[Docs] URL Template

docs/how-to-guides/huggingface/pipeline/nlp/bert/index.md

@@ -93,7 +93,7 @@ Run the service, and wait for it to load the model and start the server:
 
 ### Test the service
 
-=== "Frontend"
+=== "UI"
 
     Open http://127.0.0.1:8501, and the template `Text to Text` will be selected automatically.
 

mkdocs.yml

@@ -90,8 +90,10 @@ plugins:
 
           # how-to guides
           How-to Guides: 实战指南
-          Use Pinferencia with HuggingFace: 上线 HuggingFace 模型
+          Use Pinferencia with HuggingFace: 上线HuggingFace模型
+          Use Pinferencia with PaddlePaddle: 上线PaddlePaddle模型
           HuggingFace: HuggingFace
+          PaddlePaddle: PaddlePaddle
 
           NLP Tasks: 自然语言处理
           Install Dependencies: 安装依赖
@@ -105,6 +107,20 @@ plugins:
           Define Request and Response Schema: 定义请求和响应 Schema
           Custom Templates: 自定义模版
           Custom Frontend: 自定义前端
+          Image Tasks: 图像任务
+          Image Generation: 图像生成
+          Face Detection: 人脸检测
+          Object Detection: 对象检测
+          Semantic Segmentation: 语义分割
+          Text Recognition: 文字识别
+
+          Text Tasks: 文本任务
+          Emotion Detection Textcnn: 情绪分析
+          Lexical analysis: 语法分析
+          Simultaneous Translation: 同声传译
+          Text Generation: 文字生成
+
+          Define Request and Response Schema: 定义请求和响应Schema
 
           # reference
           Reference: 参考文档
@@ -139,8 +155,10 @@ plugins:
 
           # how-to guides
           How-to Guides: 实战指南
-          Use Pinferencia with HuggingFace: 上线 HuggingFace 模型
+          Use Pinferencia with HuggingFace: 上线HuggingFace模型
+          Use Pinferencia with PaddlePaddle: 上线PaddlePaddle模型
           HuggingFace: HuggingFace
+          PaddlePaddle: PaddlePaddle
 
           NLP Tasks: 自然语言处理
           Install Dependencies: 安装依赖
@@ -154,6 +172,20 @@ plugins:
           Define Request and Response Schema: 定义请求和响应 Schema
           Custom Templates: 自定义模版
           Custom Frontend: 自定义前端
+          Image Tasks: 图像任务
+          Image Generation: 图像生成
+          Face Detection: 人脸检测
+          Object Detection: 对象检测
+          Semantic Segmentation: 语义分割
+          Text Recognition: 文字识别
+
+          Text Tasks: 文本任务
+          Emotion Detection Textcnn: 情绪分析
+          Lexical analysis: 语法分析
+          Simultaneous Translation: 同声传译
+          Text Generation: 文字生成
+
+          Define Request and Response Schema: 定义请求和响应Schema
 
           # reference
           Reference: 参考文档
@@ -234,6 +266,20 @@ nav:
         - Translation - Google T5: how-to-guides/huggingface/pipeline/nlp/translation/index.md
       - Vision Tasks:
         - Image Classification: how-to-guides/huggingface/pipeline/vision/index.md
+    - Use Pinferencia with PaddlePaddle:
+      - Install Dependencies: how-to-guides/paddlepaddle/dependencies/index.md
+      - Image Tasks:
+        - Image Classification: how-to-guides/paddlepaddle/modules/image/image_classification/index.md
+        - Image Generation: how-to-guides/paddlepaddle/modules/image/image_generation/index.md
+        - Face Detection: how-to-guides/paddlepaddle/modules/image/face_detection/index.md
+        # - Object Detection: how-to-guides/paddlepaddle/modules/image/object_detection/index.md
+        - Semantic Segmentation: how-to-guides/paddlepaddle/modules/image/semantic_segmentation/index.md
+        # - Text Recognition: how-to-guides/paddlepaddle/modules/image/text_recognition/index.md
+      - Text Tasks:
+        - Emotion Detection Textcnn: how-to-guides/paddlepaddle/modules/text/emotion_detection_textcnn/index.md
+        - Lexical analysis: how-to-guides/paddlepaddle/modules/text/lexical_analysis/index.md
+        # - Simultaneous Translation: how-to-guides/paddlepaddle/modules/text/simultaneous_translation/index.md
+        - Text Generation: how-to-guides/paddlepaddle/modules/text/text_generation/index.md
     - Define Request and Response Schema: how-to-guides/schema/index.md
     - Custom Templates: how-to-guides/custom-templates/index.md
     - Custom Frontend: how-to-guides/custom-frontend/index.md

