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roadcast/README.md

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+# RoAdCast - Flask + PyTorch CNN starter
+
+This project contains a minimal Flask app and a small PyTorch CNN scaffold so you can train and run a model directly from VS Code.
+
+Quick setup (Windows PowerShell):
+
+1. Create and activate a virtual environment
+
+```powershell
+python -m venv .venv
+.\.venv\Scripts\Activate.ps1
+```
+
+2. Install dependencies
+
+```powershell
+pip install -r requirements.txt
+```
+
+
+3. Dataset layout
+
+Image dataset (folder-per-class):
+
+data/
+  class1/
+    img1.jpg
+  class2/
+    img2.jpg
+
+CSV dataset (single file):
+
+data.csv  (expects a `label` column and numeric feature columns)
+
+Train commands:
+
+Image training (default cnn):
+
+```powershell
+python train.py data --epochs 5 --batch-size 16
+```
+
+CSV/tabular training (MLP):
+
+```powershell
+python train.py data.csv --model-type mlp --epochs 20 --batch-size 64
+```
+
+The model will be saved as `model.pth` in the repo root (best validation checkpoint).
+
+Run the Flask app (for local testing):
+
+```powershell
+python app.py
+```
+
+Predict using curl (or Postman). Example with curl in PowerShell:
+
+```powershell
+curl -X POST -F "image=@path\to\image.jpg" http://127.0.0.1:5000/predict
+```
+
+VS Code tips
+
+- Open this folder in VS Code.
+- Use the Python extension and select the `.venv` interpreter.
+- Use the Run panel to add a launch configuration that runs `app.py` or `train.py`.
+- For long training runs, run training in the terminal (not the debugger) and monitor logs.
+
+Notes & next steps
+
+- The SimpleCNN uses 224x224 input and expects at least two maxpool steps; adjust `models.py` if you want smaller inputs.
+- Add better transforms and augmentation in `data.py` for better performance.
+- If GPU is available, PyTorch will use it automatically if installed with CUDA.