data['expiry_date'] = pd.to_datetime(amc_data['expiry_date'], errors='coerce')
-        if amc_data['expiry_date'].isna().any():
-            logger.error("Invalid expiry dates detected in AMC data")
-            return pd.DataFrame()
-        logger.info("AMC data loaded successfully from uploaded CSV")
-        return amc_data
-    except Exception as e:
-        logger.error(f"Error loading AMC data: {e}")
-        return pd.DataFrame()
-
 # Update dropdowns and validate uploads
-def update_dropdowns(logs_file, amc_file):
-    global logs, amc_data
+def update_dropdowns(logs_file):
+    global logs
     logs = load_logs(logs_file)
-    amc_data = load_amc_data(amc_file)
     
     if logs.empty:
         return (
@@ -128,13 +94,9 @@ def update_dropdowns(logs_file, amc_file):
             False  # Disable Generate Dashboard button
         )
     
-    lab_choices = logs['lab_id'].unique().tolist()
-    equipment_choices = logs['type'].unique().tolist()
+    lab_choices = sorted(logs['lab_id'].unique().tolist())  # Sort for better UX
+    equipment_choices = sorted(logs['type'].unique().tolist())  # Sort for better UX
     upload_status = "<p style='color: green;'>Logs loaded successfully.</p>"
-    if amc_data.empty:
-        upload_status += "<p style='color: orange;'>No AMC data uploaded; AMC expiry alerts will be unavailable.</p>"
-    else:
-        upload_status += "<p style='color: green;'>AMC data loaded successfully.</p>"
     
     return (
         gr.Dropdown(choices=lab_choices, value=lab_choices[0] if lab_choices else None, label="Select Lab"),
@@ -158,26 +120,6 @@ def detect_anomalies(logs):
         logger.error(f"Error in anomaly detection: {e}")
         return logs
 
-# AMC expiry checker
-def check_amc_expiry():
-    try:
-        if amc_data.empty:
-            logger.info("No AMC data available; skipping expiry check")
-            return None
-        today = pd.to_datetime("today").normalize()
-        expiries = amc_data[amc_data['expiry_date'] <= (today + pd.Timedelta(days=14))]
-        logger.info("AMC expiry check completed")
-        return expiries if not expiries.empty else None
-    except Exception as e:
-        logger.error(f"Error in AMC expiry check: {e}")
-        return None
-
-# Scheduler
-scheduler = BackgroundScheduler()
-scheduler.add_job(check_amc_expiry, 'interval', days=1)
-scheduler.start()
-atexit.register(lambda: scheduler.shutdown())
-
 # Generate text summary using Hugging Face
 def generate_text_summary(logs):
     if summarizer is None or logs.empty:
@@ -196,7 +138,7 @@ def generate_text_summary(logs):
         return f"Error generating summary: {str(e)}"
 
 # Generate executive insights
-def generate_executive_insights(logs, anomalies, amc_expiries):
+def generate_executive_insights(logs, anomalies):
     if summarizer is None or logs.empty:
         return "No executive insights available due to missing data or model initialization."
     try:
@@ -209,17 +151,12 @@ def generate_executive_insights(logs, anomalies, amc_expiries):
             f"Device {row['device_id']} showed anomalous usage count {row['usage_count']} on {row['timestamp'].strftime('%Y-%m-%d %H:%M:%S')}."
             for _, row in anomalies.iterrows()
         ) if not anomalies.empty else "No anomalies detected."
-        amc_text = "\n".join(
-            f"Device {row['device_id']} has AMC expiring on {row['expiry_date'].strftime('%Y-%m-%d')}."
-            for _, row in amc_expiries.iterrows()
-        ) if amc_expiries is not None else "No AMC expiries within 2 weeks."
         
         prompt = (
             f"Summarize the following lab operations data into concise executive insights:\n\n"
             f"Logs:\n{log_text}\n\n"
             f"Anomalies:\n{anomaly_text}\n\n"
-            f"AMC Expiries:\n{amc_text}\n\n"
-            f"Provide high-level insights for lab managers, focusing on operational status, issues, and upcoming maintenance needs."
+            f"Provide high-level insights for lab managers, focusing on operational status and issues."
         )
         
         insights = summarizer(prompt, max_length=200, min_length=50, do_sample=False)[0]['summary_text']
@@ -306,8 +243,7 @@ def render_dashboard(lab, equipment_type, start_date, end_date):
             return (
                 "<p style='color: red;'>Please select both Lab and Equipment Type.</p>",
                 None, None, "<p style='color: red;'>Invalid input</p>",
-                "<p style='color: red;'>Invalid input</p>", None,
-                "No summary available.", "No insights available.",
+                None, "No summary available.", "No insights available.",
                 "\n".join(log_messages[-10:]) or "No logs available."
             )
 
