Implemented image tagging using TensorFlow InceptionV3 (#28)

* Refactor docker-compose to its own folder
* Added FastAPI development environment
* Added support for GPU in docker file
* Added image classification
* creating endpoint for smart Image info
* added logo with white background on ios
* Added endpoint and trigger for image tagging
* Classify image and save into database
* Update readme

machine_learning/app/main.py

@@ -0,0 +1,51 @@
+from typing import Optional
+from pydantic import BaseModel
+import numpy as np
+from fastapi import FastAPI
+import tensorflow as tf
+from tensorflow.keras.applications import InceptionV3
+from tensorflow.keras.applications.inception_v3 import preprocess_input, decode_predictions
+from tensorflow.keras.preprocessing import image
+
+IMG_SIZE = 299
+PREDICTION_MODEL = InceptionV3(weights='imagenet')
+
+
+def warm_up():
+    img_path = f'./app/test.png'
+    img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+    PREDICTION_MODEL.predict(x)
+
+
+# Warm up model
+warm_up()
+app = FastAPI()
+
+
+class TagImagePayload(BaseModel):
+    thumbnail_path: str
+
+
+@app.post("/tagImage")
+async def post_root(payload: TagImagePayload):
+    imagePath = payload.thumbnail_path
+
+    if imagePath[0] == '.':
+        imagePath = imagePath[2:]
+
+    img_path = f'./app/{imagePath}'
+    img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+
+    preds = PREDICTION_MODEL.predict(x)
+    result = decode_predictions(preds, top=3)[0]
+    payload = []
+    for _, value, _ in result:
+        payload.append(value)
+
+    return payload