Initial Commit

README.md

@@ -1,12 +1,69 @@
----
-title: Devops Fortune Teller
-emoji: 🏢
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 6.3.0
-app_file: app.py
-pinned: false
+# 🔮 DevOps Fortune Teller
+
+AI-Powered Predictive Log Analysis for DevOps Teams
+
+## 🚀 What Does It Do?
+
+DevOps Fortune Teller analyzes your deployment and application logs to **predict potential issues before they escalate**. Instead of just reading logs, it uses AI to detect patterns and forecast problems.
+
+## ✨ Features
+
+- 🤖 **AI-Powered Analysis**: Uses transformer-based sentiment analysis
+- 🎯 **Pattern Detection**: Identifies memory issues, connection problems, performance degradation, and more
+- 📊 **Health Scoring**: Get an instant health score for your deployment
+- 🔮 **Predictive Insights**: Forecasts issues 2-4 hours before they become critical
+- 💡 **Actionable Recommendations**: Get specific advice on how to fix detected issues
+
+## 🎨 What Makes It Unique?
+
+Unlike traditional log viewers, this tool:
+- Treats logs as **predictive data** rather than just historical records
+- Uses **AI sentiment analysis** to gauge system health
+- Provides **confidence scores** on predictions
+- Suggests **proactive actions** to prevent failures
+
+## 📋 Supported Log Patterns
+
+The tool detects:
+- Memory pressure and OOM risks
+- Connection timeouts and network issues
+- Performance degradation (slow queries, high CPU)
+- Lock contention and deadlocks
+- Disk space exhaustion
+- Cascading timeout failures
+
+## 🔧 How to Use
+
+1. Paste your logs (supports standard ERROR/WARN/INFO formats)
+2. Click "Predict Issues"
+3. Review predictions, health score, and recommendations
+4. Take action before problems escalate!
+
+## 📝 Example Log Format
+
+```
+2026-01-10 14:23:45 INFO Deployment started
+2026-01-10 14:23:47 WARN Memory usage at 78%
+2026-01-10 14:24:01 ERROR Connection timeout
+```
+
+## 🛠️ Tech Stack
+
+- **Gradio**: Beautiful web interface
+- **Transformers**: AI-powered sentiment analysis
+- **Pattern Recognition**: Custom algorithms for DevOps-specific issues
+
+## 🎯 Perfect For
+
+- Post-deployment health checks
+- Production incident investigation
+- Proactive monitoring
+- Team standups and retrospectives
+
+## 📄 License
+
+MIT License - Feel free to use and modify!
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Built with ❤️ for DevOps teams who want to stay ahead of issues

app.py

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+# app.py
+import gradio as gr
+from transformers import pipeline
+import re
+from datetime import datetime, timedelta
+import torch
+
+# Initialize sentiment analysis pipeline
+try:
+    sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+except:
+    sentiment_analyzer = None
+
+def extract_log_level(line):
+    """Extract log level from line"""
+    if 'ERROR' in line.upper():
+        return 'ERROR'
+    elif 'WARN' in line.upper() or 'WARNING' in line.upper():
+        return 'WARN'
+    elif 'INFO' in line.upper():
+        return 'INFO'
+    elif 'DEBUG' in line.upper():
+        return 'DEBUG'
+    else:
+        return 'UNKNOWN'
+
+def analyze_patterns(lines):
+    """Detect patterns in logs"""
+    patterns = {
+        'memory_issues': False,
+        'connection_issues': False,
+        'performance_issues': False,
+        'lock_issues': False,
+        'disk_issues': False,
+        'timeout_issues': False
+    }
+    
+    keywords = {
+        'memory_issues': ['memory', 'oom', 'heap', 'ram'],
+        'connection_issues': ['connection', 'timeout', 'refused', 'unreachable'],
+        'performance_issues': ['slow', 'cpu', 'performance', 'latency'],
+        'lock_issues': ['lock', 'deadlock', 'blocked'],
+        'disk_issues': ['disk', 'storage', 'space', 'inode'],
+        'timeout_issues': ['timeout', 'timed out', 'deadline exceeded']
+    }
+    
+    for line in lines:
+        line_lower = line.lower()
+        for pattern_type, pattern_keywords in keywords.items():
+            if any(keyword in line_lower for keyword in pattern_keywords):
+                patterns[pattern_type] = True
+    
+    return patterns
+
+def generate_predictions(error_count, warn_count, patterns, sentiment_score):
