# Project export: LogFlowAI This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small. ## Project metadata - Hackathon: TreeHacks 2025 - Tagline: Agentic AI & StatML DevOps platform that monitor your systems, convert log chaos into actionable insights, and deliver clear incident reports—making troubleshooting prescriptive instead of reactive. - Devpost: https://devpost.com/software/creatorai - GitHub: https://github.com/DhyeyMavani2003/logflowai - Demo: https://dhyeymavani.com/logflowai/ - Video: https://www.youtube.com/embed/F7ZFRWIjm44?enablejsapi=1&hl=en_US&rel=0&start=&version=3&wmode=transparent - Team: 3 GitHub contributor(s) — Dhyey Mavani (35 commits), github-actions[bot] (8 commits), YuCheng (Tom) Yuan (2 commits) ## Devpost submission (written by the team) ### Overview In the spirit of innovation and agility, LogFlowAI was seeded in our mind in a meta sense during our internship and work experiences at AWS & TikTok respectively—a project that melds deep technical expertise with a sharp business acumen to revolutionize how organizations handle system logs and preempt failures. Below, we share our journey through inspiration, development, challenges, accomplishments, learnings, and our ambitious plans ahead. Our mission was clear: revolutionize the way organizations handle system logs by transforming raw data into actionable business insights. ### Inspiration During our time in the industry, we saw how reactive troubleshooting could cost valuable time and resources. Inspired by the potential of predictive analytics and real-time monitoring, we asked: What if we could forecast system failures and diagnose issues before they escalate? This vision led us to create a platform that marries deep technical prowess with strategic business insights—empowering teams to move from crisis management to proactive decision-making. What It Does LogFlowAI is an end-to-end solution designed to modernize log analysis and system monitoring by combining automation with state-of-the-art AI. Key capabilities include: Real-Time Log Ingestion & Processing: Seamlessly ingests log data from diverse sources—ranging from CSV files and syslog streams to JSON endpoints and Kubernetes logs—with support for both batch processing and sub-second real-time streaming. Real-Time Log Ingestion & Processing: Seamlessly ingests log data from diverse sources—ranging from CSV files and syslog streams to JSON endpoints and Kubernetes logs—with support for both batch processing and sub-second real-time streaming. Advanced Filtering & Analytics: Empowers teams to sift through millions of log entries using sophisticated search capabilities, including full-text search, regex patterns, and custom filters. This enables pinpointing issues quickly and accurately. Advanced Filtering & Analytics: Empowers teams to sift through millions of log entries using sophisticated search capabilities, including full-text search, regex patterns, and custom filters. This enables pinpointing issues quickly and accurately. Predictive Insights & Neural Network Predictions: Leverages customized advanced machine learning models (built manually with deep learning frameworks) trained on historical data and large-scale HDFS trace benchmarks to predict system anomalies and potential failures. Predictive Insights & Neural Network Predictions: Leverages customized advanced machine learning models (built manually with deep learning frameworks) trained on historical data and large-scale HDFS trace benchmarks to predict system anomalies and potential failures. Interactive Dashboard Visualizations: Converts complex log data into clear, actionable insights through dynamic dashboards. Visualizations include real-time metrics, heat maps, and time-series graphs that help teams monitor service health and error rates at a glance. Interactive Dashboard Visualizations: Converts complex log data into clear, actionable insights through dynamic dashboards. Visualizations include real-time metrics, heat maps, and time-series graphs that help teams monitor service health and error rates at a glance. Extensible API & Seamless Integration: Offers robust RESTful endpoints to integrate with existing infrastructure, enabling automated analyses and streamlined reporting across various business systems. Extensible API & Seamless Integration: Offers robust RESTful endpoints to integrate with existing infrastructure, enabling automated analyses and streamlined reporting across various business systems. This combination of capabilities not only enhances operational efficiency but also translates technical insights into strategic business intelligence for sales, operations, and executive teams. How We Built It Our approach was to design a modular, scalable architecture that unifies diverse technologies into one cohesive platform: Modular Architecture & Data Orchestration: We integrated robust data ingestion pipelines with a suite of management commands and endpoints, ensuring that log data—regardless of its origin—is processed