Update app.py

app.py

@@ -1,173 +1,138 @@
-import gradio as gr
+from flask import Flask, request, jsonify
+from transformers import pipeline
 import pandas as pd
-from datetime import datetime
+from datetime import datetime, timedelta
 import json
-from transformers import pipeline
-import logging
-import os
-import plotly.express as px
-
-# Configure logging for debugging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-# Load Hugging Face summarization model
-try:
-    logging.info("Attempting to load Hugging Face model...")
-    summarizer = pipeline("text2text-generation", model="google/flan-t5-base")
-    logging.info("Hugging Face model loaded successfully")
-except Exception as e:
-    logging.error(f"Failed to load model: {str(e)}")
-    raise e
-
-# Format summary prompt and generate report
-def summarize_logs(df, lab_name, start_date, end_date):
-    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"
-        downtime_events = 3  # Placeholder
-
-        prompt = (
-            f"Summarize maintenance and usage logs for lab {lab_name} "
-            f"from {start_date} to {end_date}. "
-            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"]
-        logging.info("Summary generated successfully")
-        return summary
-    except Exception as e:
-        logging.error(f"Summary generation failed: {str(e)}")
-        return "Failed to generate summary."
 
-# Create a bar chart for usage hours per device
-def create_usage_chart(df):
+app = Flask(__name__)
+
+# Initialize Hugging Face summarization pipeline
+summarizer = pipeline("text2text-generation", model="t5-small")
+
+# Helper function to calculate days until AMC expiry
+def days_until_expiry(expiry_date_str):
     try:
-        usage_data = df.groupby("device_id")["usage_hours"].sum().reset_index()
-        fig = px.bar(
-            usage_data,
-            x="device_id",
-            y="usage_hours",
-            title="Usage Hours per Device",
-            labels={"device_id": "Device ID", "usage_hours": "Usage Hours"},
-            color="usage_hours",
-            color_continuous_scale="Blues"
-        )
-        fig.update_layout(
-            title_font_size=16,
-            margin=dict(l=20, r=20, t=40, b=20),
-            plot_bgcolor="white",
-            paper_bgcolor="white",
-            font=dict(size=12)
-        )
-        return fig
-    except Exception as e:
-        logging.error(f"Failed to create usage chart: {str(e)}")
+        expiry_date = datetime.strptime(expiry_date_str, "%Y-%m-%d")
+        current_date = datetime.now()
+        return (expiry_date - current_date).days
+    except ValueError:
         return None
 
-# Main Gradio function
-def process_logs(file_obj, lab_site, start_date, end_date):
+# Helper function to detect anomalies (rule-based)
+def detect_anomalies(logs):
+    anomalies = []
+    for log in logs:
+        # Rule 1: Flag ERROR status as high severity
+        if log["status"] == "ERROR":
+            anomalies.append({
+                "device_id": log["device_id"],
+                "issue": "ERROR status detected",
+                "detected_on": log["timestamp"],
+                "severity": "high"
+            })
+        # Rule 2: Flag usage spikes (>7 hours as example threshold)
+        if log["usage_hours"] > 7:
+            anomalies.append({
+                "device_id": log["device_id"],
+                "issue": "Usage spike",
+                "detected_on": log["timestamp"],
+                "severity": "high"
+            })
+        # Rule 3: Flag downtime (usage_hours = 0 with DOWN status)
+        if log["status"] == "DOWN" and log["usage_hours"] == 0:
+            anomalies.append({
+                "device_id": log["device_id"],
+                "issue": "Unplanned downtime",
+                "detected_on": log["timestamp"],
+                "severity": "medium"
+            })
+    return anomalies
+
+# Helper function to generate AMC reminders
+def generate_amc_reminders(logs):
+    reminders = []
+    for log in logs:
+        days_left = days_until_expiry(log["amc_expiry"])
+        if days_left is not None and 0 < days_left <= 30:
+            reminders.append({
+                "device_id": log["device_id"],
+                "amc_expiry": log["amc_expiry"],
+                "days_remaining": days_left,
+                "alert": f"AMC expires in {days_left} days"
+            })
+    return reminders
+
+# Helper function to summarize logs
+def summarize_logs(logs, prompt):
+    # Convert logs to text for summarization
+    log_text = "\n".join([f"Device {log['device_id']} ({log['log_type']}): Status {log['status']}, Usage {log['usage_hours']} hours, Timestamp {log['timestamp']}, AMC Expiry {log['amc_expiry']}" for log in logs])
+    input_text = f"{prompt}\n\nLogs:\n{log_text}"
+    
+    # Use Hugging Face summarizer
+    summary = summarizer(input_text, max_length=150, min_length=50, do_sample=False)[0]["generated_text"]
+    return summary
+
+# API endpoint to process logs
+@app.route("/process-logs", methods=["POST"])
+def process_logs():
     try:
-        if file_obj is None:
-            logging.warning("No file uploaded, returning empty results")
-            return "No file uploaded.", "No data to preview.", None
-        
-        # Read file based on extension
-        file_name = file_obj.name if hasattr(file_obj, 'name') else file_obj
-        logging.info(f"Processing file: {file_name}")
+        data = request.get_json()
+        logs = data.get("logs", [])
+        prompt = data.get("prompt", "Summarize downtime and usage patterns for SmartLab-1 from May 1 to May 14")
+
+        if not logs:
+            return jsonify({"error": "No logs provided"}), 400
+
+        # Convert logs to DataFrame for analysis
+        df = pd.DataFrame(logs)
         
