. ├── backend/
│ ├── accounts/
│ │ ├── cloud-fundamental-workshop-f46754ecb2ba.json
│ │ └── logwise-service-account.json
│ ├── models/
│ │ └── schemas.py
│ ├── routes/
│ │ ├── dashboard.py
│ │ ├── explain.py
│ │ ├── investigation.py
│ │ └── logs.py
│ ├── services/
│ │ ├── bigquery_service.py
│ │ ├── gcp_logging.py
│ │ ├── llm_explainer.py
│ │ └── storage_service.py
│ ├── .env
│ ├── .gitignore
│ ├── cloud_config.py
│ ├── main.py
│ ├── requirements.txt
│ └── temp.py
├── frontend/
│ └── logwise-frontend/
│ ├── .env
│ ├── .gitignore
│ ├── eslint.config.js
│ ├── index.html
│ ├── node_modules/
│ ├── package-lock.json
│ ├── package.json
│ ├── public/
│ │ └── vite.svg
│ ├── README.md
│ ├── services/
│ │ └── api.tsx
│ ├── src/
│ │ ├── App.css
│ │ ├── App.tsx
│ │ ├── assets/
│ │ │ └── react.svg
│ │ ├── components/
│ │ │ ├── APITester.tsx
│ │ │ ├── FloatingActionButton.tsx
│ │ │ ├── GCPProjectCard.tsx
│ │ │ ├── SessionCard.tsx
│ │ │ ├── SessionList.tsx
│ │ │ ├── topNavBar.tsx
│ │ │ └── useChartData.tsx
│ │ ├── index.css
│ │ ├── main.tsx
│ │ ├── pages/
│ │ │ ├── Dashboard.tsx
│ │ │ ├── GCPProjectConnectionForm.tsx
│ │ │ └── InvestigationSession.tsx
│ │ ├── services/
│ │ │ └── api.tsx
│ │ └── vite-env.d.ts
│ ├── tsconfig.app.json
│ ├── tsconfig.json
│ ├── tsconfig.node.json
│ └── vite.config.ts
├── .git/
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
└── venv/

🔍 LogWise - AI-Powered GCP Log Investigation Platform

LogWise is a developer-first platform for investigating, understanding, and documenting logs from Google Cloud Platform (GCP). It combines powerful log filtering, AI-based explanations, and session tracking to help users debug cloud infrastructure efficiently.

Although LogWise currently runs locally, the platform is fully integrated with real GCP services, making it cloud-ready and deployment-optimized.

---

📌 Key Highlights

• AI explanations using Google Gemini (via Vertex AI)
• Real GCP log retrieval via Cloud Logging API
• Connected project and session management using BigQuery
• Secure service key upload and storage in Google Cloud Storage
• Secrets handled securely via Secret Manager
• Extensively used IAM to allow only service accounts for accessing the platform and added a degree of security

🔧 All major GCP integrations are complete. Only frontend/backend deployment and full mock-to-live data transition are pending, covered under future scope.

---

💡 Features

• 🧠 Click-to-explain AI log interpretation
• ⏱️ Time- and severity-based filtering
• 📊 Dynamic log visualization charts
• 💬 Optional user notes on explained logs
• 💾 Persistent session saving and retrieval (per project)

---

🛠️ Tech Stack

Layer	Technology
Frontend	React + TypeScript + Tailwind CSS
Backend	FastAPI (Python)
Database	BigQuery
AI	Google Gemini (via Vertex AI)
Storage	Google Cloud Storage
Secrets	Secret Manager

---

☁️ GCP Integration Overview

Component	GCP Service	Purpose
🧠 AI Log Explanation	Vertex AI (Gemini)	Use Gemini 2.0 Flash for log interpretation
📊 Project/Sessions	BigQuery	Store project metadata and user sessions
🔐 Secret Management	Secret Manager	Secure Gemini API key handling
🧾 Key File Storage	Cloud Storage	Store uploaded service key JSON files
🌐 Frontend Hosting	Firebase Hosting (planned)	Planned hosting for React frontend
🔙 Backend	Cloud Run (planned)	Host the FastAPI backend

✅ Cloud service integrations are functional and tested.
🚫 Hosting not yet configured — part of future scope.

---

🔌 Backend API Routes (Updated)

📁 /dashboard Endpoints

Route	Method	Description
/projects	GET	Fetch list of connected GCP projects (currently returns mock data)
/sessions	GET	Fetch list of investigation sessions (mock data)
/start-session	POST	Start a new session for a project (returns generated session ID)

📁 /logs

Route	Method	Description
/logs	POST	Fetch logs from GCP using filters (time, severity, service)

📁 /explain

Route	Method	Description
/explain	POST	Use Gemini to explain the meaning and context of a selected log

📁 /investigation

Route	Method	Description
/save-session	POST	Save a session (AI explanation + optional user comment) to BigQuery
/sessions/{project_id}	GET	Retrieve all sessions for a specific GCP project
/session/{project_id}/{session_id}	GET	Get detailed data for a specific session

---

🗃️ BigQuery Tables

📁 Dataset: logwise_data

Table	Purpose
projects	Store connected GCP project metadata
sessions	Tracks each investigation session
session_comments	Stores AI explanations and user comments per session

---

🔄 Session Workflow

• Session is created only when the user saves a log explanation.
• Every session is tied to a project_id and gets an auto-incrementing session_id.
• Saved sessions are stored in BigQuery with explanation + optional comment.
• Uses IAM role managers to ensure only authorized users can access the data.

---

🧪 Local Development with GCP Services

While the system is not yet deployed, it uses live GCP services from your local environment.

✅ Requirements

• Python 3.10+
• Node.js + npm
• GCP Project with:
  • Vertex AI API enabled
  • BigQuery dataset and tables created
  • Cloud Storage bucket for service keys
  • Secret Manager storing Gemini API key

---

⚙️ Local Setup

🔹 Backend

cd backend
python -m venv venv && source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt
uvicorn main:app --reload

🔹 Frontend

cd frontend/logwise-frontend
npm install
npm run dev

🚀 Future Scope

The following enhancements are planned and can serve as future milestones:

🚀 Deploy frontend to Firebase Hosting or Cloud Run

🚀 Deploy backend to Cloud Run with Docker

📄 Export session logs and explanations to PDF for reporting