efficiently and accurately. Modular Architecture & Data Orchestration: We integrated robust data ingestion pipelines with a suite of management commands and endpoints, ensuring that log data—regardless of its origin—is processed efficiently and accurately. Hybrid Data Processing: By supporting both batch and real-time streaming, LogFlowAI offers sub-second latency in log analysis while handling vast volumes of data, ensuring that teams always have the most current insights. Hybrid Data Processing: By supporting both batch and real-time streaming, LogFlowAI offers sub-second latency in log analysis while handling vast volumes of data, ensuring that teams always have the most current insights. AI-Powered Predictive Analytics: A neural network model, trained on extensive datasets including HDFS trace benchmarks, drives our predictive capabilities. This model empowers the system to forecast potential issues before they occur, enabling proactive maintenance. AI-Powered Predictive Analytics: A neural network model, trained on extensive datasets including HDFS trace benchmarks, drives our predictive capabilities. This model empowers the system to forecast potential issues before they occur, enabling proactive maintenance. Complex Computational Graph: Our Langchain computational graph features an orchestrator LLM that directs the overall workflow by delegating tasks to specialized modules. Three sub-LLMs, gaining information from our backend neural network, handle specific analyses, ensuring comprehensive coverage of our log data. Their outputs are then merged and refined by an orchestrator-combine LLM, delivering cohesive, actionable insights. Complex Computational Graph: Our Langchain computational graph features an orchestrator LLM that directs the overall workflow by delegating tasks to specialized modules. Three sub-LLMs, gaining information from our backend neural network, handle specific analyses, ensuring comprehensive coverage of our log data. Their outputs are then merged and refined by an orchestrator-combine LLM, delivering cohesive, actionable insights. Integrated Visual & API Layers: Our interactive dashboards and comprehensive API provide intuitive access to key metrics and allow seamless integration with other business tools, ensuring that insights are both accessible and actionable across the organization. Integrated Visual & API Layers: Our interactive dashboards and comprehensive API provide intuitive access to key metrics and allow seamless integration with other business tools, ensuring that insights are both accessible and actionable across the organization. Quality & Scalability Focus: We built our solution with continuous integration and deployment in mind, ensuring that as the system scales, it remains reliable, secure, and easy to maintain. Quality & Scalability Focus: We built our solution with continuous integration and deployment in mind, ensuring that as the system scales, it remains reliable, secure, and easy to maintain. Challenges We Ran Into Building a platform as comprehensive as LogFlowAI presented several challenges: Data Scale & Integrity: Processing massive log datasets while ensuring data quality and consistency demanded innovative parsing and validation techniques. Data Scale & Integrity: Processing massive log datasets while ensuring data quality and consistency demanded innovative parsing and validation techniques. Seamless Integration: Orchestrating various components—ranging from real-time ingestion and filtering to predictive analytics and visualization—required meticulous synchronization and robust architecture design. Seamless Integration: Orchestrating various components—ranging from real-time ingestion and filtering to predictive analytics and visualization—required meticulous synchronization and robust architecture design. Balancing Technical Innovation with Business Value: Translating complex technical insights into actionable, easy-to-understand business intelligence required iterative design and close collaboration with potential users to ensure relevance and usability. Balancing Technical Innovation with Business Value: Translating complex technical insights into actionable, easy-to-understand business intelligence required iterative design and close collaboration with potential users to ensure relevance and usability. Accomplishments We’re Proud Of Transformative Predictive Analytics: Successfully integrating AI to forecast system issues, thereby shifting the paradigm from reactive troubleshooting to proactive system management. Transformative Predictive Analytics: Successfully integrating AI to forecast system issues, thereby shifting the paradigm from reactive troubleshooting to proactive system management. Real-Time Operational Insights: Delivering dynamic dashboards and advanced filtering capabilities that empower teams to monitor and act on system performance instantly. Real-Time Operational Insights: Delivering dynamic dashboards and advanced filtering capabilities that empower teams