+    """Generate predictions based on analysis"""
+    predictions = []
+    
+    # Memory issues prediction
+    if patterns['memory_issues'] and warn_count > 0:
+        confidence = min(95, 70 + (warn_count * 5))
+        predictions.append({
+            'icon': '⚠️',
+            'type': 'warning',
+            'title': 'Memory Pressure Detected',
+            'message': f'Based on memory warnings, pod restart likely within 2-4 hours if load increases. Consider scaling horizontally or increasing memory limits.',
+            'confidence': confidence,
+            'action': 'Review memory usage metrics and consider pod autoscaling'
+        })
+    
+    # Connection issues prediction
+    if patterns['connection_issues']:
+        confidence = min(95, 75 + (error_count * 3))
+        predictions.append({
+            'icon': '🔴',
+            'type': 'critical',
+            'title': 'Connection Instability',
+            'message': 'Database/service connection issues may cascade to dependent services. Network or connection pool exhaustion detected.',
+            'confidence': confidence,
+            'action': 'Check connection pool settings and network stability'
+        })
+    
+    # Performance degradation
+    if patterns['performance_issues']:
+        confidence = min(90, 65 + (warn_count * 4))
+        predictions.append({
+            'icon': '⚠️',
+            'type': 'warning',
+            'title': 'Performance Degradation',
+            'message': 'Slow queries or high CPU detected. Performance will likely degrade further under increased load.',
+            'confidence': confidence,
+            'action': 'Optimize queries and review resource allocation'
+        })
+    
+    # Lock/Deadlock issues
+    if patterns['lock_issues']:
+        confidence = min(85, 60 + (error_count * 5))
+        predictions.append({
+            'icon': '🔴',
+            'type': 'critical',
+            'title': 'Resource Contention',
+            'message': 'Lock acquisition failures suggest possible deadlock scenario. Transaction conflicts detected.',
+            'confidence': confidence,
+            'action': 'Review transaction isolation levels and locking strategy'
+        })
+    
+    # Disk issues
+    if patterns['disk_issues']:
+        confidence = min(90, 70 + (error_count * 4))
+        predictions.append({
+            'icon': '🔴',
+            'type': 'critical',
+            'title': 'Disk Space Warning',
+            'message': 'Disk space or inode exhaustion detected. Service interruption imminent if not addressed.',
+            'confidence': confidence,
+            'action': 'Clean up logs and temporary files, expand storage'
+        })
+    
+    # Timeout cascade prediction
+    if patterns['timeout_issues'] and error_count > 2:
+        confidence = min(88, 68 + (error_count * 3))
+        predictions.append({
+            'icon': '⚠️',
+            'type': 'warning',
+            'title': 'Timeout Cascade Risk',
+            'message': 'Multiple timeout events detected. This pattern often leads to cascading failures across microservices.',
+            'confidence': confidence,
+            'action': 'Increase timeout thresholds or implement circuit breakers'
+        })
+    
+    # All clear
+    if not predictions and error_count == 0:
+        predictions.append({
+            'icon': '✅',
+            'type': 'success',
+            'title': 'All Systems Nominal',
+            'message': 'No concerning patterns detected. Your deployment looks healthy! Keep monitoring.',
+            'confidence': 95,
+            'action': 'Continue normal operations'
+        })
+    
+    return predictions
+
+def calculate_health_score(error_count, warn_count, info_count, sentiment_score):
+    """Calculate overall health score"""
+    base_score = 100
+    
+    # Deduct points for errors and warnings
+    base_score -= error_count * 15
+    base_score -= warn_count * 5
+    
+    # Factor in sentiment if available
+    if sentiment_score is not None:
+        base_score = base_score * 0.7 + sentiment_score * 0.3
+    
+    return max(0, min(100, base_score))
+
