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app.py

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+import numpy as np
+import cv2 as cv2
+from tensorflow import keras
+import gradio as gr
+import matplotlib.pyplot as plt
+import os
+model = keras.models.load_model('./model1.h5')
+
+def pipeline(img_path, model= model):
+  img = plt.imread(img_path)
+  width = 96
+  height = 96
+  dim = (width, height)
+  resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
+  resized_gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
+  formato = resized_gray.reshape(96,96,1)
+  formato = np.repeat(formato, 3, axis=2)
+  formato = np.expand_dims(formato,0)
+  puntos = model.predict(formato)
+  plt.imshow(resized)
+  for i in range(1,31,2):
+      plt.plot(puntos[0][i-1], puntos[0][i], 'ro')
+  plt.savefig('tran.jpg')
+  img =  cv2.imread('tran.jpg')
+  img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+  os.remove('tran.jpg')
+  plt.clf()
+  return img
+  
+examples=[]
+examples.append("./1.png")
+examples.append("./2.png")
+examples.append("./3.png")
+examples.append("./4.png")
+examples.append("./5.png")
+
+gr.Interface(
+    pipeline,
+    inputs=gr.inputs.Image(label="Upload THE FACEEEEOOOO", type="filepath"),
+    outputs=gr.outputs.Image(type="numpy"),
+    title="point on your uglo face >:(",
+    examples=examples
+).launch()