Create app.py

app.py

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+import numpy as np
+import gradio as gr
+from tensorflow.keras.models import load_model
+import imutils
+import matplotlib.pyplot as plt
+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...")
+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):
+    image = cv2.resize(image, (128, 128))
+    image = image.astype("float") / 255.0
+    image = img_to_array(image)
+    image = np.expand_dims(image, axis=0)
+    
+    
+    proba = model.predict(image)[0]
+    idxs = np.argsort(proba)[::-1][:2]
+    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()