examples: update io-descriptor

examples/io-descriptors/README.md

@@ -1,19 +1,21 @@
 # BentoML Input/Output Types Tutorial
 
-A BentoML service supports a wide range of data types when creating a Service API. The data types can be catagorized as follows:
+BentoML supports a wide range of data types when creating a Service API. The data types can be catagorized as follows:
 - Python Standards: `str`, `int`, `float`, `list`, `dict` etc.
 - Pydantic field types: see [Pydantic types documentation](https://field-idempotency--pydantic-docs.netlify.app/usage/types/).
 - ML specific types: `nummpy.ndarray`, `torch.Tensor` , `tf.Tensor` for tensor data, `pd.DataFrame` for tabular data, `PIL.Image.Image` for
 Image data, and `pathlib.Path` for files such as audios, images, and pdfs.
 
-When creating a Bentoml service, you should use Python's type annotations to define the expected input and output types for each API endpoint. This
-step can not only helps validate the data against the specified schema, but also enhances the clarity and readability of your code. Type annotations play
-an important role in generating the BentoML API, client, and service UI components, ensuring a consitent and predictable interaction with the Service.
+When creating a Bentoml Service, you should use Python's type annotations to define the expected input and output types for each API endpoint. This
+step can not only help validate the data against the specified schema, but also enhances the clarity and readability of your code. Type annotations play
+an important role in generating the BentoML API, client, and Service UI components, ensuring a consitent and predictable interaction with the Service.
 
-You can also use `pydantic.Field` to set additional information about servicee parameters, such as default values and descriptions. This improves the API's
-usability and provides basic documentation. See the following examples for details.
+You can also use `pydantic.Field` to set additional information about service parameters, such as default values and descriptions. This improves the API's
+usability and provides basic documentation.
 
-## Running this example
+In this tutorial, you will learn how to set different input and output types for BentoML Services.
+
+## Installing Dependencies
 
 Let's start with the environment. We recommend using virtual environment for better package handling.
 
@@ -23,7 +25,8 @@ source io-descriptors-example/bin/activate
 pip install -r requirements.txt
 ```
 
-7 different API services are implemented within this example, with diversed input/output types. When running, you should specified the class name of the service
+## Running a Service
+7 different API Services are implemented in `service.py`, with diversed input/output types. When running, you should specified the class name of the Service
 you'd like to run inside `bentofile.yaml`.
 
 ```yaml
@@ -32,25 +35,25 @@ include:
   - "service.py"
 ```
 
-In the above configuration through `bentofile.yaml`, we're running the `AudioSpeedUp` service, which you can find on line 62 of `service.py`. When running a different
-service, simply replace `AudioSpeedUp` with the class name of the service.
+In the above configuration through `bentofile.yaml`, we're running the `AudioSpeedUp` Service, which you can find on line 62 of `service.py`. When running a different
+Service, simply replace `AudioSpeedUp` with the class name of the Service.
 
-For example, if you want to run the first service `ImageResize`, you can configure the `bentofile.yaml` as follows:
+For example, if you want to run the first Service `ImageResize`, you can configure the `bentofile.yaml` as follows:
 
 ```yaml
 service: "service.py:ImageResize"
 include:
   - "service.py"
 ```
 
-After you finished configuring `bentofile.yaml`, run `bentoml serve .` to deploy the service locally. You can then interact with the auto-generated swagger UI to play
+After you finished configuring `bentofile.yaml`, run `bentoml serve .` to deploy the Service locally. You can then interact with the auto-generated swagger UI to play
 around with each different API endpoints.
 
 ## Different data types
 
 ### Standard Python types
 
-The following demonstrates a simple addtion service, with both inputs and output as float parameters. You can
+The following demonstrates a simple addtion Service, with both inputs and output as float parameters. You can
 obviously change the type annotation to `int`, `str` etc. to get familiar with the interaction between type
 annotaions and the auto-generated Swagger UI when deploying locally.\
 
@@ -84,19 +87,20 @@ class AppendStringToFile:
         return txt_file
 ```
 
-Within `service.py`, example API services with 4 different file types are implemented (audio, image, text file, and pdf file). The functionality of each service
-is quite simple and self-explainatory.
+Within `service.py`, example API Services with 4 different file types are implemented (audio, image, text file, and pdf file). The functionality of each Service
+is quite simple and self-explanatory.
 
