Create app.py

app.py

@@ -0,0 +1,48 @@
+from keras.models import load_model  # TensorFlow is required for Keras to work
+from PIL import Image, ImageOps  # Install pillow instead of PIL
+import numpy as np
+import gradio as gr
+
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
+# Load the model
+model = load_model("/content/keras_model.h5", compile=False)
+
+# Load the labels
+class_names = open("/content/labels.txt", "r").readlines()
+
+# Create the array of the right shape to feed into the keras model
+# The 'length' or number of images you can put into the array is
+# determined by the first position in the shape tuple, in this case 1
+data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+
+def saluda(img):
+  # Replace this with the path to your image
+  image = img
+
+  # resizing the image to be at least 224x224 and then cropping from the center
+  size = (224, 224)
+  image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
+
+  # turn the image into a numpy array
+  image_array = np.asarray(image)
+
+  # Normalize the image
+  normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+
+  # Load the image into the array
+  data[0] = normalized_image_array
+
+  # Predicts the model
+  prediction = model.predict(data)
+  index = np.argmax(prediction)
+  class_name = class_names[index]
+  confidence_score = prediction[0][index]
+
+  return class_name[2:], confidence_score
+
+imatge_entrada = gr.Image(type='pil')
+demo = gr.Interface(fn=saluda, inputs=imatge_entrada, outputs=["text", "text"])
+
+demo.launch()