Upload app.py

app.py

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+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import numpy as np
+
+reloaded_model = from_pretrained_keras('jmparejaz/Facial_Age-gender-eth_Recognition')
+reloaded_model_eth = from_pretrained_keras('jmparejaz/Facial_eth_recognition')
+
+def rgb2gray(rgb):
+    return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])
+
+
+def predict_model(x_, model_1, model_2):
+  pred = model_1.predict(X_test.reshape(x_.shape[0], 48, 48, 1))
+  pred_eth=model_2.predict(x_.reshape(x_.shape[0], 48, 48, 1))
+  pred_gender=[round(pred[0][x][0]) for x in range(x_.shape[0])]
+  pred_age=[round(pred[1][x][0]) for x in range(x_.shape[0])]
+  pred_eth=[np.argmax(pred_eth[x]) for x in range(x_.shape[0])]
+
+  return pred_gender, pred_age, pred_eth
+
+
+def image_classifier(input_img):
+    gray=rgb2gray(input_img)
+    g,a,e=predict_model(gray.reshape(1, 48, 48, 1),reloaded_model,reloaded_model_eth)
+    
+    dict_gender={ 0: 'Male', 1:'Female'}
+    g=dict_gender[g]
+    dict_eth={0:"White", 1:"Black", 2:"Asian", 3:"Indian", 4:"Hispanic"}
+    e=dict_eth[e]
+    return ("The predicted gender is {} , predicted age is {} and the predicted ethnicity is {}".format(g,a,e))
+
+
+
+
+iface = gr.Interface(
+    image_classifier,
+    gr.inputs.Image(shape=(48, 48),), 
+    outputs=['text']
+    capture_session=True,
+    interpretation="default",
+    )
+
+if __name__ == "__main__":
+    iface.launch(share=True)