Add the model and the app

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,103 @@
+import gradio as gr
+import tensorflow as tf
+import cv2
+import mtcnn
+import numpy as np
+
+model  = tf.keras.models.load_model('./model')
+
+def load_and_preprocess_image(im_path, detector, maxWidth = 512):
+
+    desiredLeftEye = (0.36, 0.43)
+
+    # Load the image and convert it to grayscale
+    try:
+        image = cv2.imread(im_path)
+    except:
+        return 0
+
+    if image is None:
+        return 0
+
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Detect the face in the image
+    result = detector.detect_faces(image)
+
+    # Get the bounding box for the face
+    x, y, w, h = result[0]['box']
+
+    desiredFaceWidth = 224
+    desiredFaceHeight = 224
+    
+    # Get the landmarks for the face
+    landmarks = result[0]['keypoints']
+    
+    # Calculate the angle between the eyes
+    eye_1 = landmarks['left_eye']
+    eye_2 = landmarks['right_eye']
+    dy = eye_2[1] - eye_1[1]
+    dx = eye_2[0] - eye_1[0]
+    angle = np.arctan2(dy, dx) * 180 / np.pi
+
+    desiredRightEyeX = 1.0 - desiredLeftEye[0]
+
+    dist = np.sqrt((dx ** 2) + (dy ** 2))
+    desiredDist = (desiredRightEyeX - desiredLeftEye[0]) * desiredFaceWidth
+    scale = desiredDist / dist
+
+    eyesCenter = ((eye_1[0] + eye_2[0]) // 2, (eye_1[1] + eye_2[1]) // 2)
+    
+    # grab the rotation matrix for rotating and scaling the face
+    M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
+    # update the translation component of the matrix
+    tX = desiredFaceWidth * 0.5
+    tY = desiredFaceHeight * desiredLeftEye[1]
+    M[0, 2] += (tX - eyesCenter[0])
+    M[1, 2] += (tY - eyesCenter[1])
+
+    (w, h) = (desiredFaceWidth, desiredFaceHeight)
+    output = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC)
+
+    output = np.array(output)
+
+    output = tf.image.convert_image_dtype(output, dtype=tf.float32)
+
+    output = tf.image.rgb_to_grayscale(output)
+    output = tf.tile(output, [1, 1, 3])
+
+    output = tf.clip_by_value(output, 0, 1)
+
+    return output
+
+def predict_remaining_life(img_path):
+    detector = mtcnn.MTCNN()
+    # Transform the target image and add a batch dimension
+    img = load_and_preprocess_image(img_path, detector)
+    img = np.expand_dims(img, axis = 0)
+    #print(img.shape)
+    #plt.imshow(img)
+    # Put model into evaluation mode and turn on inference mode
+    pred = model.predict(img)
+    pred = round(pred[0][0]*100,1)
+    
+    # Return the prediction dictionary and prediction time 
+    return pred
+
+
+# Create title, description and article strings
+title = "Remaining Life Predictor"
+description = "A Convolutional Neural Net to predict how many years a person has left to live using a facial image"
+article = "https://arxiv.org/abs/2301.08229"
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict_remaining_life, # mapping function from input to output
+                    inputs=gr.Image(type="filepath"), # what are the inputs?
+                    outputs=gr.Number(label="Remaining Life (Year)"),
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch(debug=False, # print errors locally?
+            share=False) # generate a publically shareable URL?

model/keras_metadata.pb

@@ -0,0 +1,3 @@
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+oid sha256:12267191038e5ed3282719d4ad76f6d4d9081c7577ab407b70bb7fddb1519334
+size 57879

model/saved_model.pb

@@ -0,0 +1,3 @@
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+oid sha256:43c1adf18e88d5eb74bf50fede2446a05246f409febd3572413f9f1ca862fb9f
+size 342554

model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e80346bfc2f1fc28f3a549352900464d5106015a637716b808380012d1e4ef4
+size 165845415

requirements.txt

@@ -0,0 +1,7 @@
+torch==1.12.0
+torchvision==0.13.0
+gradio==3.1.4
+tensorflow==2.11.0
+numpy==1.24.0
+mtcnn==0.1.0
+cv2==4.6.0