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app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+import cv2, joblib
+import keras
+from keras.models import load_model
+
+face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+eye_cascade = cv2.CascadeClassifier("haarcascade_eye.xml")
+model = load_model("hp.h5")
+label = {'Alan Rickman': 0,
+ 'Daniel Radcliffe': 1,
+ 'Emma Watson': 2,
+ 'Gary Oldman': 3,
+ 'Helena Bonham Carter': 4,
+ 'Maggie Smith': 5,
+ 'Michael Gambon': 6,
+ 'Ralph Fiennes': 7,
+ 'Robbie Coltrane': 8,
+ 'Rupert Grint': 9,
+ 'Tom Felton': 10}
+def get_cropped_image_if_2_eyes(img):
+    if (img is not None):
+        gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray,1.3,5)
+        for (x,y,w,h) in faces:
+            roi_gray = gray[y:y+h,x:x+w]
+            roi_color = img[y:y+h,x:x+w]
+            eyes = eye_cascade.detectMultiScale(roi_gray)
+            if len(eyes)>=2:
+                return roi_color
+
+def predict(img):
+    if img is not None:
+        # read = Image.open(img,"r")
+        arr = np.array(img)
+        crp_img = get_cropped_image_if_2_eyes(arr)
+
+    if crp_img is not None:
+        crp_img = np.array(crp_img)
+        resized_img = cv2.resize(img,(180,180))
+        empty = np.zeros((100,180,180,3))
+        empty[0]= resized_img
+        empty= empty/255
+        prediction = model.predict(empty)
+        return {list(label.keys())[i]: prediction[0][i] for i in range(11)}
+    
+image = gr.Image(height=180, width=180)
+labels = gr.Label(num_top_classes = 5)
+gr.Interface(fn=predict, inputs= image, outputs = labels).launch(share=True)
+

hp.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f2ac4c57947f6c2c31d4586d43a587e512ff9e9223edd796d23cd58ac7227b20
+size 16676936

requirements.txt

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+joblib
+keras
+matplotlib
+matplotlib-inline
+numpy
+opencv-python
+pickleshare
+Pillow
+streamlit
+tensorboard
+tensorboard-data-server
+tensorflow
+tensorflow-estimator
+tensorflow-hub
+tensorflow-io-gcs-filesystem
+