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| # Chat with Data: AI Engineering Job Market Insights | |
| Welcome! This Streamlit app helps you chat with the 2025 AI engineering job market dataset using Google Gemini LLM. Instantly discover skills, tools, salaries, industry trends, and compare roles—using natural language Q&A. | |
| 🚀 Features | |
| Conversational Q&A on AI job market insights | |
| Dataset-powered answers about skills, tools, salaries, industry demand, and roles | |
| Skill overlap and comparison between roles or experience levels | |
| GitHub issue creation for instant feedback and support | |
| Sidebar with dataset highlights for fast reference | |
| 📦 Dataset Highlights | |
| Experience Levels: | |
| Entry, Junior, Middle, Senior, Lead | |
| Job Roles: | |
| AI Product Manager | |
| AI Researcher | |
| Computer Vision Engineer | |
| Data Analyst | |
| Data Scientist | |
| ML Engineer | |
| NLP Engineer | |
| Quant Researcher | |
| Other columns: required skills, preferred tools, salary (USD), region, industry | |
| ## 🛠️ Installation & Setup | |
| ### 1 Clone the repository | |
| ```bash | |
| git clone https://github.com/16bitSega/Capstone1.git | |
| cd Capstone1 | |
| ``` | |
| ### 2 Create a virtual environment (optional but recommended) | |
| ```bash | |
| python -m venv venv | |
| # macOS / Linux | |
| source venv/bin/activate | |
| # Windows | |
| venv\Scripts activate | |
| ``` | |
| ### 3 Install dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| `requirements.txt` includes: | |
| ```text | |
| streamlit>=1.51.0 | |
| pandas>=2.3.3 | |
| numpy>=2.3.4 | |
| python-dotenv>=1.2.1 | |
| google-genai>=1.50.1 | |
| requests>=2.32.5 | |
| ``` | |
| --- | |
| ### 4 Configure environment variables (`.env`) | |
| Create a file named `.env` in the `Chat_with_data` directory (next to `main.py`): | |
| PS: You could ignore the part related to GITHUB if you are not waiting for a support ticket addressing your account :) | |
| ```env | |
| GOOGLE_API_KEY=your_gemini_api_key_here | |
| GITHUB_TOKEN=your_github_token | |
| GITHUB_REPO=your_github_user/repo_name | |
| ``` | |
| #### 4.1 Get a Gemini API key | |
| 1. Open **Google AI Studio**. | |
| 2. Sign in and go to the **API Keys** section. | |
| 3. Create a new API key and copy it into `GOOGLE_API_KEY`. | |
| ### 5. Add dataset: Place ai_job_market.csv in the project root. | |
| ▶️ Running the App | |
| From the `Chat_with_data` directory: | |
| ```bash | |
| streamlit run main.py | |
| ``` | |
| Streamlit will print a local URL (usually `http://localhost:8501`) — open it in your browser. | |
| --- | |
| ## Usage workflow with screenshots | |
| ### Initial state — Home screen (`home.png`) | |
| When you open the app, you see: | |
| - The app title and description. | |
| - Dataset information on the left sidebar | |
| - A **Example questions** section to help you get started. | |
| This represents the initial state of the Data Insights App before any user interaction. | |
|  | |
| --- | |
| ### Answering the question (`answer.png`) | |
| User is asking **What skills overlap between entry NLP Engineer and middle AI Product Manager?** | |
| This action is searching for Skills that are represented in roles matching their levels | |
|  | |
| --- | |
| ### Console logs (`console.png`) | |
| The app logs key events to the console using Python’s `logging` module | |
| This screenshot shows a real console session during interaction, confirming that: | |
| - The agent uses function calling to invoke tools | |
| - Database queries are executed and results printed to the console | |
|  | |
| ### Ticket creation (`ticket.png`) | |
| The form allows you to add the title and description, and the ticket will be added to the **Issues** | |
| On the GitHub page related to the project. | |
|  | |
| 📄 License | |
| MIT | |