Update app.py

app.py

@@ -5,18 +5,29 @@ from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error, r2_score
 
-# Load the dataset
+# Function to load data
 @st.cache_data
-def load_data():
-    url = "https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv"
+def load_data_from_url(url):
     data = pd.read_csv(url)
     return data
 
 # App title
 st.title("House Price Prediction")
 
+# Sidebar for user inputs
+st.sidebar.header("Upload Your Data")
+uploaded_file = st.sidebar.file_uploader("Upload a CSV file", type=["csv"])
+data_url = st.sidebar.text_input("Or enter a URL to a CSV file")
+
 # Load data
-data = load_data()
+if uploaded_file:
+    data = pd.read_csv(uploaded_file)
+elif data_url:
+    data = load_data_from_url(data_url)
+else:
+    st.write("Please upload a CSV file or enter a URL to a CSV file.")
+    st.stop()
+
 st.write("Dataset", data)
 
 # Feature selection
@@ -25,7 +36,7 @@ selected_features = st.sidebar.multiselect("Select features", data.columns[:-1])
 
 if selected_features:
     X = data[selected_features]
-    y = data["medv"]  # Median value of homes
+    y = data.iloc[:, -1]  # Assuming the last column is the target
 
     # Split data
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
@@ -37,6 +48,15 @@ if selected_features:
     # Prediction
     y_pred = model.predict(X_test)
 
+    # Display results
+    st.write("Selected Features", selected_features)
+    st.write("Model Coefficients", model.coef_)
+    st.write("Predictions", y_pred)
+    st.write("Actual Values", y_test.values)
+
+    # Model performance
+    st.write("Mean Squared Error", mean_squared_error(y_test, y_pred))
+    st.write("R-squared Score", r2_score(y_test, y_pred))
 
     # Plot
     fig, ax = plt.subplots()
@@ -46,12 +66,3 @@ if selected_features:
     ax.set_ylabel('Predicted')
     ax.set_title('Actual vs Predicted')
     st.pyplot(fig)
-
-    # Model performance
-    st.write("Mean Squared Error", mean_squared_error(y_test, y_pred))
-    st.write("R-squared Score", r2_score(y_test, y_pred))
-    # Display results
-    st.write("Selected Features", selected_features)
-    st.write("Model Coefficients", model.coef_)
-    st.write("Predictions", y_pred)
-    st.write("Actual Values", y_test.values)