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‎.gitignore

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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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.idea/
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# PyPI configuration file
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.pypirc

‎README.md

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# project-nlp-log-classification
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Log classification using hybrid classification framework
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# Log Classification With Hybrid Classification Framework
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This project implements a hybrid log classification system, combining three complementary approaches to handle varying levels of complexity in log patterns. The classification methods ensure flexibility and effectiveness in processing predictable, complex, and poorly-labeled data patterns.
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---
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## Classification Approaches
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1. **Regular Expression (Regex)**:
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- Handles the most simplified and predictable patterns.
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- Useful for patterns that are easily captured using predefined rules.
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2. **Sentence Transformer + Logistic Regression**:
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- Manages complex patterns when there is sufficient training data.
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- Utilizes embeddings generated by Sentence Transformers and applies Logistic Regression as the classification layer.
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3. **LLM (Large Language Models)**:
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- Used for handling complex patterns when sufficient labeled training data is not available.
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- Provides a fallback or complementary approach to the other methods.
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---
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## Folder Structure
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1. **`training/`**:
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- Contains the code for training models using Sentence Transformer and Logistic Regression.
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- Includes the code for regex-based classification.
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2. **`models/`**:
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- Stores the saved models, including Sentence Transformer embeddings and the Logistic Regression model.
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3. **`resources/`**:
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- This folder contains resource files such as test CSV files, output files, images, etc.
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4. **Root Directory**:
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- Contains the FastAPI server code (`server.py`).
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---
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## Setup Instructions
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1. **Install Dependencies**:
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Make sure you have Python installed on your system. Install the required Python libraries by running the following command:
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```bash
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pip install -r requirements.txt
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```
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2. **Run the FastAPI Server**:
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To start the server, use the following command:
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```bash
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uvicorn server:app --reload
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```
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Once the server is running, you can access the API at:
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- `http://127.0.0.1:8000/` (Main endpoint)
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- `http://127.0.0.1:8000/docs` (Interactive Swagger documentation)
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- `http://127.0.0.1:8000/redoc` (Alternative API documentation)
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---
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## Usage
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Upload a CSV file containing logs to the FastAPI endpoint for classification. Ensure the file has the following columns:
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- `source`
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- `log_message`
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The output will be a CSV file with an additional column `target_label`, which represents the classified label for each log entry.
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---
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## Disclaimer
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**Copyrights Reserved**:
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@Codebasics Inc
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@LearnerX Pvt Ltd
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This project, including its code and resources, is intended solely for educational purposes and should not be used for any commercial purposes without proper authorization.

‎classify.py

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from processor_regex import classify_with_regex
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from processor_bert import classify_with_bert
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from processor_llm import classify_with_llm
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def classify(logs):
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labels = []
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for source, log_msg in logs:
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label = classify_log(source, log_msg)
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labels.append(label)
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return labels
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def classify_log(source, log_msg):
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if source == "LegacyCRM":
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label = classify_with_llm(log_msg)
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else:
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label = classify_with_regex(log_msg)
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if not label:
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label = classify_with_bert(log_msg)
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return label
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def classify_csv(input_file):
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import pandas as pd
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df = pd.read_csv(input_file)
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# Perform classification
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df["target_label"] = classify(list(zip(df["source"], df["log_message"])))
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# Save the modified file
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output_file = "output.csv"
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df.to_csv(output_file, index=False)
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return output_file
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if __name__ == '__main__':
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classify_csv("test.csv")
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# logs = [
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# ("ModernCRM", "IP 192.168.133.114 blocked due to potential attack"),
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# ("BillingSystem", "User 12345 logged in."),
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# ("AnalyticsEngine", "File data_6957.csv uploaded successfully by user User265."),
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# ("AnalyticsEngine", "Backup completed successfully."),
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# ("ModernHR", "GET /v2/54fadb412c4e40cdbaed9335e4c35a9e/servers/detail HTTP/1.1 RCODE 200 len: 1583 time: 0.1878400"),
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# ("ModernHR", "Admin access escalation detected for user 9429"),
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# ("LegacyCRM", "Case escalation for ticket ID 7324 failed because the assigned support agent is no longer active."),
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# ("LegacyCRM", "Invoice generation process aborted for order ID 8910 due to invalid tax calculation module."),
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# ("LegacyCRM", "The 'BulkEmailSender' feature is no longer supported. Use 'EmailCampaignManager' for improved functionality."),
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# ("LegacyCRM", " The 'ReportGenerator' module will be retired in version 4.0. Please migrate to the 'AdvancedAnalyticsSuite' by Dec 2025")
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# ]
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# labels = classify(logs)
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#
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# for log, label in zip(logs, labels):
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# print(log[0], "->", label)
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‎models/log_classifier.joblib

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‎processor_bert.py

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import joblib
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from sentence_transformers import SentenceTransformer
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model_embedding = SentenceTransformer('all-MiniLM-L6-v2') # Lightweight embedding model
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model_classification = joblib.load("models/log_classifier.joblib")
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def classify_with_bert(log_message):
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embeddings = model_embedding.encode([log_message])
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probabilities = model_classification.predict_proba(embeddings)[0]
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if max(probabilities) < 0.5:
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return "Unclassified"
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predicted_label = model_classification.predict(embeddings)[0]
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return predicted_label
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if __name__ == "__main__":
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logs = [
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"alpha.osapi_compute.wsgi.server - 12.10.11.1 - API returned 404 not found error",
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"GET /v2/3454/servers/detail HTTP/1.1 RCODE 404 len: 1583 time: 0.1878400",
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"System crashed due to drivers errors when restarting the server",
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"Hey bro, chill ya!",
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"Multiple login failures occurred on user 6454 account",
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"Server A790 was restarted unexpectedly during the process of data transfer"
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]
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for log in logs:
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label = classify_with_bert(log)
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print(log, "->", label)