to monitor and act on system performance instantly. Business-Driven Innovation: Bridging the gap between technical data and strategic decision-making, enabling non-technical stakeholders to make informed, forward-looking decisions. Business-Driven Innovation: Bridging the gap between technical data and strategic decision-making, enabling non-technical stakeholders to make informed, forward-looking decisions. Robust, Scalable Architecture: Building a solution that is not only technologically advanced but also engineered for reliability and scalability across diverse enterprise environments. Robust, Scalable Architecture: Building a solution that is not only technologically advanced but also engineered for reliability and scalability across diverse enterprise environments. What We Learned Our journey with LogFlowAI has underscored several key lessons: Integration is Essential: The seamless fusion of various data sources and analytic tools is critical for delivering accurate and actionable insights. Integration is Essential: The seamless fusion of various data sources and analytic tools is critical for delivering accurate and actionable insights. Automation Fuels Efficiency: Implementing automated data pipelines, testing, and deployment processes is indispensable for maintaining high-quality software and operational resilience. Automation Fuels Efficiency: Implementing automated data pipelines, testing, and deployment processes is indispensable for maintaining high-quality software and operational resilience. User-Centric Design is Paramount: Bridging the divide between complex technical processes and business strategy requires a clear focus on usability and actionable reporting. User-Centric Design is Paramount: Bridging the divide between complex technical processes and business strategy requires a clear focus on usability and actionable reporting. Innovative Thinking Drives Business Impact: Leveraging advanced AI and real-time analytics not only solves technical problems but also creates tangible business value by anticipating challenges and guiding strategic decisions. Innovative Thinking Drives Business Impact: Leveraging advanced AI and real-time analytics not only solves technical problems but also creates tangible business value by anticipating challenges and guiding strategic decisions. What’s Next for LogFlowAI Our journey is far from over. We envision a future where LogFlowAI continues to push the boundaries of what’s possible: Enhanced Predictive Analytics: Further refining our machine learning models to improve the accuracy of system failure predictions and anomaly detection. Enhanced Predictive Analytics: Further refining our machine learning models to improve the accuracy of system failure predictions and anomaly detection. Dynamic Real-Time Monitoring: Developing even more interactive and customizable dashboards to provide deeper insights into system performance and business impact. Dynamic Real-Time Monitoring: Developing even more interactive and customizable dashboards to provide deeper insights into system performance and business impact. Expanded Data Integration: Broadening our support for additional log sources and data streams to offer a more comprehensive view of operational health. Expanded Data Integration: Broadening our support for additional log sources and data streams to offer a more comprehensive view of operational health. Scalability & Enterprise Readiness: Optimizing performance and security to ensure our platform can scale effortlessly for larger organizations with complex infrastructures. Scalability & Enterprise Readiness: Optimizing performance and security to ensure our platform can scale effortlessly for larger organizations with complex infrastructures. Deepened AI Insights for Business Strategy: Enhancing our generative AI capabilities to deliver even more nuanced, business-focused insights that drive strategic planning and growth. Deepened AI Insights for Business Strategy: Enhancing our generative AI capabilities to deliver even more nuanced, business-focused insights that drive strategic planning and growth. Closing Thoughts LogFlowAI is more than a log analysis tool—it’s a vision for a future where data drives every decision and every log tells a story. By combining real-time analytics, advanced AI, and user-centric design, we are transforming the way organizations approach DevOps and business strategy. As we continue to innovate and expand, our commitment remains to empower teams with the foresight and agility needed to thrive in an ever-evolving digital landscape. Let's build a future where proactive insights replace reactive firefighting, and where every byte of data fuels smarter, strategic decisions. ## README (from the GitHub repository) # LogFlowAI - Real-time Log Analysis & Monitoring [![Pytest + CI/CD](https://github.com/DhyeyMavani2003/logflowai/actions/workflows/django.yml/badge.svg)](https://github.com/DhyeyMavani2003/logflowai/actions/workflows/django.yml) ![Docs](https://github.com/DhyeyMavani2003/logflowai/actions/workflows/sphinx.yml/badge.svg) [![Coverage