Subida inicial del modelo

.gitattributes

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inference.py

@@ -1,15 +1,28 @@
-import pickle
+import joblib
+import numpy as np
 from sklearn.feature_extraction.text import TfidfVectorizer
 
-# Cargar el modelo y el vectorizador
+# Cargar el vectorizador usando joblib
 with open('vectorizer.pkl', 'rb') as f:
-    vectorizer = pickle.load(f)
+    vectorizer = joblib.load(f)
 
+# Cargar el modelo usando joblib
 with open('model.pkl', 'rb') as f:
-    model = pickle.load(f)
+    model = joblib.load(f)
+
+def preprocess(text):
+    """Preprocesa el texto para la inferencia."""
+    # Transformar el texto usando el vectorizador cargado
+    return vectorizer.transform([text])
 
 def predict(text):
-    # Preprocesar el texto usando el vectorizador
-    X = vectorizer.transform([text])
-    # Hacer una predicción usando el modelo
+    """Realiza la predicción a partir del texto ingresado."""
+    X = preprocess(text)
+    # Realizar la predicción usando el modelo cargado
     return model.predict(X)
+
+if __name__ == "__main__":
+    # Probar el modelo con un texto de ejemplo
+    test_text = "Este es un ejemplo de texto para predecir"
+    result = predict(test_text)
+    print(f"Predicción: {result}")

requirements.txt

@@ -2,4 +2,5 @@ pandas
 scikit-learn
 imbalanced-learn
 joblib
-numpy
+numpy
+pickle