Upload app.py

app.py

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+
+import streamlit as st
+import tensorflow as tf
+import keras
+import numpy as np
+from PIL import Image
+
+# Load the model
+model = tf.keras.models.load_model("/content/modelo_celeba_50e.h5", compile=False)
+
+# Create a title
+st.title("Clasificador de imágenes de CelebA")
+
+# Add a button to upload an image
+uploaded_image = st.file_uploader("Cargar imagen")
+
+# If an image is uploaded, process it
+if uploaded_image is not None:
+
+    # Convert the uploaded file to a PIL Image object
+    image = Image.open('/content/Uriel_foto.png').convert('RGB')
+    image = image.resize((178,218))
+
+    # Convert the PIL Image object to a NumPy array
+    image = np.array(image)/255.0
+
+    image = np.expand_dims(image,axis=0)
+
+    # Make a prediction
+    prediction = model.predict(image)
+
+    # Display the prediction
+    st.write("La predicción es:")
+    st.write(prediction)