Status](https://coveralls.io/repos/github/DhyeyMavani2003/logflowai/badge.png)](https://coveralls.io/github/DhyeyMavani2003/logflowai) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![pages-build-deployment](https://github.com/DhyeyMavani2003/logflowai/actions/workflows/pages/pages-build-deployment/badge.svg?branch=gh-pages)](https://github.com/DhyeyMavani2003/logflowai/actions/workflows/pages/pages-build-deployment) [![Documentation Status](https://readthedocs.org/projects/logflowai/badge/?version=latest)](https://logflowai.readthedocs.io/en/latest/?badge=latest) LogFlowAI is a web application created for real-time log analysis and monitoring. The platform ingests, filters, and visualizes log data to help teams diagnose issues and monitor system performance effectively. This project also demonstrates advanced capabilities ranging from deep learning using PyTorch to hybrid data operations via an InterSystems IRIS vector store, alongside large-scale log/data analytics using HDFS trace benchmarks. ## Key Features - **Real-time Log Ingestion:** Stream and process logs from diverse sources including CSV files, syslog streams, JSON endpoints, and Kubernetes logs. Support both batch processing for historical data and real-time streaming with sub-second latency. Automatically handle data validation, field extraction, and timestamp normalization across multiple timezones. - **Advanced Filtering:** Power through millions of log entries with our sophisticated search capabilities. Combine full-text search, regex patterns, and field-specific filters to pinpoint exactly what you need. Features include fuzzy matching, saved searches, query templates, and support for complex boolean operations across log level, service name, timestamp, and custom fields. - **Dashboard Visualizations:** Transform raw logs into actionable insights with our interactive dashboards. Track key metrics like error rates and service health in real-time, visualize log patterns with heat maps and time-series graphs, and create custom views for different teams. All visualizations support drill-down capabilities and export options for deeper analysis. - **Extensible API:** Seamlessly integrate LogFlowAI into your existing infrastructure through our comprehensive RESTful API. Ingest logs programmatically, trigger automated analyses, and export metrics to external systems. The API includes robust authentication, rate limiting, detailed documentation, and support for custom plugins to extend functionality. - **Neural Network Architecture:** A deep learning model built in PyTorch, trained on CSV files (including preprocessed HDFS trace benchmarks) to predict outcomes based on historical data. - **Database Architecture:** An IRIS vector store is employed for advanced document storage and similarity searches using SQL-backed operations. - **HDFS Trace Bench Data:** Large-scale HDFS trace benchmarks provide performance metrics and error patterns, offering rich data for analytics and model training. ## Getting Started To clone and set up the application locally, follow the instructions below. ### Clone the Repository ```bash git clone https://github.com/DhyeyMavani2003/logflowai.git cd logflowai ``` ### Set Up a Virtual Environment Create and activate a virtual environment to manage dependencies locally. ```bash python3 -m venv env source env/bin/activate ``` ### Install Dependencies ```bash pip install -r requirements.txt ``` ### Run Database Migrations Apply migrations to set up the database schema. ```bash python manage.py makemigrations python manage.py migrate ``` ### Import Log Data Your log data (e.g., CSV files) is located under `logapp/data/`. You can import logs using the built-in management command. For example, to import logs from a CSV: ```bash python manage.py import_logs ``` Or, you can trigger the import from the LogFlowAI home page using the "Import Logs from CSV" button. ### Start the Development Server To run the application locally, use the following command: ```bash python manage.py runserver ``` Visit [http://127.0.0.1:8000/](http://127.0.0.1:8000/) in your browser to view the application. ## System Architecture & Design LogFlowAI is built with a modular and scalable architecture. Below is a high-level diagram of the core components and their interactions. ![LogFlowAI System Architecture](./img/system_design.png) ### Architecture Components - **LogDB:** The central database storing log entries for efficient querying. - **Importer:** A suite of management commands and endpoints to ingest log data from various sources. - **Home:** Displays log entries with advanced filtering options. - **Dashboard:** Provides aggregated visual insights (e.g., logs per hour, unique services). - **API:** RESTful endpoints to trigger log imports and fetch filtered log data. ### Functional Design The following diagram provides a detailed breakdown of the application’s internal components and workflows. ```mermaid graph TD %% Data Ingestion Layer subgraph "Data Ingestion" A1[Various Log Sources CSV, Syslog, JSON, Kubernetes] A2[Log Importer Management Commands & Endpoints] A1 --> A2 end %% Data Storage Layer subgraph "Data Storage" B1["LogDB PostgreSQL/SQLite"] B2["IRIS Vector Store Document Embeddings"] end %% Backend Processing Layer subgraph "Backend Processing" C1[Log Filtering & Aggregation] C2[Dashboard Metrics Computation] C1 --> B1 C2 --> B1 end %% Machine Learning & Predictions subgraph "Machine Learning" M1[Neural Network PyTorch] M2[Training Data:
CSV & HDFS Trace Benchmarks] M2 --> M1 M1 --> B1 end %% Document Ingestion & Similarity Search subgraph "Search & Similarity" S1[Document Ingestion