+def analyze_sentiment(lines):
+    """Analyze sentiment of log messages"""
+    if not sentiment_analyzer:
+        return None
+    
+    try:
+        # Extract message content (remove timestamps and log levels)
+        messages = []
+        for line in lines:
+            # Remove common log prefixes
+            cleaned = re.sub(r'^\d{4}-\d{2}-\d{2}\s+\d{2}:\d{2}:\d{2}', '', line)
+            cleaned = re.sub(r'^(ERROR|WARN|WARNING|INFO|DEBUG)', '', cleaned)
+            cleaned = cleaned.strip()
+            if cleaned and len(cleaned) > 10:
+                messages.append(cleaned[:512])  # Limit length
+        
+        if not messages:
+            return None
+        
+        # Analyze sentiment (take average)
+        results = sentiment_analyzer(messages[:20])  # Limit to avoid timeout
+        
+        positive_count = sum(1 for r in results if r['label'] == 'POSITIVE')
+        sentiment_score = (positive_count / len(results)) * 100
+        
+        return sentiment_score
+    except:
+        return None
+
+def format_prediction_html(predictions):
+    """Format predictions as HTML"""
+    html = ""
+    for pred in predictions:
+        color = {
+            'critical': '#ef4444',
+            'warning': '#f59e0b',
+            'success': '#10b981'
+        }.get(pred['type'], '#6b7280')
+        
+        html += f"""
+        <div style="border-left: 4px solid {color}; padding: 12px; margin: 10px 0; background: #f9fafb; border-radius: 4px;">
+            <div style="font-size: 18px; margin-bottom: 4px;">{pred['icon']} <strong>{pred['title']}</strong></div>
+            <div style="color: #4b5563; margin-bottom: 8px;">{pred['message']}</div>
+            <div style="font-size: 12px; color: #6b7280;">
+                <strong>Confidence:</strong> {pred['confidence']}% | 
+                <strong>Action:</strong> {pred['action']}
+            </div>
+        </div>
+        """
+    return html
+
+def analyze_logs(log_text):
+    """Main analysis function"""
+    if not log_text.strip():
+        return "⚠️ Please paste some logs to analyze", "", ""
+    
+    lines = [line.strip() for line in log_text.split('\n') if line.strip()]
+    
+    # Count log levels
+    error_count = sum(1 for line in lines if extract_log_level(line) == 'ERROR')
+    warn_count = sum(1 for line in lines if extract_log_level(line) == 'WARN')
+    info_count = sum(1 for line in lines if extract_log_level(line) == 'INFO')
+    
+    # Analyze patterns
+    patterns = analyze_patterns(lines)
+    
+    # Sentiment analysis
+    sentiment_score = analyze_sentiment(lines)
+    
+    # Calculate health score
+    health_score = calculate_health_score(error_count, warn_count, info_count, sentiment_score)
+    
+    # Generate predictions
+    predictions = generate_predictions(error_count, warn_count, patterns, sentiment_score)
+    
+    # Format summary
+    health_color = '#10b981' if health_score > 75 else '#f59e0b' if health_score > 50 else '#ef4444'
+    
+    summary = f"""
+    <div style="padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 8px; color: white;">
+        <h2 style="margin: 0 0 10px 0;">🔮 DevOps Fortune Teller Analysis</h2>
+        <div style="font-size: 14px; opacity: 0.9;">AI-Powered Predictive Log Analysis</div>
+    </div>
+    
+    <div style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 10px; margin: 20px 0;">
+        <div style="background: #fee2e2; padding: 15px; border-radius: 8px; text-align: center;">
+            <div style="font-size: 24px; font-weight: bold; color: #dc2626;">{error_count}</div>
+            <div style="color: #991b1b; font-size: 12px;">Errors</div>
+        </div>
+        <div style="background: #fef3c7; padding: 15px; border-radius: 8px; text-align: center;">
+            <div style="font-size: 24px; font-weight: bold; color: #d97706;">{warn_count}</div>
+            <div style="color: #92400e; font-size: 12px;">Warnings</div>
+        </div>
+        <div style="background: #dbeafe; padding: 15px; border-radius: 8px; text-align: center;">
+            <div style="font-size: 24px; font-weight: bold; color: #2563eb;">{info_count}</div>
+            <div style="color: #1e40af; font-size: 12px;">Info</div>
