arpit9516/data-visualization-project

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1. Project Type and Purpose

- This is a full‑stack, no‑code AutoML platform built on Streamlit. It guides users through the end‑to‑end ML workflow—from data ingestion through model deployment as a REST API (via FastAPI). The UI is in Streamlit (app.py + pages/), the core pipeline logic lives in utils/, and models get stored/deployed.

2. High‑Level Architecture Components

a. User Interface (Frontend)

• Streamlit Web App (app.py + pages/01_…/09)

• Renders forms, charts, tables; captures user inputs and triggers pipeline steps.

b. Core Pipeline (Backend)

• utils/ modules implementing each step: data_loader, data_cleaner, data_profiler, data_visualizer, feature_engineering, model_selection, model_training, model_evaluation, model_explanation, model_deployment.

• Orchestrated by Streamlit callbacks in each page.

c. Data Storage

• Uploaded datasets (CSV/Excel) stored in ephemeral memory or temporary folder.

• Persisted models stored under a models/ or deployed_models/ directory (according to README).

d. Model Serving

• FastAPI service spun up by utils/model_deployment.py that exposes the trained model as REST endpoints.

• Automatic OpenAPI docs for the deployed model.

e. Third‑Party Libraries/Services

• SHAP or LIME for explainability (model_explanation).

• Plotting libraries (e.g., Plotly, Matplotlib) for visualization.

• Hyperparameter search frameworks (e.g., scikit‑optimize, Optuna) under model_selection.

3. Relationships and Data Flows

- User Browser → Streamlit App: requests pages, submits data

- Streamlit App → utils/data_loader: uploads & reads data

- Data flows sequentially through cleaner → profiler → visualizer → feature_engineering → model_selection → model_training → evaluation → explanation.

- Metadata/results (charts, tables, metrics) flow back to UI for display.

- Final trained model → utils/model_deployment → FastAPI server → external clients.

4. Key Architectural Patterns

- Layered MVC‑like structure:

• View: Streamlit pages

• Controller/Orchestration: app.py + page scripts

• Model/Business Logic: utils/ modules

- Pipeline pattern: each pages/0X_… step triggers a corresponding utils/ function.

- Microservice for model serving (FastAPI).

5. Diagram Guidelines

a. Components to Draw

• “User Browser” icon

• “Streamlit Frontend” box containing sub‑boxes: Data Ingestion, Data Pre‑Processing, Visualization, Profiling, Cleaning, Feature Engineering, Model Selection, Model Training, Model Evaluation, Model Explanation, Model Deployment (one box per page)

• “Core Logic” box (utils/) with arrows from each page to its corresponding utility module

• “Data Storage” cylinder for uploaded datasets and persisted models

• “FastAPI Model Service” box for deployed REST API

• “Third‑Party Libraries” boundary showing SHAP, LIME, plotting libs

b. Interactions & Data Flow

• Directional arrows:

– Browser → Streamlit (HTTP GET/POST)

– Streamlit → utils modules (function calls)

– utils → Data Storage (read/write)

– utils/model_deployment → FastAPI (spawn service & write model artifacts)

– Clients → FastAPI (prediction requests)

• Label arrows with action names (e.g., “upload data”, “train model”, “GET /predict”)

c. Color Coding & Shapes

• Frontend (Streamlit) in one color (e.g., blue)

• Backend Utils modules in another (e.g., green)

• Data Storage in cylinder (orange)

• External Services/Libs in dashed border (gray)

• FastAPI service in a separate color (e.g., purple)

d. Additional Details

• Show sequence: pages 01 → pages 09 in pipeline order (left to right or top to bottom)

• Indicate that each page imports and calls a corresponding utils/ module

• Annotate model metrics flow back to the UI

6. Tailoring to This Project

- Emphasize one‑to‑one mapping between pages/0X_* and utils/*.py.

- Highlight that the “Model Deployment” step does double duty: saves the model and launches a FastAPI microservice.

- Note that storage is file‑based (no external database); show local storage icons.

- Since it’s no‑code, the UI is central—draw it prominently and link every pipeline step to it.

7. Best Practices

- Use clear, descriptive labels for each component and arrow.

- Group related pages or utils under a common “ML Workflow” container.

- Use directional arrows to show data flow; annotate arrow with method or data type (e.g., DataFrame, JSON).

- Apply color coding consistently so frontend, backend logic, storage, and external services are instantly distinguishable.

- If using tools like draw.io or Lucidchart, use swimlanes or layered views to separate UI vs. processing vs. serving.

- Keep the diagram uncluttered: group minor modules but show explicit connections for critical data paths.

By following these guidelines, you’ll produce a detailed, accurate system design diagram that captures the end‑to‑end architecture of the AutoML Streamlit platform—including UI, core logic, storage, and deployed model service.