Create app.py

app.py

@@ -1,43 +1,7 @@
 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 greet(name):
+    return "Hello " + name + "!!"
 
-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_.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",
-    )
-
-iface.launch(share=True)
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()