Update app.py

app.py

@@ -2,77 +2,13 @@ import numpy as np
 import matplotlib.pyplot as plt
 import time
 
+from huggingface_hub import from_pretrained_keras
 from tensorflow import keras
 from tensorflow.keras import layers
 app = FastAPI()
 
-
-# Model / data parameters
-num_classes = 9
-input_shape = (28, 28, 3)
-batch_size = 1000
-epochs = 1
-
-# Define baseline model
-def baseline_model():
-
-  # Create model
-  model = keras.Sequential(
-    [
-        keras.Input(shape=input_shape),
-        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
-        layers.Conv2D(128, kernel_size=(3, 3), activation="relu"),
-        layers.Flatten(),
-        layers.Dropout(0.5),
-        layers.Dense(num_classes, activation="softmax"),
-    ]
-  )
-  model.summary()
-
-  # Compile model
-  model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
-
-  return model
-
-# Load Data
-path = './pathmnist.npz'
-with np.load(path) as data:
-  x_train = data['train_images']
-  y_train = data['train_labels']
-  x_test = data['test_images']
-  y_test = data['test_labels']
-  x_val = data['val_images']
-  y_val = data['val_labels']
-
-# Show DataSet Images
-for image in x_train:
-  plt.imshow(image)
-  plt.show()
-  break
-
-# Normalize images to the [0, 1] range
-x_train = x_train.astype("float32") / 255
-x_test = x_test.astype("float32") / 255
-x_val = x_val.astype("float32") / 255
-
-print("x_train shape:", x_train.shape)
-print(x_train.shape[0], "train samples")
-print(x_test.shape[0], "test samples")
-print(x_val.shape[0], "test samples")
-
-# Convert class vectors to binary class matrices
-y_train = keras.utils.to_categorical(y_train, num_classes)
-y_test = keras.utils.to_categorical(y_test, num_classes)
-y_val = keras.utils.to_categorical(y_val, num_classes)
-
-model = baseline_model()
-
-# Fit model
-#history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
-inicio = time.time()
-history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val))
-fin = time.time()
-print(fin-inicio)
+model = from_pretrained_keras("mat27/medmnsitPrueba")
+model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
 
 @app.get("/generate")
 def generate(x: np.array):