add api demo

Dockerfile

@@ -0,0 +1,28 @@
+# Select the base image
+FROM  python:3.10
+
+# Set the working directory
+WORKDIR /code
+
+# Copy the requirements file
+COPY ./requirements.txt /code/requirements.txt
+
+# Copy the source code
+COPY ./api /code/api
+COPY ./cifar.h5 /code/cifar.h5
+COPY ./api/ /code/api/
+COPY ./cifar10/ /code/cifar10/
+
+
+# Install ffmpeg
+RUN apt-get update -y
+RUN apt-get install ffmpeg libsm6 libxext6 libxext6 -y 
+
+# Install the dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+
+# Expose the port
+EXPOSE 80
+
+CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "80"]

README.md

@@ -13,12 +13,12 @@ Resumen First architecture tested
 ![](docs/first_result.jpg)
 [Complete graphics (Basic)](https://tensorboard.dev/experiment/82Hg4m0YQ1uMWkw69qSv6w/#scalars)
 
-
 [Complete graphics (Best)](https://tensorboard.dev/experiment/jhqlZ8dBRtOzxMm9o8wQcw/#scalars)
 
-![Transfer learning result](./docs/transfer_learning_result.jpg)
 
 [Architecture using transfer learning](docs/model_plot3.png)
+![Transfer learning result](./docs/transfer_learning_result.jpg)
+
 
 [Complete Graphics (Transfer LEarning)](https://tensorboard.dev/experiment/o7f6pvAMT3KSpX88ge0h1Q/#scalars)
 

api/__init__.py

@@ -0,0 +1,25 @@
+from fastapi import FastAPI
+
+app = FastAPI(
+    title="Cifar 10 Api",
+    description='''
+    This is a simple api for cifar 10 dataset.
+
+    The classes trainer are the next:
+
+    - airplane
+    - automobile
+    - bird
+    - cat
+    - deer
+    - dog
+    - frog
+    - horse
+    - ship
+    - truck
+    ''',
+    version="1.0",
+
+)
+
+from .views import *

api/views.py

@@ -0,0 +1,38 @@
+from . import app
+from fastapi import UploadFile, File, HTTPException
+import cv2
+from cifar10 import Model
+import numpy as np
+
+model = Model()
+
+ALLOW_EXTENSION = ['jpg', 'jpeg', 'png', ]
+
+@app.get('/')
+def index():
+    return {'message': 'Hello, World!'}
+
+@app.post('/analyze_image')
+async def analyze_image(file: UploadFile = File(...)):
+    '''
+        Analyze image and return the result
+
+        Args:
+            file (UploadFile): image file
+
+        Returns:
+            dict: result
+    '''
+    extension = file.filename.split('.')[-1].lower()
+
+    if extension not in ALLOW_EXTENSION:
+        raise HTTPException(status_code=400, detail='File extension not allowed')
+
+    contents = await file.read()
+    nparr = np.fromstring(contents, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    result = model.predict(img)
+
+    return {'predict': result}

requirements.txt

@@ -5,4 +5,9 @@ opencv-python
 ipykernel
 
 #devs
-matplotlib
+matplotlib
+
+#api
+fastapi
+uvicorn
+python-multipart

requirements_macos.txt

@@ -7,4 +7,9 @@ ipykernel
 
 
 #dev 
-matplotlib
+matplotlib
+
+#api
+fastapi
+uvicorn
+python-multipart