Update app.py

app.py

@@ -7,23 +7,14 @@ import cv2
 import numpy as np
 from tensorflow.keras.preprocessing.image import img_to_array
 from tensorflow.keras.models import load_model
-print("[INFO] loading network...")
+import pickle
+
 model = load_model('daging128.model')
 mlb = pickle.loads(open('daging128.pickle', "rb").read())
 
 labels = ['Busuk', 'Segar', 'Setengah']
 
-def prosesgambar(gambar):
-  # load the image
-  image = gambar
-  output = imutils.resize(image, width=400)
-  
-  # pre-process the image for classification
-  image = cv2.resize(image, (94, 94))
-  image = image.astype("float") / 255.0
-  image = img_to_array(image)
-  image = np.expand_dims(image, axis=0)
-  return image
+
 
 
 def gambaran(image):
@@ -38,13 +29,6 @@ def gambaran(image):
     return labels[idxs[0]]
 
 
-def prediksi(gambar):
-  a = np.round(model.predict(prosesgambar(gambar)), 4)[0].tolist()
-  if a.index(max(a)) == 1:
-    pred = "Segar"
-  else:
-    pred = "Busuk"
-  return pred
 
 demo = gr.Interface(gambaran, gr.Image(shape=(128, 128)), "text")
 demo.launch()