‎processor_llm.py

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from dotenv import load_dotenv
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from groq import Groq
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load_dotenv()
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groq = Groq()
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def classify_with_llm(log_msg):
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"""
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Generate a variant of the input sentence. For example,
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If input sentence is "User session timed out unexpectedly, user ID: 9250.",
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variant would be "Session timed out for user 9251"
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"""
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prompt = f'''Classify the log message into one of these categories:
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(1) Workflow Error, (2) Deprecation Warning.
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If you can't figure out a category, return "Unclassified".
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Only return the category name. No preamble.
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Log message: {log_msg}'''
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chat_completion = groq.chat.completions.create(
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messages=[{"role": "user", "content": prompt}],
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model="llama-3.3-70b-versatile",
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temperature=0.5
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)
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return chat_completion.choices[0].message.content
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if __name__ == "__main__":
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print(classify_with_llm(
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"Case escalation for ticket ID 7324 failed because the assigned support agent is no longer active."))
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print(classify_with_llm(
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"The 'ReportGenerator' module will be retired in version 4.0. Please migrate to the 'AdvancedAnalyticsSuite' by Dec 2025"))
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print(classify_with_llm("System reboot initiated by user 12345."))

‎processor_regex.py

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import re
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def classify_with_regex(log_message):
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regex_patterns = {
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r"User User\d+ logged (in|out).": "User Action",
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r"Backup (started|ended) at .*": "System Notification",
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r"Backup completed successfully.": "System Notification",
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r"System updated to version .*": "System Notification",
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r"File .* uploaded successfully by user .*": "System Notification",
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r"Disk cleanup completed successfully.": "System Notification",
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r"System reboot initiated by user .*": "System Notification",
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r"Account with ID .* created by .*": "User Action"
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}
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for pattern, label in regex_patterns.items():
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if re.search(pattern, log_message):
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return label
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return None
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if __name__ == "__main__":
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print(classify_with_regex("Backup completed successfully."))
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print(classify_with_regex("Account with ID 1234 created by User1."))
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print(classify_with_regex("Hey Bro, chill ya!"))
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‎resources/output.csv

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source,log_message,target_label
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ModernCRM," ""IP 192.168.133.114 blocked due to potential attack""",Security Alert
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BillingSystem," ""User 12345 logged in.""",Security Alert
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AnalyticsEngine," ""File data_6957.csv uploaded successfully by user User265.""",System Notification
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AnalyticsEngine," ""Backup completed successfully.""",System Notification
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ModernHR," ""GET /v2/54fadb412c4e40cdbaed9335e4c35a9e/servers/detail HTTP/1.1 RCODE 200 len: 1583 time: 0.1878400""",HTTP Status
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ModernHR," ""Admin access escalation detected for user 9429""",Security Alert
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LegacyCRM," ""Case escalation for ticket ID 7324 failed because the assigned support agent is no longer active.""",Workflow Error
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LegacyCRM," ""Invoice generation process aborted for order ID 8910 due to invalid tax calculation module.""",Workflow Error
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LegacyCRM," ""The 'BulkEmailSender' feature is no longer supported. Use 'EmailCampaignManager' for improved functionality.""",Deprecation Warning
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LegacyCRM," ""The 'ReportGenerator' module will be retired in version 4.0. Please migrate to the 'AdvancedAnalyticsSuite' by Dec 2025""",Deprecation Warning

‎resources/test.csv

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source,log_message
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ModernCRM, "IP 192.168.133.114 blocked due to potential attack"
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BillingSystem, "User 12345 logged in."
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AnalyticsEngine, "File data_6957.csv uploaded successfully by user User265."
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AnalyticsEngine, "Backup completed successfully."
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ModernHR, "GET /v2/54fadb412c4e40cdbaed9335e4c35a9e/servers/detail HTTP/1.1 RCODE 200 len: 1583 time: 0.1878400"
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ModernHR, "Admin access escalation detected for user 9429"
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LegacyCRM, "Case escalation for ticket ID 7324 failed because the assigned support agent is no longer active."
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LegacyCRM, "Invoice generation process aborted for order ID 8910 due to invalid tax calculation module."
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LegacyCRM, "The 'BulkEmailSender' feature is no longer supported. Use 'EmailCampaignManager' for improved functionality."
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LegacyCRM, "The 'ReportGenerator' module will be retired in version 4.0. Please migrate to the 'AdvancedAnalyticsSuite' by Dec 2025"

‎server.py

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import pandas as pd
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from fastapi import FastAPI, UploadFile, HTTPException
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from fastapi.responses import FileResponse
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from classify import classify
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app = FastAPI()
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@app.post("/classify/")
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async def classify_logs(file: UploadFile):
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if not file.filename.endswith('.csv'):
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raise HTTPException(status_code=400, detail="File must be a CSV.")
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try:
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# Read the uploaded CSV
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df = pd.read_csv(file.file)
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if "source" not in df.columns or "log_message" not in df.columns:
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raise HTTPException(status_code=400, detail="CSV must contain 'source' and 'log_message' columns.")
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# Perform classification
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df["target_label"] = classify(list(zip(df["source"], df["log_message"])))
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print("Dataframe:",df.to_dict())
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# Save the modified file
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output_file = "resources/output.csv"
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df.to_csv(output_file, index=False)
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print("File saved to output.csv")
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return FileResponse(output_file, media_type='text/csv')
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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finally:
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file.file.close()
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# # Clean up if the file was saved
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# if os.path.exists("output.csv"):
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# os.remove("output.csv")

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