-        if file_name.endswith(".json"):
-            df = pd.read_json(file_name)
-        elif file_name.endswith(".csv"):
-            df = pd.read_csv(file_name)
-        else:
-            logging.error("Unsupported file format")
-            return "Unsupported file format. Please upload a CSV or JSON file.", None, None
-
-        logging.info(f"File loaded successfully with {len(df)} rows")
-
-        # Convert timestamp to datetime and filter by date range
-        try:
-            df["timestamp"] = pd.to_datetime(df["timestamp"])
-            start_date = pd.to_datetime(start_date)
-            end_date = pd.to_datetime(end_date)
-            df = df[(df["timestamp"] >= start_date) & (df["timestamp"] <= end_date)]
-            logging.info(f"Filtered to {len(df)} rows within date range {start_date} to {end_date}")
-        except Exception as e:
-            logging.error(f"Date filtering failed: {str(e)}")
-            return f"Failed to filter data by date: {str(e)}", None, None
-
-        if df.empty:
-            logging.warning("No data within the specified date range")
-            return "No data available for the specified date range.", "No data to preview.", None
-
-        summary = summarize_logs(df, lab_site, start_date, end_date)
-        preview = df.head().to_markdown() if not df.empty else "No data available."
-        chart = create_usage_chart(df)
-
-        return summary, preview, chart
+        # Calculate summary metrics
+        total_devices = len(df["device_id"].unique())
+        avg_uptime = len(df[df["status"] == "OK"]) / len(df) * 100 if len(df) > 0 else 0
+        downtime_events = len(df[df["status"] == "DOWN"])
+        most_used_device = df.groupby("device_id")["usage_hours"].sum().idxmax() if not df.empty else "N/A"
+
+        # Generate outputs
+        summary = {
+            "total_devices": total_devices,
+            "avg_uptime": f"{avg_uptime:.1f}%",
+            "downtime_events": downtime_events,
+            "most_used_device": most_used_device
+        }
+        anomalies = detect_anomalies(logs)
+        amc_reminders = generate_amc_reminders(logs)
+        text_summary = summarize_logs(logs, prompt)
+
+        # Generate maintenance report
+        report = f"""
+SmartLab-1 Maintenance Report (May 1–14, 2025)
+Generated on: {datetime.now().strftime('%Y-%m-%d')}
+
+1. Summary
+- Total Devices: {total_devices}
+- Average Uptime: {avg_uptime:.1f}%
+- Downtime Events: {downtime_events}
+- Most Used Device: {most_used_device}
+
+2. Anomalies Detected
+{chr(10).join([f"- {a['device_id']}: {a['issue']} on {a['detected_on']} ({a['severity']} severity)" for a in anomalies]) or "No anomalies detected"}
+
+3. AMC Alerts
+{chr(10).join([f"- {r['device_id']}: AMC expires on {r['amc_expiry']} ({r['days_remaining']} days remaining)" for r in amc_reminders]) or "No AMC expirations within 30 days"}
+
+4. AI-Generated Summary
+{text_summary}
+"""
+
+        return jsonify({
+            "summary": summary,
+            "anomalies": anomalies,
+            "amc_reminders": amc_reminders,
+            "maintenance_report": report
+        })
+
     except Exception as e:
-        logging.error(f"Failed to process file: {str(e)}")
-        return f"Failed to process file: {str(e)}", None, None
-
-# Gradio Interface with Dashboard Layout
-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;}
-    """) as iface:
-        gr.Markdown("<h1>LabOps Log Analyzer Dashboard (Hugging Face AI)</h1>")
-        gr.Markdown("Upload a CSV or JSON file containing lab equipment logs to analyze usage.")
-
-        with gr.Row():
-            with gr.Column(scale=1):
-                file_input = gr.File(label="Upload Logs (CSV or JSON)", file_types=[".csv", ".json"])
-                lab_site_input = gr.Textbox(label="Lab Site", placeholder="e.g., Lab A")
-                start_date_input = gr.Textbox(label="Start Date (YYYY-MM-DD)", placeholder="e.g., 2025-01-01")
-                end_date_input = gr.Textbox(label="End Date (YYYY-MM-DD)", placeholder="e.g., 2025-01-31")
-                submit_button = gr.Button("Submit", variant="primary")
-            
-            with gr.Column(scale=2):
-                with gr.Group(elem_classes="dashboard-container"):
-                    gr.Markdown("<div class='dashboard-title'>Analysis Dashboard</div>")
-                    
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            with gr.Group(elem_classes="dashboard-section"):
-                                gr.Markdown("### Summary Report")
-                                summary_output = gr.Textbox(lines=5)
-                    
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            with gr.Group(elem_classes="dashboard-section"):
-                                gr.Markdown("### Usage Chart")
-                                chart_output = gr.Plot()
-                        
-                        with gr.Column(scale=1):
-                            with gr.Group(elem_classes="dashboard-section"):
-                                gr.Markdown("### Log Preview")
-                                preview_output = gr.Markdown()
-
-        submit_button.click(
-            fn=process_logs,
-            inputs=[file_input, lab_site_input, start_date_input, end_date_input],
-            outputs=[summary_output, preview_output, chart_output]
-        )
-
-    logging.info("Gradio interface initialized successfully")
-except Exception as e:
-    logging.error(f"Failed to initialize Gradio interface: {str(e)}")
-    raise e
+        return jsonify({"error": str(e)}), 500
 
 if __name__ == "__main__":
-    try:
-        logging.info("Launching Gradio interface...")
-        iface.launch(server_name="0.0.0.0", server_port=7860, debug=True, share=False)
-        logging.info("Gradio interface launched successfully")
-    except Exception as e:
-        logging.error(f"Failed to launch Gradio interface: {str(e)}")
-        print(f"Error launching app: {str(e)}")
-        raise e
+    app.run(debug=True, host="0.0.0.0", port=5000)