Added Face Analytics

.gitignore

@@ -3,6 +3,7 @@
 /src/base/__pycache__
 /src/book_rec/__pycache__
 /src/cat_and_dog/__pycache__
+/src/face_analytics/__pycache__
 /src/movie_rec/__pycache__
 /src/movie_2022_rec/__pycache__
 /src/movie_reviews/__pycache__

main.py

@@ -3,6 +3,7 @@ from fastapi import FastAPI
 # Importing Models and Schemas
 from src.movie_reviews.main import movie_reviews, Schema as MovieReviewsSchema
 from src.cat_and_dog.main import cat_and_dog, Schema as CatAndDogSchema
+from src.face_analytics.main import face_analytics, Schema as FaceAnalytics
 from src.book_rec.main import book_rec, Schema as BookRecSchema
 from src.movie_rec.main import movie_rec, Schema as MovieRecSchema
 from src.movie_2022_rec.main import movie_2022_rec, Schema as Movie2022RecSchema
@@ -42,6 +43,10 @@ def endpoint_movie_reviews(req: MovieReviewsSchema):
 def endpoint_cat_and_dog(req: CatAndDogSchema):
     return cat_and_dog(req)
 
+@app.post("/face_analytics")
+def endpoint_face_analytics(req: CatAndDogSchema):
+    return face_analytics(req)
+
 @app.post("/book_rec")
 def endpoint_book_rec(req: BookRecSchema):
     return book_rec(req)

src/cat_and_dog/main.py

@@ -37,4 +37,7 @@ def predict(img_data, img_url):
 	img = img / 255.
 	pred = model.predict(img)[0, 0]
 	pred = float(pred)
-	return [round(1-pred, 3), round(pred, 3)]
+
+	return [
+		[round(1-pred, 3), round(pred, 3)],
+	]

src/face_analytics/main.py

@@ -0,0 +1,41 @@
+import numpy as np
+import tensorflow as tf
+import io, base64, requests
+from pydantic import BaseModel
+
+# SCHEMA
+class Schema(BaseModel):
+	resized_img_base64:str = None,
+	img_url:str = None
+
+# Request Handler
+def face_analytics(req):
+	resized_img_base64 = req.resized_img_base64
+	img_url = req.img_url
+	output = predict(resized_img_base64, img_url)
+	return output
+
+model_path = "./src/face_analytics/model.h5"
+model = tf.keras.models.load_model(model_path)
+
+def predict(img_data, img_url):
+	if img_url == None:
+		content = img_data.replace(" ", "+")
+		converted = bytes(content, "utf-8")
+		img = base64.decodebytes(converted)
+	else:
+		img = requests.get(img_url).content
+
+	img = io.BytesIO(img)
+	img = tf.keras.preprocessing.image.load_img(img, target_size=model.input_shape[1:])
+	img = np.array(img)
+	img = img.reshape(1, *img.shape)
+	img = tf.keras.applications.inception_v3.preprocess_input(img)
+	pred = model.predict(img)
+
+	return [[round(j, 3) for j in i] for i in np.hstack([(1-pred).T, pred.T]).tolist()]
+	return [
+		[0.3, 0.7],
+		[0.2, 0.8],
+		[0.9, 0.1],
+	]

src/face_analytics/model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3f535da515d0b8fd513f410655f4fc6921825ca6511a67d2f5e9a44a211ea18
+size 194103456

src/movie_reviews/main.py

@@ -54,7 +54,7 @@ def predict(text):
 	output = [0, 0]
 	output[pred] = 0.8
 	output[1-pred] = 0.2
-	return output
+	return [output]
 
 def preprocess(text):
     text = text.lower()	# Lowercase