Updating to CodeBert tpo identify anomalies

app.py

@@ -1,30 +1,61 @@
 import gradio as gr
+from transformers import pipeline
+import re
 
-def letter_counter(word: str, letter: str) -> int:
+def anomalies_detector(logs: str) -> list[tuple[int, str]]:
     """
-    Count the number of occurrences of a letter in a word or text.
+    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:
-        word (str): The input text to search through
-        letter (str): The letter to search for
+        logs (str): The input text containing log entries
 
     Returns:
-        int: The number of times the letter appears in the text
+        list[tuple[int, str]]: List of tuples containing (line_number, anomalous_text)
     """
-    word = word.lower()
-    letter = letter.lower()
-    count = word.count(letter)
-    return count
+    # Initialize the text classification pipeline with a model specialized in log analysis
+    classifier = pipeline(
+        "text-classification",
+        model="microsoft/codebert-base",  # Using CodeBERT which is better for technical text
+        top_k=2  # Get both normal and anomalous probabilities
+    )
+    
+    # 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)
+        
+        # Check if the line is classified as anomalous
+        # CodeBERT returns probabilities for both classes
+        for result in results:
+            if result['label'] == 'LABEL_1' and result['score'] > 0.7:
+                anomalies.append((line_num, line))
+                break
+    
+    return anomalies
 
 # Create a standard Gradio interface
 demo = gr.Interface(
-    fn=letter_counter,
-    inputs=["textbox", "textbox"],
-    outputs="number",
-    title="Letter Counter",
-    description="Enter text and a letter to count how many times the letter appears in the text."
+    fn=anomalies_detector,
+    inputs="textbox",
+    outputs="text",
+    title="Log Anomaly Detector",
+    description="Enter log entries to detect anomalous patterns using AI. 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)
+    demo.launch(mcp_server=True, share=True)

pyproject.toml

@@ -6,4 +6,5 @@ readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
     "gradio[mcp]>=5.33.0",
+    "transformers>=4.52.4",
 ]

uv.lock

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@@ -763,10 +808,14 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "gradio", extra = ["mcp"] },
+    { name = "transformers" },
 ]
 
 [package.metadata]
-requires-dist = [{ name = "gradio", extras = ["mcp"], specifier = ">=5.33.0" }]
+requires-dist = [
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+]
 
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