& Embedding] S2[Similarity Search] S1 --> B2 S2 --> B2 end %% API & Integration Layer subgraph "API & Integration" A3[RESTful API & FastAPI
/predict Endpoint] A3 --> C1 A3 --> C2 A3 --> M1 A3 --> S2 end %% Frontend Layer subgraph "Frontend" F1[Home Page:
Log Listing & Filtering] F2[Dashboard:
Visualizations & Metrics] F1 --> C1 F2 --> C2 end ``` ## Neural Network Architecture The model is implemented in PyTorch, using data loaded from several CSV files including standard splits (train, val, test) and benchmark data. A custom CSVDataset processes numerical features and converts targets into tensors. ### Dataset and Preprocessing - Data is sourced from multiple CSV files: - Standard splits: train.csv, val.csv, test.csv - Benchmark dataset: rowNumberResult.csv (contains performance metrics from HDFS traces) - Preprocessing involves cleaning data, extracting numerical features, and preparing tensors for model input. ### Model Architecture The neural network (encapsulated in the Net class) includes: - **Input Layer:** Accepts a dynamic set of features. - **Four Hidden Layers:** - Layer 1: Linear (input → 128) → BatchNorm → ReLU → Dropout (0.3) - Layer 2: Linear (128 → 64) → BatchNorm → ReLU → Dropout (0.3) - Layer 3: Linear (64 → 32) → BatchNorm → ReLU → Dropout (0.3) - Layer 4: Linear (32 → 16) → BatchNorm → ReLU → Dropout (0.3) - **Output Layer:** A final linear layer producing a single output, transformed via sigmoid activation to yield probabilities. ### Training and Evaluation - **Loss Function:** Utilizes nn.BCEWithLogitsLoss() to handle [README truncated for size] ## Detected evidence (automated analysis) Indexed codebase: 52 recognized source files, 109 KB. - Anthropic (technology) — detected in the code - CSS (language) — detected in the code - Django (technology) — detected in the code - FastAPI (technology) — detected in the code - HTML (language) — detected in the code - LangChain (technology) — detected in the code - OpenAI (technology) — detected in the code - Python (language) — detected in the code - PyTorch (technology) — detected in the code - Docker (technology) — claimed on Devpost, not found in the code - JavaScript (language) — claimed on Devpost, not found in the code ## Codebase structure (from repository index) ### Files (75 of 75) ``` .gitattributes .github/workflows/django.yml .github/workflows/sphinx.yml .github/workflows/tasks.yml .gitignore .readthedocs.yaml docs/make.bat docs/Makefile docs/README.md docs/requirements.txt docs/source/_static/custom.css docs/source/_templates/source-buttons.html docs/source/api_endpoints.rst docs/source/conf.py docs/source/index.rst docs/source/log_entry_model.rst docs/source/management_commands.rst docs/source/parse_database.rst LICENSE logflowai/.coveragerc logflowai/agentic_historical_reports/report_20250216_073636.md logflowai/agentic_historical_reports/report_20250216_075015.md logflowai/agentic_historical_reports/report_20250216_075207.md logflowai/agentic_historical_reports/report_20250216_075844.md logflowai/agentic_historical_reports/report_20250216_082637.md logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_084607.md logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_085139.md logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_085415.md logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_090657.md logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_091743.md logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_112619.md logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_113642.md logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_121013.md logflowai/db.sqlite3 logflowai/logapp_tests/test_HDFS.py logflowai/logapp/__init__.py logflowai/logapp/admin.py logflowai/logapp/apps.py logflowai/logapp/data/HDFS_2k.log_structured.csv logflowai/logapp/management/commands/__init__.py logflowai/logapp/management/commands/execute_scrapybara_email.py logflowai/logapp/management/commands/import_logs.py logflowai/logapp/management/commands/run_orchestrator.py logflowai/logapp/migrations/__init__.py logflowai/logapp/migrations/0001_initial.py logflowai/logapp/migrations/0002_rename_content_logentry_message_and_more.py logflowai/logapp/migrations/0003_alter_logentry_message.py logflowai/logapp/migrations/0004_alter_logentry_level.py