Update app.py

app.py

@@ -1,28 +1,23 @@
 import gradio as gr
-from joblib import load
-from huggingface_hub import hf_hub_download
-import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from joblib import dump
+import os
 
-# Baixe o modelo do Hugging Face Hub
-model_path = hf_hub_download(
-    repo_id="coan/botClaiton",
-    filename="model.pkl",
-    use_auth_token=True  # Caso o repositório seja privado
-)
+def train_model():
+    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)
 
-# Carregue o modelo
-model = joblib.load(model_path)
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
 
-# Carregar o modelo diretamente do Hugging Face
-model_path = hf_hub_download(repo_id="coan/botClaiton", filename="model.joblib")
-model = load(model_path)
+    model_filename = "/mnt/data/model.pkl"
+    dump(model, model_filename)
+    
+    return f"Modelo treinado e salvo em: {model_filename}"
 
-# Função de previsão
-def predict_price(open_price, high_price, low_price, volume):
-    # Criar um dataframe com os dados de entrada
-    data = pd.DataFrame({
-        "Open": [open_price],
-        "High": [high_price],
-        "Low": [low_price],
-        "Volume": [volume],
-    })
+iface = gr.Interface(fn=train_model, inputs=[], outputs=["text"])
+iface.launch()