pinferencia/frontend/templates/raw_request.py

@@ -24,4 +24,5 @@ def render(self):
             with st.spinner("Wait for result"):
                 prediction = self.predict(json.loads(raw_text), parse_data=False)
             st.write("Response")
-            st.json(prediction)
+            if prediction:
+                st.json(prediction)

pinferencia/frontend/templates/url_image_to_image.py

@@ -0,0 +1,45 @@
+import base64
+from io import BytesIO
+
+import streamlit as st
+from PIL import Image
+
+from .base import BaseTemplate
+
+
+class Template(BaseTemplate):
+    title = (
+        '<span style="color:salmon;">Image</span> '
+        '<span style="color:slategray;">to</span> '
+        '<span style="color:lightseagreen;">Image</span>'
+    )
+
+    def render(self):
+        """Render the Streamlit Frontend"""
+        super().render()
+
+        with st.form("Image Upload", clear_on_submit=True):
+            uploaded_file = st.file_uploader(
+                "Choose an image...", type=["jpg", "png", "jpeg"]
+            )
+            st.form_submit_button("Upload and Run")
+
+        col1, col2 = st.columns(2)
+        col1.markdown(
+            '<h3 style="text-align: center;">Input</h3>',
+            unsafe_allow_html=True,
+        )
+        col2.markdown(
+            '<h3 style="text-align: center;">Result</h3>',
+            unsafe_allow_html=True,
+        )
+        if uploaded_file is not None:
+            image = Image.open(uploaded_file)
+            col1.image(image, use_column_width=True)
+            base64_img_str = base64.b64encode(uploaded_file.getvalue()).decode()
+            with st.spinner("Waiting for result"):
+                prediction = self.auto_predict({"base64_img_str": base64_img_str})
+                if isinstance(prediction, list) and prediction:
+                    prediction = prediction[0]
+                result_image = Image.open(BytesIO(base64.b64decode(prediction)))
+                col2.image(result_image)

pinferencia/frontend/templates/url_image_to_text.py

@@ -0,0 +1,43 @@
+import base64
+import json
+
+import streamlit as st
+from PIL import Image
+
+from .base import BaseTemplate
+from .utils import display_text_prediction
+
+
+class Template(BaseTemplate):
+    title = (
+        '<span style="color:salmon;">Image</span> '
+        '<span style="color:slategray;">to</span> '
+        '<span style="color:lightseagreen;">Text</span>'
+    )
+
+    def render(self):
+        super().render()
+        with st.form("Image Upload", clear_on_submit=True):
+            uploaded_file = st.file_uploader(
+                "Choose an image...", type=["jpg", "png", "jpeg"]
+            )
+            st.form_submit_button("Upload and Run")
+
+        col1, col2 = st.columns(2)
+        col1.markdown(
+            '<h3 style="text-align: center;">Input</h3>',
+            unsafe_allow_html=True,
+        )
+        col2.markdown(
+            '<h3 style="text-align: center;">Result</h3>',
+            unsafe_allow_html=True,
+        )
+        if uploaded_file is not None:
+            image = Image.open(uploaded_file)
+            col1.image(image, use_column_width=True)
+            base64_img_str = base64.b64encode(uploaded_file.getvalue()).decode()
+            with st.spinner("Waiting for result"):
+                prediction = self.auto_predict({"base64_img_str": base64_img_str})
+                if isinstance(prediction, dict):
+                    prediction = json.dumps(prediction)
+                display_text_prediction(prediction, component=col2)

pinferencia/task.py

@@ -6,6 +6,8 @@
 IMAGE_TO_IMAGE = "image_to_image"
 TEXT_TO_TEXT = "text_to_text"
 TEXT_TO_IMAGE = "text_to_image"
+URL_IMAGE_TO_TEXT = "url_image_to_text"
+URL_IMAGE_TO_IMAGE = "url_image_to_image"
 
