Create app.py

app.py

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+import tensorflow_hub as hub
+import pickle
+import sklearn
+
+embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4") 
+
+with open('./model.pck', 'rb') as f:
+    model = pickle.load(f)
+
+import gradio as gr
+
+def convert(text):
+    #Se genera el embedding del texto
+    text_embed = embed([text])
+    #El modelo hace su predicción
+    prediction = model.predict_proba(text_embed).flatten()
+    #Se devuelve el percentaje que el modelo ha predicho para cada etiqueta
+    return {"ham": float(prediction[0]), "spam" : float(prediction[1])}
+
+
+iface = gr.Interface(
+    fn=convert, 
+    inputs="text",
+    outputs="label",
+    examples=["I will help you win the lottery, my friend", "Please, darling, could you pick up the kids from school today?"],
+    title="Ham or spam?",
+    description="Copy the text message you just received and we'll let you know if it is ham or spam",
+)
+
+iface.launch()