Update app.py

app.py

@@ -13,9 +13,8 @@ logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(
 # Preload Hugging Face summarization model at startup
 logging.info("Preloading Hugging Face model...")
 try:
-    # Check for GPU availability using torch.cuda
     device = 0 if torch.cuda.is_available() else -1
-    summarizer = pipeline("text2text-generation", model="google/flan-t5-base", device=device)  # Use GPU if available
+    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", device=device)  # Lighter model
     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)}")
@@ -26,15 +25,10 @@ def summarize_logs(df, progress=gr.Progress()):
     progress(0.1, "Generating summary report...")
     try:
         total_devices = df["device_id"].nunique()
-        avg_uptime = "97%"  # Placeholder
         most_used = df.groupby("device_id")["usage_hours"].sum().idxmax() if not df.empty else "N/A"
 
-        prompt = (
-            f"Summarize maintenance and usage logs. "
-            f"There were {total_devices} devices. "
-            f"The most used device was {most_used}."
-        )
-        summary = summarizer(prompt, max_length=200, do_sample=False)[0]["generated_text"]
+        prompt = f"Maintenance logs: {total_devices} devices. Most used: {most_used}."
+        summary = summarizer(prompt, max_length=50, min_length=10, do_sample=False)[0]["summary_text"]
         logging.info("Summary generated successfully")
         return summary
     except Exception as e:
@@ -49,13 +43,12 @@ def detect_anomalies(df, progress=gr.Progress()):
             logging.warning("Required columns for anomaly detection not found")
             return "Anomaly detection requires 'usage_hours' and 'downtime' columns."
 
-        # Sample data if too large
-        if len(df) > 10000:  # Adjust threshold as needed
-            df = df.sample(n=10000, random_state=42)
-            logging.info("Sampled data for anomaly detection to 10,000 rows")
+        if len(df) > 5000:
+            df = df.sample(n=5000, random_state=42)
+            logging.info("Sampled data for anomaly detection to 5,000 rows")
 
         features = df[["usage_hours", "downtime"]].fillna(0)
-        iso_forest = IsolationForest(contamination=0.1, random_state=42, n_jobs=-1)  # Use all CPU cores
+        iso_forest = IsolationForest(contamination=0.1, random_state=42, n_jobs=-1)
         df["anomaly"] = iso_forest.fit_predict(features)
         
         anomalies = df[df["anomaly"] == -1][["device_id", "usage_hours", "downtime", "timestamp"]]
@@ -63,12 +56,8 @@ def detect_anomalies(df, progress=gr.Progress()):
             return "No anomalies detected."
         
         anomaly_lines = ["**Detected Anomalies:**"]
-        for idx, row in anomalies.iterrows():
-            anomaly_lines.append(f"- Device ID: {row['device_id']}")
-            anomaly_lines.append(f"  Usage Hours: {row['usage_hours']}")
-            anomaly_lines.append(f"  Downtime: {row['downtime']}")
-            anomaly_lines.append(f"  Timestamp: {row['timestamp']}")
-            anomaly_lines.append("---")
+        for idx, row in anomalies.head(5).iterrows():
+            anomaly_lines.append(f"- Device ID: {row['device_id']}, Usage Hours: {row['usage_hours']}, Downtime: {row['downtime']}, Timestamp: {row['timestamp']}")
         anomaly_list = "\n".join(anomaly_lines)
         logging.info("Anomalies detected successfully")
         return anomaly_list
@@ -94,10 +83,8 @@ def check_amc_reminders(df, current_date, progress=gr.Progress()):
             return "No AMC reminders due within the next 30 days."
         
         reminder_lines = ["**Upcoming AMC Reminders:**"]
-        for idx, row in reminders.iterrows():
-            reminder_lines.append(f"- Device ID: {row['device_id']}")
-            reminder_lines.append(f"  AMC Date: {row['amc_date']}")
-            reminder_lines.append("---")
+        for idx, row in reminders.head(5).iterrows():
+            reminder_lines.append(f"- Device ID: {row['device_id']}, AMC Date: {row['amc_date']}")
         reminder_list = "\n".join(reminder_lines)
         logging.info("AMC reminders generated successfully")
         return reminder_list
@@ -111,11 +98,8 @@ def generate_dashboard_insights(df, progress=gr.Progress()):
     try:
         total_devices = df["device_id"].nunique()
         avg_usage = df["usage_hours"].mean() if "usage_hours" in df.columns else 0
-        prompt = (
-            f"Generate executive-level insights. "
-            f"There were {total_devices} devices with an average usage of {avg_usage:.2f} hours."
-        )
-        insights = summarizer(prompt, max_length=150, do_sample=False)[0]["generated_text"]
+        prompt = f"Insights: {total_devices} devices, avg usage {avg_usage:.2f} hours."
+        insights = summarizer(prompt, max_length=50, min_length=10, do_sample=False)[0]["summary_text"]
         logging.info("Dashboard insights generated successfully")
         return insights
     except Exception as e:
@@ -126,11 +110,10 @@ def generate_dashboard_insights(df, progress=gr.Progress()):
 def create_usage_chart(df, progress=gr.Progress()):
     progress(0.9, "Creating usage chart...")
     try:
-        # Limit the data for chart to top 10 devices to reduce load
         usage_data = df.groupby("device_id")["usage_hours"].sum().reset_index()
-        if len(usage_data) > 10:
-            usage_data = usage_data.nlargest(10, "usage_hours")
-            logging.info("Limited chart data to top 10 devices")
+        if len(usage_data) > 5:
+            usage_data = usage_data.nlargest(5, "usage_hours")
+            logging.info("Limited chart data to top 5 devices")
 
         custom_colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4']
         fig = px.bar(
@@ -155,7 +138,7 @@ def create_usage_chart(df, progress=gr.Progress()):
         return None
 