+        </div>
+        <div style="background: {health_color}20; padding: 15px; border-radius: 8px; text-align: center;">
+            <div style="font-size: 24px; font-weight: bold; color: {health_color};">{health_score}%</div>
+            <div style="color: #374151; font-size: 12px;">Health Score</div>
+        </div>
+    </div>
+    """
+    
+    # Format patterns detected
+    patterns_html = "<h3>🔍 Patterns Detected:</h3><ul style='color: #4b5563;'>"
+    pattern_names = {
+        'memory_issues': 'Memory Pressure',
+        'connection_issues': 'Connection Problems',
+        'performance_issues': 'Performance Issues',
+        'lock_issues': 'Lock Contention',
+        'disk_issues': 'Disk Space Issues',
+        'timeout_issues': 'Timeout Events'
+    }
+    detected = [pattern_names[k] for k, v in patterns.items() if v]
+    if detected:
+        for pattern in detected:
+            patterns_html += f"<li>{pattern}</li>"
+    else:
+        patterns_html += "<li>No critical patterns detected</li>"
+    patterns_html += "</ul>"
+    
+    # Format predictions
+    predictions_html = "<h3>🎯 Predictions & Recommendations:</h3>" + format_prediction_html(predictions)
+    
+    return summary, patterns_html, predictions_html
+
+# Sample logs for demo
+sample_logs = """2026-01-10 14:23:45 INFO Deployment started for service-auth v2.1.0
+2026-01-10 14:23:47 WARN Memory usage at 78% on pod-auth-3
+2026-01-10 14:23:50 INFO Health check passed for 3/3 pods
+2026-01-10 14:24:01 ERROR Connection timeout to database cluster db-primary
+2026-01-10 14:24:02 INFO Retrying connection (attempt 1/3)
+2026-01-10 14:24:05 WARN Slow query detected: SELECT * FROM users WHERE status='active' (2.3s)
+2026-01-10 14:24:08 ERROR Connection timeout to database cluster db-primary
+2026-01-10 14:24:10 INFO Connection restored to db-primary
+2026-01-10 14:24:15 ERROR Failed to acquire lock on resource user_session_123
+2026-01-10 14:24:18 WARN High CPU usage detected: 89% on pod-auth-2
+2026-01-10 14:24:20 INFO Processing queue: 1247 items pending
+2026-01-10 14:24:25 ERROR Disk space warning: /var/log at 92% capacity
+2026-01-10 14:24:30 WARN Response time degradation: p95 latency 1.8s (threshold: 1.0s)"""
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(), title="DevOps Fortune Teller") as demo:
+    gr.Markdown("""
+    # 🔮 DevOps Fortune Teller
+    ### AI-Powered Predictive Log Analysis for DevOps
+    Paste your deployment, application, or error logs below and get AI-powered predictions about potential issues before they escalate.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            log_input = gr.Textbox(
+                label="📋 Paste Your Logs Here",
+                placeholder="Paste your logs here (supports standard formats with ERROR, WARN, INFO levels)...",
+                lines=15,
+                max_lines=20
+            )
+            
+            with gr.Row():
+                analyze_btn = gr.Button("🔮 Predict Issues", variant="primary", size="lg")
+                sample_btn = gr.Button("📝 Load Sample Logs", size="lg")
+        
+        with gr.Column(scale=1):
+            summary_output = gr.HTML(label="Summary")
+            patterns_output = gr.HTML(label="Patterns")
+            predictions_output = gr.HTML(label="Predictions")
+    
+    gr.Markdown("""
+    ---
+    ### 🎯 How It Works
+    This tool uses transformer-based sentiment analysis combined with pattern recognition to:
+    - Detect concerning patterns in your logs
+    - Predict potential issues before they become critical
+    - Provide actionable recommendations
+    - Calculate a health score for your deployment
+    
+    **Supported Log Levels:** ERROR, WARN/WARNING, INFO, DEBUG
+    """)
+    
+    # Button actions
+    analyze_btn.click(
+        fn=analyze_logs,
+        inputs=[log_input],
+        outputs=[summary_output, patterns_output, predictions_output]
+    )
+    
+    sample_btn.click(
+        fn=lambda: sample_logs,
+        outputs=[log_input]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio==4.44.0
+transformers==4.36.2
+torch==2.1.2
+sentencepiece==0.1.99
+protobuf==3.20.3