logflowai/logapp/migrations/0005_alter_logentry_timestamp.py logflowai/logapp/models.py logflowai/logapp/parse_database.py logflowai/logapp/templates/logapp/dashboard.html logflowai/logapp/templates/logapp/home.html logflowai/logapp/tests.py logflowai/logapp/urls.py logflowai/logapp/views.py logflowai/logflowai/__init__.py logflowai/logflowai/asgi.py logflowai/logflowai/settings.py logflowai/logflowai/urls.py logflowai/logflowai/wsgi.py logflowai/manage.py logflowai/pytest.ini neural-network-api-service/api/main.py neural-network-api-service/Dockerfile neural-network-api-service/main.ipynb neural-network-api-service/requirements.txt neural-network-api-service/sample.py neural-network-api-service/splitter.py neural-network-api-service/test.csv neural-network-api-service/train.csv neural-network-api-service/val.csv pyproject.toml README.md requirements.txt ``` ### Dependencies - docs/requirements.txt: beautifulsoup4, Django, django-celery-beat, folium@==0.18.0, python-dotenv, pytz@==2024.2, sphinx, sphinx-wagtail-theme - neural-network-api-service/requirements.txt: fastapi, ipykernel, mangum, openai, pandas, pydantic, pyngrok, python-dotenv, setuptools, tiktoken, torch, torchvision, uvicorn - requirements.txt: aiohappyeyeballs@==2.4.3, aiohttp@==3.10.11, aiosignal@==1.3.1, alabaster@==1.0.0, amqp@==5.2.0, annotated-types@==0.7.0, anthropic@==0.39.0, asgiref@==3.8.1, async-timeout@==4.0.3, attrs@==24.2.0, babel@==2.16.0, backoff@==2.2.1, beautifulsoup4@==4.12.3, billiard@==4.2.1, black@==24.10.0, branca@==0.8.0, bs4@==0.0.2, cachetools@==5.5.0, celery@==5.4.0, certifi@==2024.8.30, cfgv@==3.4.0, charset-normalizer@==3.4.0, click@==8.1.7, click-didyoumean@==0.3.1, click-plugins@==1.1.1, click-repl@==0.3.0, contourpy@==1.3.1, coverage@==7.6.4, coveralls@==4.0.1, cron-descriptor@==1.4.5, cycler@==0.12.1, distro@==1.9.0, Django@==5.1.5, django-timezone-field@==7.0, djangorestframework@==3.15.2, docopt@==0.6.2, docstring_parser@==0.16, exceptiongroup@==1.2.2, filelock@==3.16.1, folium@==0.18.0, fonttools@==4.56.0, frozenlist@==1.5.0, furo@==2024.8.6, geographiclib@==2.0, googleapis-common-protos@==1.67.0, greenlet@==3.1.1, grpc-google-iam-v1@==0.13.1, grpcio@==1.67.0, grpcio-status@==1.67.0, httplib2@==0.22.0, identify@==2.6.1, imagesize@==1.4.1, imaplib2@==3.6, iniconfig@==2.0.0, jiter@==0.8.2, joblib@==1.4.2, jsonpatch@==1.33, kiwisolver@==1.4.8, kombu@==5.4.2, langchain@==0.3.18, langchain-core@==0.3.35, langchain-text-splitters@==0.3.6, langgraph@==0.2.73, langgraph-checkpoint@==2.0.15, langgraph-sdk@==0.1.51, langsmith@==0.3.8, matplotlib@==3.10.0, msgpack@==1.1.0, multidict@==6.1.0, nodeenv@==1.9.1, numpy, oauth2client@==4.1.3, openai@==1.63.0, orjson@==3.10.15, pandas@==2.2.3, pathspec@==0.12.1, pillow@==11.1.0, playwright@==1.50.0, pluggy@==1.5.0, pre_commit@==4.0.1, prompt_toolkit@==3.0.48, propcache@==0.2.0, proto-plus@==1.25.0, protobuf@==5.28.3, pyasn1@==0.6.1, pyasn1_modules@==0.4.1, pydantic@==2.9.2, pydantic_core@==2.23.4, pyee@==12.1.1, Pygments@==2.18.0, pyparsing@==3.2.0, pytest@==8.3.3, pytest-cov@==6.0.0, pytest-django@==4.9.0, python-crontab@==3.2.0, python-dotenv@==1.0.1, pytz@==2024.2, rsa@==4.9, scikit-learn@==1.5.2, scipy@==1.14.1, scrapybara@==2.2.7, shapely@==2.0.6, six@==1.16.0, Sphinx@==8.1.3, sphinx_wagtail_theme@==6.4.0, sphinx-basic-ng@==1.0.0b2, sphinx-copybutton@==0.5.2, sphinxcontrib-applehelp@==2.0.0, sphinxcontrib-devhelp@==2.0.0, sphinxcontrib-htmlhelp@==2.1.0, sphinxcontrib-jsmath@==1.0.1, sphinxcontrib-qthelp@==2.0.0, sphinxcontrib-serializinghtml@==2.0.0, SQLAlchemy@==2.0.38, sqlparse@==0.5.1, tenacity@==9.0.0, threadpoolctl@==3.5.0, tomli@==2.1.0, tqdm@==4.66.5, tzdata@==2024.2, uritemplate@==4.1.1, urllib3@==2.2.3, vine@==5.1.0, virtualenv@==20.27.1, whitenoise@==6.8.2, xyzservices@==2024.9.0, yarl@==1.17.1, zstandard@==0.23.0 ### Recent commits (newest first) - update mermaid and readme + push new cleaned report - update db and organize models better - add cleaned report and perform views timestamp update - commit neural network service api backend - Add .gitattributes for CSV files - Merge branch 'main' of https://github.com/DhyeyMavani2003/logflowai - readthedocs setup with index update - ran periodic workflow for db update - add new outlook email hacktreeeeee@outlook.com - Merge branch 'main' of https://github.com/DhyeyMavani2003/logflowai - prompt engineered perp and updated inputs - ran periodic workflow for db update - update run orchestrator with API link https://6971-34-138-181-102.ngrok-free.app - add perplexity workflow for cleaned summary reports - ran periodic workflow for db update - update link for api endpoint and make dependencies for numpy flexible - commit merge - integrate langgraph workflow end to end - ran periodic workflow for db update - Update tasks.yml: add working directory ## Key source files (fetched from GitHub, selected and truncated for size) ### logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_084607.md ```markdown ## Final Cleaned Technical Report ## Technical Report: Analysis of Error on Mac ### Introduction This report analyzes a strange error encountered on a Mac, considering insights from Distributed Systems, Mobile Systems, and Operating Systems perspectives. ### Analysis 1. **Distributed Systems Analysis**: The error is likely related to Distributed Systems, with a high probability of 100%. This suggests issues such as network connectivity problems, server downtime, or misconfiguration in the distributed system being accessed. 