 # Main Gradio function
-def process_logs(file_obj, progress=gr.Progress()):
+async def process_logs(file_obj, row_limit=10000, progress=gr.Progress()):
     try:
         progress(0, "Starting file processing...")
         if file_obj is None:
@@ -169,11 +152,17 @@ def process_logs(file_obj, progress=gr.Progress()):
             logging.error("Unsupported file format")
             return "Unsupported file format. Please upload a CSV file.", None, None, None, None, None
 
-        # Use pandas to load CSV
         progress(0.05, "Loading CSV file...")
         try:
-            df = pd.read_csv(file_name)
-            logging.info(f"File loaded successfully with {len(df)} rows")
+            usecols = ["device_id", "timestamp", "usage_hours", "downtime", "amc_date"]
+            dtypes = {
+                "device_id": "string",
+                "usage_hours": "float32",
+                "downtime": "float32",
+                "amc_date": "string"
+            }
+            df = pd.read_csv(file_name, usecols=usecols, dtype=dtypes, nrows=row_limit)
+            logging.info(f"File loaded successfully with {len(df)} rows (limited to {row_limit} rows)")
         except Exception as e:
             logging.error(f"Failed to load CSV: {str(e)}")
             return f"Failed to load CSV: {str(e)}", None, None, None, None, None
@@ -191,35 +180,29 @@ def process_logs(file_obj, progress=gr.Progress()):
 
         # Step 1: Summary Report
         progress(0.2, "Generating summary...")
-        summary = f"**Step 1: Summary Report**\n{summarize_logs(df, progress)}\n---\n"
+        summary = f"**Step 1: Summary Report**  \n{summarize_logs(df, progress)}"
 
         # Step 2: Log Preview
         progress(0.3, "Previewing logs...")
         if not df.empty:
             preview_lines = ["**Step 2: Log Preview (First 5 Rows)**"]
             for idx, row in df.head().iterrows():
-                preview_lines.append(f"**Row {idx + 1}:**")
-                preview_lines.append(f"- Device ID: {row['device_id']}")
-                preview_lines.append(f"- Timestamp: {row['timestamp']}")
-                preview_lines.append(f"- Usage Hours: {row['usage_hours']}")
-                preview_lines.append(f"- Downtime: {row['downtime']}")
-                preview_lines.append(f"- AMC Date: {row['amc_date']}")
-                preview_lines.append("---")
-            preview = "\n".join(preview_lines) + "\n---\n"
+                preview_lines.append(f"**Row {idx + 1}:** Device ID: {row['device_id']}, Timestamp: {row['timestamp']}, Usage Hours: {row['usage_hours']}, Downtime: {row['downtime']}, AMC Date: {row['amc_date']}")
+            preview = "\n".join(preview_lines)
         else:
-            preview = "**Step 2: Log Preview**\nNo data available.\n---\n"
+            preview = "**Step 2: Log Preview**  \nNo data available."
 
         # Step 3: Usage Chart
         chart = create_usage_chart(df, progress)
 
         # Step 4: Anomaly Detection
-        anomalies = f"**Step 3: Anomaly Detection**\n{detect_anomalies(df, progress)}\n---\n"
+        anomalies = f"**Step 3: Anomaly Detection**  \n{detect_anomalies(df, progress)}"
 
         # Step 5: AMC Reminders
-        amc_reminders = f"**Step 4: AMC Reminders**\n{check_amc_reminders(df, datetime.now(), progress)}\n---\n"
+        amc_reminders = f"**Step 4: AMC Reminders**  \n{check_amc_reminders(df, datetime.now(), progress)}"
 
         # Step 6: Dashboard Insights
-        insights = f"**Step 5: Dashboard Insights (AI)**\n{generate_dashboard_insights(df, progress)}\n---\n"
+        insights = f"**Step 5: Dashboard Insights (AI)**  \n{generate_dashboard_insights(df, progress)}"
 
         progress(1.0, "Processing complete!")
         return summary, preview, chart, anomalies, amc_reminders, insights
@@ -231,11 +214,13 @@ def process_logs(file_obj, progress=gr.Progress()):
 try:
     logging.info("Initializing Gradio Blocks interface...")
     with gr.Blocks(css="""
-        .dashboard-container {border: 1px solid #e0e0e0; padding: 10px; border-radius: 5px; background-color: #f9f9f9;}
-        .dashboard-title {font-size: 24px; font-weight: bold; margin-bottom: 10px;}
-        .dashboard-section {margin-bottom: 15px;}
-        .dashboard-section h3 {font-size: 18px; margin-bottom: 5px;}
-        .dashboard-section p {margin: 2px 0;}
+        .dashboard-container {border: 1px solid #e0e0e0; padding: 10/* Reduced padding */ 10px; border-radius: 5px; background-color: #f9f9f9;}
+        .dashboard-title {font-size: 24px; font-weight: bold; margin-bottom: 5px; /* Reduced margin */}
+        .dashboard-section {margin-bottom: 5px; /* Reduced margin */}
+        .dashboard-section h3 {font-size: 18px; margin-bottom: 2px; /* Reduced margin */}
+        .dashboard-section p {margin: 1px 0; line-height: 1.2; /* Tighter line spacing */}
+        .dashboard-section li {margin: 1px 0; line-height: 1.2; /* Tighter spacing for list items */}
+        .dashboard-section ul {margin: 2px 0; padding-left: 20px; /* Reduced margin/padding for lists */}
     """) as iface:
         gr.Markdown("<h1>LabOps Log Analyzer Dashboard (Hugging Face AI)</h1>")
         gr.Markdown("Upload a CSV file containing lab equipment logs to analyze usage.")