import streamlit as st
import pandas as pd
import numpy as np
from scipy import stats

class Analyzer:
    def perform_analysis(self, df):
        analysis_type = st.selectbox("Select analysis type", 
                                     ["Descriptive Statistics", "Correlation Analysis", "Hypothesis Testing", "Custom Query"])
        
        if analysis_type == "Descriptive Statistics":
            st.write(df.describe())
            
            if st.checkbox("Show additional statistics"):
                st.write("Skewness:")
                st.write(df.skew())
                st.write("Kurtosis:")
                st.write(df.kurtosis())
        
        elif analysis_type == "Correlation Analysis":
            corr_matrix = df.corr()
            st.write(corr_matrix)
            
            if st.checkbox("Show heatmap"):
                fig = px.imshow(corr_matrix, color_continuous_scale='RdBu_r')
                st.plotly_chart(fig)
        
        elif analysis_type == "Hypothesis Testing":
            test_type = st.selectbox("Select test type", ["T-Test", "ANOVA", "Chi-Square"])
            
            if test_type == "T-Test":
                col1 = st.selectbox("Select first column", df.columns)
                col2 = st.selectbox("Select second column", df.columns)
                t_stat, p_value = stats.ttest_ind(df[col1], df[col2])
                st.write(f"T-statistic: {t_stat}")
                st.write(f"P-value: {p_value}")
            
            elif test_type == "ANOVA":
                grouping_col = st.selectbox("Select grouping column", df.columns)
                value_col = st.selectbox("Select value column", df.columns)
                groups = [group for name, group in df.groupby(grouping_col)[value_col]]
                f_stat, p_value = stats.f_oneway(*groups)
                st.write(f"F-statistic: {f_stat}")
                st.write(f"P-value: {p_value}")
            
            elif test_type == "Chi-Square":
                col1 = st.selectbox("Select first column", df.columns)
                col2 = st.selectbox("Select second column", df.columns)
                contingency_table = pd.crosstab(df[col1], df[col2])
                chi2, p_value, dof, expected = stats.chi2_contingency(contingency_table)
                st.write(f"Chi-square statistic: {chi2}")
                st.write(f"P-value: {p_value}")
        
        elif analysis_type == "Custom Query":
            query = st.text_input("Enter a custom query (e.g., 'column_name > 5')")
            if query:
                try:
                    result = df.query(query)
                    st.write(result)
                except Exception as e:
                    st.error(f"Error in query: {str(e)}")