2. **Mobile Systems Analysis**: Similarly, the error could be related to Mobile Systems, also with a probability of 100%. This indicates potential issues with mobile-related configurations or connectivity. 3. **Operating System Analysis**: The probability of the error occurring due to the Operating System itself is 0%, indicating that the issue is unlikely to be caused by the OS. Instead, it might be due to application software, hardware issues, or network problems. ### Conclusion Based on the analysis, the error on the Mac is most likely related to Distributed Systems or Mobile Systems, possibly due to network connectivity issues, server downtime, or misconfiguration. It is recommended to check the network connection, server status, or configurations related to these systems for resolution. ``` ### logflowai/cleaned_historical_summary_reports/cleaned_report_20250216_085139.md ```markdown ## Final Cleaned Technical Report ## Introduction This technical report aims to diagnose and address a strange error encountered on a Mac computer. The analysis considers insights from Distributed Systems, Mobile Systems, and Operating Systems perspectives to provide a comprehensive understanding of the issue. ## Analysis 1. **Distributed Systems**: The probability of the error being related to Distributed Systems is approximately 0.0404, indicating that it is unlikely to be the cause. Therefore, the issue is probably not related to Distributed Systems. 2. **Mobile Systems**: The probability of the error being related to Mobile Systems is extremely low, at approximately 7.76e-12. This suggests that Mobile Systems are not contributing to the error. 3. **Operating Systems**: There is a significant probability of approximately 63.8% that the error is related to the Operating System. The predicted class indicates that the issue likely stems from the Operating System itself. ## Recommendations Given the high likelihood that the error is related to the Operating System, it is advisable to: - Check for any recent updates or patches for the Mac's OS. - Review any recent changes or installations that might have affected the system. - Explore other potential causes such as software bugs, hardware issues, or third-party applications. ## Conclusion The strange error on the Mac is most likely related to the Operating System. Addressing potential OS issues and reviewing recent system changes should be the primary focus for resolving the problem. ``` ### pyproject.toml ``` [tool.black] line-length = 79 include = '\.pyi?$' exclude = ''' /( \.git | \.hg | \.mypy_cache | \.tox | \.venv | _build | buck-out | build | dist )/ ''' ``` ### requirements.txt ``` aiohappyeyeballs==2.4.3 aiohttp==3.10.11 aiosignal==1.3.1 alabaster==1.0.0 amqp==5.2.0 annotated-types==0.7.0 anthropic==0.39.0 asgiref==3.8.1 async-timeout==4.0.3 attrs==24.2.0 babel==2.16.0 backoff==2.2.1 beautifulsoup4==4.12.3 billiard==4.2.1 black==24.10.0 branca==0.8.0 bs4==0.0.2 cachetools==5.5.0 celery==5.4.0 certifi==2024.8.30 cfgv==3.4.0 charset-normalizer==3.4.0 click==8.1.7 click-didyoumean==0.3.1 click-plugins==1.1.1 click-repl==0.3.0 contourpy==1.3.1 coverage==7.6.4 coveralls==4.0.1 cron-descriptor==1.4.5 cycler==0.12.1 distro==1.9.0 Django==5.1.5 django-timezone-field==7.0 djangorestframework==3.15.2 docopt==0.6.2 docstring_parser==0.16 exceptiongroup==1.2.2 filelock==3.16.1 folium==0.18.0 fonttools==4.56.0 frozenlist==1.5.0 furo==2024.8.6 geographiclib==2.0 googleapis-common-protos==1.67.0 greenlet==3.1.1 grpc-google-iam-v1==0.13.1 grpcio==1.67.0 grpcio-status==1.67.0 httplib2==0.22.0 identify==2.6.1 imagesize==1.4.1 imaplib2==3.6 iniconfig==2.0.0 jiter==0.8.2 joblib==1.4.2 jsonpatch==1.33 kiwisolver==1.4.8 kombu==5.4.2 langchain==0.3.18 langchain-core==0.3.35 langchain-text-splitters==0.3.6 langgraph==0.2.73 langgraph-checkpoint==2.0.15 langgraph-sdk==0.1.51 langsmith==0.3.8 matplotlib==3.10.0 msgpack==1.1.0 multidict==6.1.0 nodeenv==1.9.1 numpy oauth2client==4.1.3 openai==1.63.0 orjson==3.10.15 pandas==2.2.3 pathspec==0.12.1 pillow==11.1.0 playwright==1.50.0 pluggy==1.5.0 pre_commit==4.0.1 prompt_toolkit==3.0.48 propcache==0.2.0 proto-plus==1.25.0 protobuf==5.28.3 pyasn1==0.6.1 pyasn1_modules==0.4.1 pydantic==2.9.2 pydantic_core==2.23.4 pyee==12.1.1 Pygments==2.18.0 pyparsing==3.2.0 pytest==8.3.3 pytest-cov==6.0.0 pytest-django==4.9.0 python-crontab==3.2.0 python-dotenv==1.0.1 pytz==2024.2 rsa==4.9 scikit-learn==1.5.2 scipy==1.14.1 scrapybara==2.2.7 shapely==2.0.6 six==1.16.0 Sphinx==8.1.3 sphinx-basic-ng==1.0.0b2 sphinx-copybutton==0.5.2 