 # Specific Task
 TRANSLATION = "translation"
@@ -25,4 +27,6 @@
     IMAGE_TO_IMAGE,
     TEXT_TO_TEXT,
     TEXT_TO_IMAGE,
+    URL_IMAGE_TO_TEXT,
+    URL_IMAGE_TO_IMAGE,
 )

pinferencia/tools.py

@@ -1,4 +1,10 @@
 import base64
+
+try:
+    import cv2
+except ImportError:
+    pass
+import numpy as np
 from io import BytesIO
 
 from PIL import Image
@@ -12,3 +18,13 @@ def pil_image_to_base64_str(image: Image) -> str:
 
 def base64_str_to_pil_image(base64_str: str) -> Image:
     return Image.open(BytesIO(base64.b64decode(base64_str)))
+
+
+def base64_str_to_cv2(base64_str: str) -> np.ndarray:
+    return cv2.imdecode(
+        np.fromstring(base64.b64decode(base64_str), np.uint8), cv2.IMREAD_COLOR
+    )
+
+
+def cv2_to_base64_str(image: np.ndarray) -> str:
+    return base64.b64encode(cv2.imencode(".jpg", image)[1].tostring())

tests/conftest.py

@@ -1,4 +1,5 @@
 import pytest
+import numpy as np
 
 
 @pytest.fixture(scope="session")
@@ -56,3 +57,8 @@ def sum_product_model_metadata():
 @pytest.fixture
 def image_base64_string():
     return "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"  # noqa
+
+
+@pytest.fixture
+def image_np_ndarray():
+    return np.array([[1, 2], [3, 4]])

tests/unittest/test_frontend/template/test_url_image_to_image.py

@@ -0,0 +1,94 @@
+from unittest.mock import MagicMock, Mock
+
+import pytest
+from PIL import Image
+
+from pinferencia.frontend.templates.url_image_to_image import Template, st
+
+
+@pytest.mark.parametrize("upload", [True, False])
+@pytest.mark.parametrize(
+    "return_value_and_display_type",
+    [
+        (["use image_base64_string"], "image"),
+    ],
+)
+@pytest.mark.parametrize(
+    "metadata",
+    [
+        {},
+        {"input_type": "str"},
+        {"output_type": "str"},
+        {"input_type": "list", "output_type": "str"},
+        {"input_type": "str", "output_type": "list"},
+    ],
+)
+def test_render(
+    upload,
+    return_value_and_display_type,
+    metadata,
+    image_base64_string,
+    image_byte,
+    monkeypatch,
+):
+    return_value, display_type = return_value_and_display_type
+    if display_type == "image":
+        return_value = [image_base64_string]
+
+    # magic mock for general purpose mock
+    st_mock = MagicMock()
+
+    # mock the file uploader, return a byte object if upload is true
+    file_uploader_mock = Mock(return_value=image_byte if upload else None)
+    monkeypatch.setattr(st, "file_uploader", file_uploader_mock)
+
+    # mock the upload form
+    monkeypatch.setattr(st, "form", st_mock.form)
+    monkeypatch.setattr(st, "form_submit_button", st_mock.form_submit_button)
+
+    # mock the two columns
+    col1_mock = MagicMock()
+    col2_mock = MagicMock()
+    mock_colums = Mock(return_value=(col1_mock, col2_mock))
+    monkeypatch.setattr(st, "columns", mock_colums)
+
+    # general purpose mock
+    monkeypatch.setattr(st, "spinner", st_mock.spinner)
+
+    # mock the model manager
+    model_manager = MagicMock()
+    model_manager.predict = Mock(return_value=return_value)
+
+    # initialize and render the template
+    tmpl = Template(model_name="test", metadata=metadata, model_manager=model_manager)
+    tmpl.render()
+
+    # assert file uploader is correctly called
+    assert file_uploader_mock.call_count == 1
+    assert file_uploader_mock.call_args[1]["type"] == ["jpg", "png", "jpeg"]
+
+    # assert spinner is called if upload is true
+    assert st_mock.spinner.call_count == (1 if upload else 0)
+
+    # assert the correct method is called to display the result
+    assert col2_mock.image.call_count == (1 if upload else 0)
+    if col2_mock.image.called:
+        call_arg = col2_mock.image.call_args[0][0]
+        assert call_arg == Image.open(image_byte)
+
+    # assert the model manager's predict is correctly called
+    assert model_manager.predict.call_count == (1 if upload else 0)
+    if model_manager.predict.called:
+        assert (
+            model_manager.predict.call_args[1]["data"]
+            == [{"base64_img_str": image_base64_string}]
+            if metadata.get("input_type") == "list"
+            else image_base64_string
+        )
+        assert model_manager.predict.call_args[1]["model_name"] == "test"
+        assert model_manager.predict.call_args[1]["version_name"] is None
+
+
+def test_title():
+    assert "image" in Template.title.lower()
+    assert "text" not in Template.title.lower()