app.py

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

model = load_model('/content/bananafreshness/pisang.h5')
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 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(prediksi, gr.Image(shape=(200, 200)), "text")
demo.launch()