Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from transformers import pipeline
 import re
 
-def anomalies_detector(logs: str) -> list[str]:
+def anomalies_detector(logs: str) -> str:
     """
     Detect anomalies in software logs using a Hugging Face transformer model.
     This function uses a specialized model trained to identify unusual patterns
@@ -12,44 +12,349 @@ def anomalies_detector(logs: str) -> list[str]:
     - Security-related events
     - Performance anomalies
     - Unexpected behavior patterns
-
+    
     Args:
         logs (str): The input text containing log entries
-
+        
     Returns:
-        list[tuple[int, str]]: List of tuples containing (line_number, anomalous_text)
+        str: Formatted analysis results with detected anomalies
     """
-    # Initialize the text classification pipeline with a proper classification model
-    classifier = pipeline("text-classification",
-                          model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
-
+    if not logs or not logs.strip():
+        return "⚠️ No log data provided. Please enter log entries to analyze."
+    
+    # Initialize the text classification pipeline
+    classifier = pipeline(
+        "text-classification",
+        model="distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+    )
     
     # Split logs into lines
-    log_lines = logs.split('\n')
-    anomalies = []
+    log_lines = [line for line in logs.split('\n') if line.strip()]
+    
+    if not log_lines:
+        return "⚠️ No valid log entries found."
     
     # Process each line
+    anomalies = []
+    total_lines = len(log_lines)
+    negative_count = 0
+    
     for line_num, line in enumerate(log_lines, 1):
-        if not line.strip():  # Skip empty lines
-            continue
+        try:
+            # Get classification result
+            result = classifier(line[:512])[0]  # Limit to 512 chars for model
             
-        # Get classification result
-        results = classifier(line)
-        
-        
-        for log, res in zip(logs, results):
-            anomalies.append(f"{log} => {res}")
-    return anomalies
+            # Consider "NEGATIVE" sentiment as potential anomaly
+            if result['label'] == 'NEGATIVE' and result['score'] > 0.7:
+                anomalies.append({
+                    'line': line_num,
+                    'text': line,
+                    'confidence': result['score']
+                })
+                negative_count += 1
+        except Exception as e:
+            continue
+    
+    # Format output
+    output = f"📊 **Analysis Summary**\n"
+    output += f"{'='*60}\n\n"
+    output += f"📝 Total log entries analyzed: **{total_lines}**\n"
+    output += f"🔍 Potential anomalies detected: **{negative_count}**\n"
+    output += f"✅ Health rate: **{((total_lines - negative_count) / total_lines * 100):.1f}%**\n\n"
+    
+    if anomalies:
+        output += f"⚠️ **Detected Anomalies:**\n"
+        output += f"{'-'*60}\n\n"
+        for idx, anomaly in enumerate(anomalies, 1):
+            output += f"**#{idx} | Line {anomaly['line']}** (Confidence: {anomaly['confidence']:.2%})\n"
+            output += f"```\n{anomaly['text']}\n```\n\n"
+    else:
+        output += "✨ **No significant anomalies detected!**\n"
+        output += "Your logs appear to be healthy.\n"
+    
+    return output
+
+
+# Custom CSS for dark purple and white theme
+custom_css = """
+:root {
+    --primary-purple: #6B46C1;
+    --dark-purple: #553C9A;
+    --light-purple: #9F7AEA;
+    --deep-purple: #3C2A5E;
+    --white: #FFFFFF;
+    --off-white: #F7FAFC;
+    --light-gray: #E2E8F0;
+}
+
+/* Main background */
+.gradio-container {
+    background: linear-gradient(135deg, var(--deep-purple) 0%, var(--dark-purple) 50%, var(--primary-purple) 100%) !important;
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif !important;
+}
+
+/* Header styling */
+.contain {
+    background: rgba(255, 255, 255, 0.98) !important;
+    border-radius: 20px !important;
+    box-shadow: 0 20px 60px rgba(107, 70, 193, 0.3) !important;
+    padding: 2rem !important;
+    margin: 2rem auto !important;
+    max-width: 1400px !important;
+}
+
+/* Title styling */
+h1 {
+    background: linear-gradient(135deg, var(--primary-purple), var(--light-purple)) !important;
+    -webkit-background-clip: text !important;
+    -webkit-text-fill-color: transparent !important;
+    background-clip: text !important;
+    font-weight: 800 !important;
+    font-size: 2.5rem !important;
+    margin-bottom: 0.5rem !important;
+    text-align: center !important;
+}
+
+/* Description text */
+.prose p {
+    color: var(--dark-purple) !important;
+    font-size: 1.1rem !important;
+    text-align: center !important;
+    margin-bottom: 1.5rem !important;
+}
+
+/* Input/Output boxes */
+.input-text, .output-text, textarea {
+    border: 2px solid var(--light-purple) !important;
+    border-radius: 12px !important;
+    background: var(--white) !important;
+    font-family: 'Fira Code', 'Courier New', monospace !important;
+    font-size: 0.95rem !important;
+    transition: all 0.3s ease !important;
+}
+
+.input-text:focus, textarea:focus {
+    border-color: var(--primary-purple) !important;
+    box-shadow: 0 0 0 3px rgba(107, 70, 193, 0.1) !important;
+    outline: none !important;
+}
+
+/* Labels */
+label {
+    color: var(--dark-purple) !important;
+    font-weight: 600 !important;
+    font-size: 1rem !important;
+    margin-bottom: 0.5rem !important;
+}
+
+/* Submit button */
+.submit-button, button[id$="-submit-button"] {
+    background: linear-gradient(135deg, var(--primary-purple), var(--dark-purple)) !important;
+    color: var(--white) !important;
+    border: none !important;
+    border-radius: 12px !important;
+    padding: 0.75rem 2rem !important;
+    font-weight: 600 !important;
+    font-size: 1rem !important;
+    cursor: pointer !important;
+    transition: all 0.3s ease !important;
+    box-shadow: 0 4px 15px rgba(107, 70, 193, 0.3) !important;
+}
+
+.submit-button:hover, button[id$="-submit-button"]:hover {
+    transform: translateY(-2px) !important;
+    box-shadow: 0 6px 20px rgba(107, 70, 193, 0.4) !important;
+}
+
+/* Clear button */
+.clear-button, button.secondary {
+    background: var(--white) !important;
+    color: var(--dark-purple) !important;
+    border: 2px solid var(--light-purple) !important;
+    border-radius: 12px !important;
+    padding: 0.75rem 2rem !important;
+    font-weight: 600 !important;
+    transition: all 0.3s ease !important;
+}
 