 Notice that for class `ImageResize`, two different API endpoints are implemented. This is because BentoML can support images parameters directly through
 `PIL.Image.Image`, which means that image objects can be directly passed through clients, instead of a file object.
 
-The last two services are examples of having `numpy.ndarray` or `pandas.DataFrame` as input parameters. Since they all work quite similarly with the above examples,
-we will not specifically explain them in this tutorial. You can try to write revise the service with `torch.Tensor` as input to check your understanding.
+The last two Services are examples of having `numpy.ndarray` or `pandas.DataFrame` as input parameters. Since they all work quite similarly with the above examples,
+we will not specifically explain them in this tutorial. You can try to write revise the Service with `torch.Tensor` as input to check your understanding.
 
 To serve the these examples locally, run `bentoml serve .`
 
 ```bash
-bentoml serve .
+$ bentoml serve .
+
 2024-03-22T19:25:24+0000 [INFO] [cli] Starting production HTTP BentoServer from "service:ImageResize" listening on http://localhost:3000 (Press CTRL+C to quit)
 ```
 
@@ -106,10 +110,15 @@ You may also send request with `curl` command or any HTTP client, e.g.:
 
 ```bash
 curl -X 'POST' \
-  'http://0.0.0.0:3000/classify_generated_texts' \
+  'http://localhost:3000/transpose' \
   -H 'accept: application/json' \
-  -H 'Content-Type: text/plain' \
-  -d 'I have an idea!'
+  -H 'Content-Type: application/json' \
+  -d '{
+  "tensor": [
+    [0, 1, 2, 3],
+    [4, 5, 6, 7]
+  ]
+}'
 ```
 
 ## Deploy to BentoCloud

examples/io-descriptors/requirements.txt

@@ -5,3 +5,4 @@ torch
 accelerate
 pydub
 pdf2img
+pandas

examples/io-descriptors/service.py

@@ -12,26 +12,24 @@
 from bentoml.validators import DataframeSchema
 from bentoml.validators import DType
 
-# PROMPT_TEMPLATE = """<image>\nUSER: What's the content of the image?\nASSISTANT:"""
-
 
 @bentoml.service()
 class ImageResize:
     @bentoml.api()
-    def generate(self, image: Image, height: str = "64", width: str = "64") -> Image:
-        size = int(height), int(width)
-        return image.resize(size, im.ANTIALIAS)
+    def generate(self, image: Image, height: int = 64, width: int = 64) -> Image:
+        size = height, width
+        return image.resize(size, im.LANCZOS)
 
     @bentoml.api()
     def generate_with_path(
         self,
         image: t.Annotated[Path, bentoml.validators.ContentType("image/jpeg")],
-        height: str = "64",
-        width: str = "64",
+        height: int = 64,
+        width: int = 64,
     ) -> Image:
-        size = int(height), int(width)
+        size = height, width
         image = im.open(image)
-        return image.resize(size, im.ANTIALIAS)
+        return image.resize(size, im.LANCZOS)
 
 
 @bentoml.service()
@@ -46,12 +44,13 @@ class AppendStringToFile:
     @bentoml.api()
     def append_string_to_eof(
         self,
+        context: bentoml.Context,
         txt_file: t.Annotated[Path, bentoml.validators.ContentType("text/plain")],
         input_string: str,
     ) -> t.Annotated[Path, bentoml.validators.ContentType("text/plain")]:
-        with open(txt_file, "a") as file:
+        with open(output_path, "a") as file:
             file.write(input_string)
-        return txt_file
+        return output_path
 
 
 @bentoml.service()
@@ -72,15 +71,17 @@ class AudioSpeedUp:
     @bentoml.api()
     def speed_up_audio(
         self,
+        context: bentoml.Context,
         audio: t.Annotated[Path, bentoml.validators.ContentType("audio/mpeg")],
         velocity: float,
     ) -> t.Annotated[Path, bentoml.validators.ContentType("audio/mp3")]:
         from pydub import AudioSegment
-
-        sound = AudioSegment.from_file(audio)  # type：
+        import os
+        output_path = os.path.join(context.temp_dir, "output.mp3")
+        sound = AudioSegment.from_file(audio) 
         sound = sound.speedup(velocity)
-        sound.export("output.mp3", format="mp3")
-        return Path("output.mp3")
+        sound.export(output_path, format="mp3")
+        return Path(output_path)
 
 
 @bentoml.service()