Update README.md

README.md

@@ -1,4 +1,74 @@
 <div align="center">
   <img alt="logo" height="200px" src=".readme/icon.png" >
-  <h1>Samuel</h1>
+  <h1>Osam</h1>
+  <p>
+    Get up and running vision foundational models locally.
+  </p>
+  <br>
+  <br>
+  <br>
 </div>
+
+*Osam* (/oʊˈsɑm/) is a tool to run open source vision foundational models locally,
+built inspired by [Ollama](https://github.com/ollama/ollama).
+
+*Osam* gives you:
+
+- **Visual foundational models** - Segment-Anything Model, Efficient-SAM, etc;
+- **Local APIs** - CLI & Python & HTTP interface;
+- **Customization** - Host custom vision models.
+
+
+## Installation
+
+```bash
+pip install osam
+```
+
+
+## Usage
+
+### CLI
+
+```bash
+# Run a model with an image
+osam run sam:308m --image examples/_images/dogs.jpg > mask.jpg
+
+# Get a JSON output
+osam run sam:308m --image examples/_images/dogs.jpg --json
+# {"model": "sam:308m", "mask": "..."}
+
+# Give a prompt
+osam run sam:308m --image examples/_images/dogs.jpg --json \
+  --prompt '{"points": [[1439, 504], [1439, 1289]], "point_labels": [1, 1]}'
+```
+
+### Python
+
+```python
+import numpy as np
+import PIL.Image
+
+import osam
+
+request = osam.types.GenerateRequest(
+    model=model_name,
+    image=np.asarray(PIL.Image.open("examples/_images/dogs.jpg)),
+    prompt=osam.types.Prompt(points=[[1439, 504], [1439, 1289]], point_labels=[1, 1]),
+)
+response = osam.apis.generate(request=request)
+PIL.Image.fromarray(response.mask).save("mask.jpg")
+print(response.mask.shape, request.mask.dtype)  # (1600, 2560), "uint8"
+```
+
+### HTTP
+
+```bash
+# Get up the server
+osam serve
+
+# POST request
+curl 127.0.0.1:11368/api/generate -X POST \
+  -H "Content-Type: application/json" \
+  -d "{\"model\": \"sam:308m\", \"image\": \"$(cat examples/_images/dogs.jpg | base64)\"}"
+```