base model

app.py

@@ -1,11 +1,103 @@
 '''
-    Gradio app file
+    Build Project
 '''
 
+# imported necessary libraries
 import gradio as gr
+import tensorflow as tf
+import numpy as np
+import os
+import yaml
+import cv2
+from PIL import Image
+import io
+import json
 
-def greet(name):
-    return "Hello " + name + "!!"
+# read config file
+def read_config():
+    config = {}
+    print(os.path.curdir)
+    with open('models_config.yaml', 'r') as cf:
+        config = yaml.safe_load(cf)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    for var in config:
+        config[var] = config[var].replace(';', os.sep)
+
+    return config
+
+config = read_config()
+
+# loading the models
+age_model = tf.keras.models.load_model(config['A_M_PATH'])
+gender_model = tf.keras.models.load_model(config['G_M_PATH'])
+face_cascade = cv2.CascadeClassifier(config['FD_M_PATH'])
+ 
+
+def main_pipeline(image):
+    
+    #file = request.form()
+    #im_b64 = file['img']
+
+    #image = im_b64.file.read()
+
+    image = Image.open(io.BytesIO(image))
+
+    image = image.convert("RGB")
+
+    image = np.asarray(image)
+
+    gray_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    faces = face_cascade.detectMultiScale(gray_img, 1.1, 4)
+    
+    if len(faces) == 0:
+        return "NO FACE DETECTED"
+
+    x, y, w, h =  faces[0]
+    #cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
+
+
+    image = Image.fromarray(image)
+    image = image.crop((x, y, x + w, y + h))
+
+    #cv2.imshow('image', np.asarray(image))
+    #cv2.waitKey()
+
+    image = tf.image.resize(image, [224,224]) 
+    image = tf.keras.preprocessing.image.img_to_array(image)
+    image = image / 255.0      
+    image = tf.expand_dims(image, axis=0)
+
+    age_prds = age_model.predict(image)
+    gender_prds = gender_model.predict(image)
+
+    age_prds = np.around(age_prds)
+    gender_prds = np.around(gender_prds)
+    gender = ""
+
+    if gender_prds[0][0] == 0:
+        gender = 'male'
+    else:
+        gender = 'female'
+
+    data = {}
+    data['age'] = str(age_prds[0][0])
+    data['gender'] = gender
+    b_box_arr = [(x, y, w, h) for (x, y, w, h) in faces]
+
+    data['left'] = str(b_box_arr[0][0])
+    data['top'] =  str(b_box_arr[0][1])
+    data['width'] = str(b_box_arr[0][2])
+    data['height'] = str(b_box_arr[0][3])
+    predictions_json = json.dumps(data)
+
+    return predictions_json
+
+# initializing the input component
+image = gr.inputs.Image(shape = (299, 299, 3))
+# initializing the output component
+labels = gr.outputs.Label()
+
+ 
+# launching the interface
+gr.Interface(fn = main_pipeline, inputs = image,
+             outputs = labels, capture_session = True).launch()

models/age_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a79e82415e7f0da4e95873f6db3bbcc9ea141a7b3fdf8ac7463e50a4abf21ff
+size 83884320

models/gender_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5e6e1d0c0dda25a1d89dae4930f0de0939b4aabd4355454e14bc2aba27cf12e
+size 83884328

models_config.yaml

@@ -0,0 +1,5 @@
+A_M_PATH: api;models;age_model.h5
+G_M_PATH: api;models;gender_model.h5
+FD_M_PATH: api;models;haarcascade_frontalface_default.xml
+
+TEST_IMG_PATH: api;images