sphinx_wagtail_theme==6.4.0 sphinxcontrib-applehelp==2.0.0 sphinxcontrib-devhelp==2.0.0 sphinxcontrib-htmlhelp==2.1.0 sphinxcontrib-jsmath==1.0.1 sphinxcontrib-qthelp==2.0.0 sphinxcontrib-serializinghtml==2.0.0 SQLAlchemy==2.0.38 sqlparse==0.5.1 tenacity==9.0.0 threadpoolctl==3.5.0 tomli==2.1.0 tqdm==4.66.5 tzdata==2024.2 uritemplate==4.1.1 urllib3==2.2.3 vine==5.1.0 virtualenv==20.27.1 whitenoise==6.8.2 xyzservices==2024.9.0 yarl==1.17.1 zstandard==0.23.0 ``` ### docs/requirements.txt ``` python-dotenv Django django-celery-beat sphinx beautifulsoup4 sphinx-wagtail-theme folium==0.18.0 pytz==2024.2 ``` ### neural-network-api-service/requirements.txt ``` openai tiktoken python-dotenv pandas ipykernel setuptools fastapi pydantic torch torchvision mangum pyngrok uvicorn ``` ### neural-network-api-service/Dockerfile ``` # Set base image FROM python:3.9 # Set working directory WORKDIR /app # Copy requirements and install dependencies COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # Install ngrok RUN curl -s https://ngrok-agent.s3.amazonaws.com/ngrok.asc | tee /etc/apt/trusted.gpg.d/ngrok.asc >/dev/null && \ echo "deb https://ngrok-agent.s3.amazonaws.com buster main" | tee /etc/apt/sources.list.d/ngrok.list && \ apt update && apt install ngrok -y # Set NGROK authtoken (Replace YOUR_AUTHTOKEN with your actual token) RUN ngrok config add-authtoken 2ocqCtL9ZhqtSqzm345OlafoVJh_6Ga7caePkvjUWLHcgPWd5 # Copy application files COPY . . # Expose port (modify if needed) EXPOSE 8000 # Run the application CMD ["python", "api/main.py"] ``` ### neural-network-api-service/api/main.py ```python from fastapi import FastAPI from pydantic import BaseModel import torch import torch.nn as nn # Define the same deep neural network architecture with dropout and batch normalization class Net(nn.Module): def __init__(self, input_dim): super(Net, self).__init__() self.fc1 = nn.Linear(input_dim, 128) self.bn1 = nn.BatchNorm1d(128) self.dropout1 = nn.Dropout(0.3) self.fc2 = nn.Linear(128, 64) self.bn2 = nn.BatchNorm1d(64) self.dropout2 = nn.Dropout(0.3) self.fc3 = nn.Linear(64, 32) self.bn3 = nn.BatchNorm1d(32) self.dropout3 = nn.Dropout(0.3) self.fc4 = nn.Linear(32, 16) self.bn4 = nn.BatchNorm1d(16) self.dropout4 = nn.Dropout(0.3) self.fc5 = nn.Linear(16, 1) self.relu = nn.ReLU() def forward(self, x): x = self.fc1(x) x = self.bn1(x) x = self.relu(x) x = self.dropout1(x) x = self.fc2(x) x = self.bn2(x) x = self.relu(x) x = self.dropout2(x) x = self.fc3(x) x = self.bn3(x) x = self.relu(x) x = self.dropout3(x) x = self.fc4(x) x = self.bn4(x) x = self.relu(x) x = self.dropout4(x) x = self.fc5(x) return x # Set the input dimension to match the number of features (adjust if necessary) input_dim = 30 # Load the trained model model = Net(input_dim) model.load_state_dict( torch.load("model.pth", map_location=torch.device("cpu")) ) model.eval() # Define a request schema using Pydantic class PredictionRequest(BaseModel): features: list[float] # List of feature values # Initialize FastAPI app app = FastAPI() @app.post("/predict") async def predict(request: PredictionRequest): # Convert the incoming features to a tensor and add a batch dimension input_tensor = torch.tensor( request.features, dtype=torch.float32 ).unsqueeze(0) with torch.no_grad(): output = model(input_tensor).squeeze(1) probability = torch.sigmoid(output).item() prediction = 1 if probability > 0.5 else 0 return {"probability": probability, "prediction": prediction} # Run the server when this file is executed directly if __name__ == "__main__": import uvicorn uvicorn.run("inference:app", host="0.0.0.0", port=8000, reload=True) ``` ### .readthedocs.yaml ```yaml # Read the Docs configuration file for Sphinx projects # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the OS, Python version and other tools you might need build: os: ubuntu-22.04 tools: python: "3.10" # You can also specify other tool versions: # nodejs: "20" # rust: "1.70" # golang: "1.20" # Optional but recommended, declare the Python requirements required # to build your documentation # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: install: - requirements: docs/requirements.txt # Build documentation in the "docs/" directory with Sphinx sphinx: configuration: docs/source/conf.py # You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs # builder: "dirhtml" # Fail on all warnings to avoid broken references # fail_on_warning: true # Optionally build your docs in additional formats such as PDF and ePub formats: - pdf - epub ``` ### neural-network-api-service/sample.py ```python import requests url = "https://ce7c-68-65-164-53.ngrok-free.app/predict" # Change this to your ngrok URL if needed data = { "features": [ 21, 0, 0, 203, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 3, 0, 0, 3, 0, 0, 0, ] # Replace with actual feature values } response = requests.post(url, json=data) print(response.content) ``` [41 more indexed source files omitted to keep this export small. The full file list is in the Codebase structure section above.]