Update app.py

app.py

@@ -1,6 +1,39 @@
 import streamlit as st
 from transformers import pipeline
 
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+
+# # Load the California Housing dataset
+# data = fetch_california_housing(as_frame=True)
+# X = data.data
+# y = data.target
+
+# # Split the dataset into training and test sets
+# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# # Standardize features
+# scaler = StandardScaler()
+# X_train = scaler.fit_transform(X_train)
+# X_test = scaler.transform(X_test)
+
+# # Train the model
+# model = LinearRegression()
+# model.fit(X_train, y_train)
+
+# # Make predictions on the test set
+# y_pred = model.predict(X_test)
+
+# # Evaluate the model
+# mse = mean_squared_error(y_test, y_pred)
+# r2 = r2_score(y_test, y_pred)
+
+# print(f"Mean Squared Error: {mse:.2f}")
+# print(f"R-squared Score: {r2:.2f}")
+
 sentiment_pipeline = pipeline("sentiment-analysis")
 
 st.title("Sentiment Analysis with HuggingFace Spaces")