import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

def main():
    st.title("Excel Column Analysis Dashboard")
    
    uploaded_file = st.file_uploader("Upload an Excel file", type=["xls", "xlsx"])
    
    if uploaded_file is not None:
        df = pd.read_excel(uploaded_file)
        st.write("Preview of Data:")
        st.write(df.head())
        
        numeric_columns = df.select_dtypes(include=[np.number]).columns.tolist()
        
        if numeric_columns:
            selected_column = st.selectbox("Select a column for analysis (as named in the Excel file)", numeric_columns)
            
            if selected_column:
                data = df[selected_column].dropna()
                std_dev = np.std(data, ddof=1)
                
                st.write(f"**Calculated Standard Deviation:** {std_dev:.4f}")
                
                fig, axes = plt.subplots(1, 2, figsize=(12, 5))
                
                sns.histplot(data, kde=True, ax=axes[0], bins=20, color='blue')
                axes[0].set_title(f"Distribution Plot of {selected_column}")
                
                sns.lineplot(x=data.index, y=data, ax=axes[1], label='Data')
                axes[1].axhline(y=np.mean(data), color='r', linestyle='--', label='Mean')
                axes[1].axhline(y=np.mean(data) + std_dev, color='g', linestyle='--', label='+1 Std Dev')
                axes[1].axhline(y=np.mean(data) - std_dev, color='g', linestyle='--', label='-1 Std Dev')
                axes[1].legend()
                axes[1].set_title(f"Standard Deviation Plot of {selected_column}")
                
                st.pyplot(fig)
        else:
            st.warning("No numeric columns found in the uploaded file.")

if __name__ == "__main__":
    main()