Upload 3 files

.gitattributes

@@ -32,3 +32,4 @@ 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
+Female.png filter=lfs diff=lfs merge=lfs -text

age.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96f96f8ff12b7c675847480b186cfa1da984faafc8a0f184db7cfadfe4d9eebc
+size 87833539

app.py

@@ -1,23 +1,47 @@
 from fastai.vision.all import *
 import gradio as gr
+import cv2
+classifier = cv2.CascadeClassifier('models/haarcascade_frontalface_alt2.xml')
 
 def label_func(fname):
     if int(str(fname)[str(fname).index('_')+1]) == 0:
         return "Male"
     return "Female"
 
-learn = load_learner('gender.pkl')
+def get_age(fname):
+    return int(str(fname).split('/')[1].split('_')[0])
+
+def detect_face(img):
+    faces = classifier.detectMultiScale(img)
+    x, y, w, h = faces[0]
+    cropped_img = img[y:y+h, x:x+w]
+    return cropped_img
+
+learn_gender = load_learner('gender.pkl')
+learn_age = load_learner('age.pkl')
 
 categories = ('Female', 'Male')
 
+def predict_age(img):
+    detected_face = detect_face(img)
+    pred,_,_ = learn_age.predict(detected_face)
+    return str(pred[0]), detected_face
+
 def classify_image(img):
-    pred, idx, probs = learn.predict(img)
+    pred, idx, probs = learn_gender.predict(img)
     return dict(zip(categories, map(float, probs)))
 
+def process_image(img):
+    gender = classify_image(img)
+    age, face = predict_age(img)
+    return gender, age, face
+
 image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-examples = ['Male.jpg', 'Female.jpg']
+gender_output = gr.outputs.Label()
+age_output = gr.outputs.Textbox(label='Predicted Age')
+detected_face_output = gr.outputs.Image(type='numpy', label='Detected Face')
+examples = ['Male.jpg', 'Female.png']
 
 
-iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+iface = gr.Interface(fn=process_image, inputs=image, outputs=[gender_output, age_output, detected_face_output], examples=examples)
 iface.launch()