Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+import matplotlib.pyplot as plt
+
+# Title
+st.title("AI-Powered Load Forecasting")
+
+# Upload Dataset
+st.sidebar.header("Upload Data")
+uploaded_file = st.sidebar.file_uploader("Upload your CSV file", type=["csv"])
+if uploaded_file is not None:
+    data = pd.read_csv(uploaded_file)
+    st.write("Data Preview")
+    st.write(data.head())
+else:
+    st.info("Awaiting CSV file upload. You can use the sample dataset.")
+    # Load sample data
+    data = pd.read_csv("sample_data.csv")
+    st.write("Using Sample Data")
+    st.write(data.head())
+
+# Feature Selection
+st.sidebar.header("Feature Selection")
+target_variable = st.sidebar.selectbox("Select Target Variable", options=data.columns, index=len(data.columns) - 1)
+predictors = st.sidebar.multiselect("Select Predictor Variables", options=[col for col in data.columns if col != target_variable], default=data.columns[:-1])
+
+# Model Training
+if st.sidebar.button("Train Model"):
+    st.subheader("Training the Model...")
+
+    # Split data
+    X = data[predictors]
+    y = data[target_variable]
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Train Random Forest Regressor
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+
+    # Model Evaluation
+    st.write("**Evaluation Metrics:**")
+    st.write(f"Mean Absolute Error (MAE): {mean_absolute_error(y_test, y_pred):.2f}")
+    st.write(f"Mean Squared Error (MSE): {mean_squared_error(y_test, y_pred):.2f}")
+    st.write(f"R² Score: {r2_score(y_test, y_pred):.2f}")
+
+    # Plot Results
+    fig, ax = plt.subplots()
+    ax.plot(y_test.values, label="Actual", marker="o")
+    ax.plot(y_pred, label="Predicted", marker="x")
+    ax.legend()
+    ax.set_title("Actual vs. Predicted Load")
+    st.pyplot(fig)
+
+# Footer
+st.sidebar.markdown("Developed by [Sunny Nazir](https://huggingface.co/spaces)")