Create app.py

app.py

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+import numpy as np
+import cv2
+from keras.models import load_model
+import gradio as gr
+
+model_info=load_model("GenderPredict_Model.h5",compile=True)
+
+def Gender_prediction(img,choice):
+  value=-1
+  if(choice=="Through_Id_Card"):
+    face_classifier = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    scale_percent = 60 # percent of original size
+    width = int(img.shape[1] * scale_percent / 100)
+    height = int(img.shape[0] * scale_percent / 100)
+    dim = (width, height)
+    image = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
+    if faces is ():
+      print("No faces found")
+    for (x, y, w, h) in faces:
+      x = x - 25 # Padding trick to take the whole face not just Haarcascades points
+      y = y - 40 # Same here...
+      cv2.rectangle(image, (x, y), (x + w + 50, y + h + 70), (27, 200, 10), 2)
+    for (x, y, width, height) in faces:
+      roi = image[y:y+height, x:x+width]
+      cv2.imwrite("face.jpg",roi)  
+    img=cv2.resize(cv2.imread("face.jpg"),(224,224))
+    result=model_info.predict(img.reshape(1,224,224,3))
+    value=result.argmax()
+  elif(choice=="Through_Image"):
+    img=cv2.resize(img,(224,224))
+    result=model_info.predict(img.reshape(1,224,224,3))
+    value=result.argmax()
+  if(value==0):
+    return "You are Female"
+  elif(value==1):
+    return "You are male"
+  else:
+    return "No Predict please Choose any option"
+
+interface=gr.Interface(fn=Gender_prediction,inputs=[gr.components.Image(label="Choose Image",type="numpy"),gr.components.Radio(['Through_Id_Card','Through_Image'],type="value",label="Select any One")],
+                       outputs=[gr.components.Textbox(label="Your Result")])
+interface.launch(debug=True)