Update app.py

app.py

@@ -1,15 +1,81 @@
-from fastapi import FastAPI
-from transformers import pipeline
- 
-# Create a new FastAPI app instance
-app = FastAPI()
- 
-pipe = pipeline("text2text-generation", 
-model="mat27/medmnistPrueba")
-
-@app.get("/generate")
-def generate(imagen: np.array):
-    
-    output = pipe(imagen)
-    
-    return {"Precisión: ": output}
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+from huggingface_hub import push_to_hub_keras
+from tensorflow import keras
+from tensorflow.keras import layers
+
+
+# Model / data parameters
+num_classes = 9
+input_shape = (28, 28, 3)
+batch_size = 1000
+epochs = 
+
+# 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)
+
+# Evaluation of the model
+score = model.evaluate(x_test, y_test, verbose=0)
+
+print("Test loss:", score[0])
+print("Test accuracy:", score[1])