@@ -338,8 +274,7 @@ def render_dashboard(lab, equipment_type, start_date, end_date):
             return (
                 "<p style='color: orange;'>No devices found for the selected filters.</p>",
                 None, None, "<p>No anomalies detected.</p>",
-                "<p>No AMC expiries within 2 weeks.</p>", None,
-                "No summary available.", "No insights available.",
+                None, "No summary available.", "No insights available.",
                 "\n".join(log_messages[-10:]) or "No logs available."
             )
 
@@ -396,17 +331,9 @@ def render_dashboard(lab, equipment_type, start_date, end_date):
             border=0
         ) if not anomalies.empty else "<p>No anomalies detected.</p>"
 
-        # AMC Expiry Alerts
-        amc_expiries = check_amc_expiry()
-        amc_alerts = amc_expiries[['device_id', 'expiry_date']].to_html(
-            index=False,
-            classes="table table-striped",
-            border=0
-        ) if amc_expiries is not None else "<p>No AMC expiries within 2 weeks.</p>"
-
         # Generate summary and insights
         summary = generate_text_summary(filtered_logs)
-        insights = generate_executive_insights(filtered_logs, anomalies, amc_expiries)
+        insights = generate_executive_insights(filtered_logs, anomalies)
 
         # PDF Download
         pdf_data = generate_pdf(lab, equipment_type, filtered_logs, summary, insights)
@@ -420,7 +347,6 @@ def render_dashboard(lab, equipment_type, start_date, end_date):
             fig_usage,
             fig_uptime,
             anomaly_table,
-            amc_alerts,
             pdf_data,
             summary,
             insights,
@@ -434,7 +360,6 @@ def render_dashboard(lab, equipment_type, start_date, end_date):
             None,
             None,
             "<p style='color: red;'>Error</p>",
-            "<p style='color: red;'>Error</p>",
             None,
             "Error generating summary.",
             "Error generating insights.",
@@ -456,11 +381,9 @@ with gr.Blocks(
     
     # File Upload
     with gr.Group():
-        gr.Markdown("### Upload Data Files")
-        gr.Markdown("**Note**: Uploading logs.csv is required. Uploading amc_data.csv is optional for AMC expiry alerts.")
-        with gr.Row():
-            logs_file = gr.File(label="Upload Logs CSV (Required)", file_types=[".csv"])
-            amc_file = gr.File(label="Upload AMC Data CSV (Optional)", file_types=[".csv"])
+        gr.Markdown("### Upload Data File")
+        gr.Markdown("**Note**: Please upload a logs.csv file to proceed.")
+        logs_file = gr.File(label="Upload Logs CSV (Required)", file_types=[".csv"])
         upload_btn = gr.Button("Load Data", variant="primary", elem_classes="submit-btn")
         upload_status = gr.HTML(label="Upload Status")
 
@@ -507,8 +430,6 @@ with gr.Blocks(
                 uptime_chart = gr.Plot(label="Weekly Uptime %")
             with gr.Tab("Anomaly Alerts"):
                 anomaly_table = gr.HTML(label="Anomaly Alerts")
-            with gr.Tab("AMC Expiry Alerts"):
-                amc_alerts = gr.HTML(label="AMC Expiry Alerts")
             with gr.Tab("Summary"):
                 summary_output = gr.Textbox(label="Log Summary", lines=5, interactive=False)
             with gr.Tab("Executive Insights"):
@@ -519,13 +440,13 @@ with gr.Blocks(
     # Update dropdowns on file upload
     upload_btn.click(
         fn=update_dropdowns,
-        inputs=[logs_file, amc_file],
+        inputs=[logs_file],
         outputs=[lab, equipment_type, upload_status, submit_btn]
     )
 
     # Update dashboard on submit
     inputs = [lab, equipment_type, start_date, end_date]
-    outputs = [device_cards, usage_chart, uptime_chart, anomaly_table, amc_alerts, pdf_download, summary_output, insights_output, log_display]
+    outputs = [device_cards, usage_chart, uptime_chart, anomaly_table, pdf_download, summary_output, insights_output, log_display]
     submit_btn.click(render_dashboard, inputs=inputs, outputs=outputs)
 
 # Launch Gradio app