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

# Load dataset
def load_data(file_path):
    try:
        return pd.read_csv(file_path, encoding='utf-8')
    except UnicodeDecodeError:
        return pd.read_csv(file_path, encoding='ISO-8859-1')

# Convert 'Yes'/'No' to 1/0
def convert_yes_no(df, start_idx):
    for col in df.columns[start_idx:]:
        df[col] = df[col].str.strip().str.lower().map({'yes': 1, 'no': 0})
    return df

# Calculate AI Competency Score
def calculate_ai_score(df, start_idx):
    df['AI Competency Score'] = df.iloc[:, start_idx:].sum(axis=1)
    return df

# Plot distribution for columns 0 to 9
def plot_distributions(df):
    st.subheader("Distributions for Columns 0 to 9")
    for col in df.columns[:10]:
        plt.figure(figsize=(8, 4))
        sns.countplot(data=df, x=col)
        plt.xticks(rotation=45)
        st.pyplot(plt)

# Plot average AI Competency Score grouped by selected column
def plot_grouped_avg(df, group_col):
    grouped = df.groupby(group_col)['AI Competency Score'].mean().reset_index()
    plt.figure(figsize=(8, 4))
    sns.barplot(data=grouped, x=group_col, y='AI Competency Score')
    plt.xticks(rotation=45)
    st.pyplot(plt)

# Main app
def main():
    st.title("AI Competency Analysis App")

    # Load file directly from local directory
    file_path = 'AI_Competency_DIT.csv'
    df = load_data(file_path)
    st.subheader("Dataset Preview")
    st.dataframe(df)

    # Distributions for columns 0 to 9
    plot_distributions(df)

    # Unique responses for column 10
    st.subheader(f"Unique Responses for: {df.columns[10]}")
    unique_responses = df.iloc[:, 10].dropna().unique()
    st.write(unique_responses)

    # Convert Yes/No to 1/0 for columns 11 onwards
    df = convert_yes_no(df, 11)

    # Calculate AI Competency Score
    df = calculate_ai_score(df, 11)
    st.subheader("Dataset with AI Competency Score")
    st.dataframe(df)

    # Grouped average AI Competency Score
    st.subheader("Average AI Competency Score by Group")
    group_by_col = st.selectbox("Select column to group by", df.columns[:9])
    plot_grouped_avg(df, group_by_col)

    # Additional Descriptive Statistics
    st.subheader("Descriptive Statistics")
    st.write(df.describe())

    # Correlation heatmap
    st.subheader("Correlation Heatmap")
    plt.figure(figsize=(12, 8))
    sns.heatmap(df.iloc[:, 11:].corr(), annot=True, cmap='coolwarm')
    st.pyplot(plt)

if __name__ == '__main__':
    main()