-# Create a standard Gradio interface
+.clear-button:hover, button.secondary:hover {
+    background: var(--light-purple) !important;
+    color: var(--white) !important;
+    transform: translateY(-2px) !important;
+}
+
+/* Output markdown styling */
+.markdown-text {
+    background: var(--off-white) !important;
+    padding: 1.5rem !important;
+    border-radius: 12px !important;
+    border-left: 4px solid var(--primary-purple) !important;
+}
+
+.markdown-text code {
+    background: var(--white) !important;
+    padding: 0.2rem 0.4rem !important;
+    border-radius: 4px !important;
+    color: var(--dark-purple) !important;
+    border: 1px solid var(--light-purple) !important;
+}
+
+.markdown-text pre {
+    background: var(--white) !important;
+    border: 2px solid var(--light-purple) !important;
+    border-radius: 8px !important;
+    padding: 1rem !important;
+}
+
+/* Footer */
+footer {
+    background: transparent !important;
+    color: var(--white) !important;
+    text-align: center !important;
+    padding: 2rem !important;
+}
+
+footer a {
+    color: var(--white) !important;
+    text-decoration: underline !important;
+}
+
+/* Examples section */
+.examples {
+    background: var(--off-white) !important;
+    border-radius: 12px !important;
+    padding: 1rem !important;
+    border: 2px solid var(--light-purple) !important;
+}
+
+/* Scrollbar styling */
+::-webkit-scrollbar {
+    width: 10px !important;
+}
+
+::-webkit-scrollbar-track {
+    background: var(--light-gray) !important;
+    border-radius: 5px !important;
+}
+
+::-webkit-scrollbar-thumb {
+    background: var(--primary-purple) !important;
+    border-radius: 5px !important;
+}
+
+::-webkit-scrollbar-thumb:hover {
+    background: var(--dark-purple) !important;
+}
+"""
+
+# Example logs for demonstration
+example_logs = [
+    """2024-01-29 10:15:23 INFO User logged in successfully
+2024-01-29 10:15:45 INFO Database connection established
+2024-01-29 10:16:02 ERROR Failed to connect to payment gateway
+2024-01-29 10:16:15 WARN Retry attempt 1 of 3
+2024-01-29 10:16:30 ERROR Connection timeout after 30 seconds
+2024-01-29 10:16:45 CRITICAL Payment service unavailable""",
+    
+    """2024-01-29 14:22:10 INFO Application started successfully
+2024-01-29 14:22:15 INFO Loading configuration files
+2024-01-29 14:22:18 INFO Server listening on port 8080
+2024-01-29 14:22:20 INFO Ready to accept connections""",
+    
+    """2024-01-29 16:30:45 INFO User authentication request
+2024-01-29 16:30:46 WARN Invalid credentials provided
+2024-01-29 16:30:50 WARN Login attempt failed for user: admin
+2024-01-29 16:30:55 ERROR Multiple failed login attempts detected
+2024-01-29 16:31:00 CRITICAL Potential security breach - IP blocked"""
+]
+
+# Create the Gradio interface
 demo = gr.Interface(
     fn=anomalies_detector,
-    inputs="textbox",
-    outputs="text",
-    title="Log Anomaly Detector",
-    description="Enter log entries to detect anomalous patterns using BERT Model. The system will identify unusual patterns, errors, and potential issues in your logs."
+    inputs=gr.Textbox(
+        label="📋 Log Entries",
+        placeholder="Paste your software logs here (mobile app, desktop, web server, etc.)\n\nExample:\n2024-01-29 10:15:23 INFO User logged in\n2024-01-29 10:16:02 ERROR Connection failed\n2024-01-29 10:16:15 WARN Retry attempt...",
+        lines=15,
+        max_lines=25
+    ),
+    outputs=gr.Markdown(
+        label="🔍 Analysis Results"
+    ),
+    title="🛡️ Sentinel Log Analyzer",
+    description="**Enterprise-grade anomaly detection for all your software logs.** Supports mobile apps, desktop applications, web servers, and more. Powered by advanced AI to identify errors, security events, and unusual patterns in real-time.",
+    examples=example_logs,
+    css=custom_css,
+    theme=gr.themes.Soft(
+        primary_hue="purple",
+        secondary_hue="purple",
+    ),
+    allow_flagging="never",
+    analytics_enabled=False
 )
 
