Update app.py

app.py

@@ -1,20 +1,26 @@
+import gradio as gr
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
 from joblib import dump
 
-# Carregar e dividir o dataset
-data = load_iris()
-X = data.data
-y = data.target
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+def train_model():
+    # Carregar e dividir o dataset
+    data = load_iris()
+    X = data.data
+    y = data.target
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
-# Treinando o modelo
-model = LogisticRegression()
-model.fit(X_train, y_train)
+    # Treinando o modelo
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
 
-# Salvar o modelo em um arquivo
-model_filename = "model.pkl"
-dump(model, model_filename)
+    # Salvar o modelo em um arquivo na pasta /mnt/data/
+    model_filename = "/mnt/data/model.pkl"
+    dump(model, model_filename)
+    
+    return f"Modelo treinado e salvo em: {model_filename}"
 
-print("Modelo treinado e salvo!")
+# Defina a interface Gradio
+iface = gr.Interface(fn=train_model, inputs=[], outputs=["text"])
+iface.launch()