Add Python image upload docs (#48)

Author: ncoghlan

1_python/1_llm-prediction/_image-input.md

@@ -0,0 +1,102 @@
+---
+title: Image Input
+description: API for passing images as input to the model
+index: 2
+---
+
+*Required Python SDK version*: **1.1.0**
+
+Some models, known as VLMs (Vision-Language Models), can accept images as input. You can pass images to the model using the `.respond()` method.
+
+### Prerequisite: Get a VLM (Vision-Language Model)
+
+If you don't yet have a VLM, you can download a model like `qwen2-vl-2b-instruct` using the following command:
+
+```bash
+lms get qwen2-vl-2b-instruct
+```
+
+## 1. Instantiate the Model
+
+Connect to LM Studio and obtain a handle to the VLM (Vision-Language Model) you want to use.
+
+```lms_code_snippet
+  variants:
+    "Python (convenience API)":
+      language: python
+      code: |
+        import lmstudio as lms
+        model = lms.llm("qwen2-vl-2b-instruct")
+
+    "Python (scoped resource API)":
+      language: python
+      code: |
+        import lmstudio as lms
+        with lms.Client() as client:
+            model = client.llm.model("qwen2-vl-2b-instruct")
+
+```
+
+## 2. Prepare the Image
+
+Use the `prepare_image()` function or `files` namespace method to
+get a handle to the image that can subsequently be passed to the model.
+
+```lms_code_snippet
+  variants:
+    "Python (convenience API)":
+      language: python
+      code: |
+        import lmstudio as lms
+        image_path = "/path/to/image.jpg"; // Replace with the path to your image
+        image_handle = lms.prepare_image(image_path)
+
+    "Python (scoped resource API)":
+      language: python
+      code: |
+        import lmstudio as lms
+        with lms.Client() as client:
+            image_path = "/path/to/image.jpg"; // Replace with the path to your image
+            image_handle = client.files.prepare_image(image_path)
+
+```
+
+If you only have the raw data of the image, you can supply the raw data directly as a bytes
+object without having to write it to disk first. Due to this feature, binary filesystem
+paths are *not* supported (as they will be handled as malformed image data rather than as
+filesystem paths).
+
+Binary IO objects are also accepted as local file inputs.
+
+The LM Studio server supports JPEG, PNG, and WebP image formats.
+
+## 3. Pass the Image to the Model in `.respond()`
+
+Generate a prediction by passing the image to the model in the `.respond()` method.
+
+```lms_code_snippet
+  variants:
+    "Python (convenience API)":
+      language: python
+      code: |
+        import lmstudio as lms
+        image_path = "/path/to/image.jpg"; // Replace with the path to your image
+        image_handle = lms.prepare_image(image_path)
+        model = lms.llm("qwen2-vl-2b-instruct")
+        chat = lms.Chat()
+        chat.add_user_message("Describe this image please", images=[image_handle])
+        prediction = model.respond(chat)
+
+    "Python (scoped resource API)":
+      language: python
+      code: |
+        import lmstudio as lms
+        with lms.Client() as client:
+            image_path = "/path/to/image.jpg"; // Replace with the path to your image
+            image_handle = client.files.prepare_image(image_path)
+            model = client.llm.model("qwen2-vl-2b-instruct")
+            chat = lms.Chat()
+            chat.add_user_message("Describe this image please", images=[image_handle])
+            prediction = model.respond(chat)
+
+```

1_python/1_llm-prediction/image-input.md

@@ -31,7 +31,7 @@ Connect to LM Studio and obtain a handle to the VLM (Vision-Language Model) you
 
 ## 2. Prepare the Image
 
-Use the `client.files.prepareImage()` method to get a handle to the image you can pass to the model.
+Use the `client.files.prepareImage()` method to get a handle to the image that can be subsequently passed to the model.
 
 ```lms_code_snippet
   variants:
@@ -54,7 +54,7 @@ If you only have the image in the form of a base64 string, you can use the `clie
         const image = await client.files.prepareImageBase64(imageBase64);
 ```
 
-We support JPEG, PNG, and WebP image formats.
+The LM Studio server supports JPEG, PNG, and WebP image formats.
 
 ## 3. Pass the Image to the Model in `.respond()`