-# Launch both the Gradio web interface and the MCP server
+# Launch the application
 if __name__ == "__main__":
-    demo.launch(mcp_server=True, share=True)
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        show_error=True
+    )
+
+
+
+# import gradio as gr
+# from transformers import pipeline
+# import re
+
+# def anomalies_detector(logs: str) -> list[str]:
+#     """
+#     Detect anomalies in software logs using a Hugging Face transformer model.
+#     This function uses a specialized model trained to identify unusual patterns
+#     in system logs, such as:
+#     - Error messages
+#     - Unusual system states
+#     - Security-related events
+#     - Performance anomalies
+#     - Unexpected behavior patterns
+
+#     Args:
+#         logs (str): The input text containing log entries
+
+#     Returns:
+#         list[tuple[int, str]]: List of tuples containing (line_number, anomalous_text)
+#     """
+#     # Initialize the text classification pipeline with a proper classification model
+#     classifier = pipeline("text-classification",
+#                           model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+    
+#     # Split logs into lines
+#     log_lines = logs.split('\n')
+#     anomalies = []
+    
+#     # Process each line
+#     for line_num, line in enumerate(log_lines, 1):
+#         if not line.strip():  # Skip empty lines
+#             continue
+            
+#         # Get classification result
+#         results = classifier(line)
+        
+        
+#         for log, res in zip(logs, results):
+#             anomalies.append(f"{log} => {res}")
+#     return anomalies
+
+# # Create a standard Gradio interface
+# demo = gr.Interface(
+#     fn=anomalies_detector,
+#     inputs="textbox",
+#     outputs="text",
+#     title="Log Anomaly Detector",
+#     description="Enter log entries to detect anomalous patterns using BERT Model. The system will identify unusual patterns, errors, and potential issues in your logs."
+# )
+
+# # Launch both the Gradio web interface and the MCP server
+# if __name__ == "__main__":
+#     demo.launch(mcp_server=True, share=True)