# To run this app, use: streamlit run test.py
import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Application title and description
st.title("Machine Learning Model Visualization")
st.write("This application demonstrates random forest classification on the iris dataset")

# Data acquisition and preparation
@st.cache_data
def load_data():
    from sklearn.datasets import load_iris
    iris = load_iris()
    df = pd.DataFrame(iris.data, columns=iris.feature_names)
    df['target'] = iris.target
    return df, iris.target_names

data, target_names = load_data()

# Interactive data exploration
st.subheader("Dataset Exploration")
if st.checkbox("Display dataset"):
    st.dataframe(data)

# Feature selection interface
st.subheader("Feature Selection")
features = st.multiselect(
    "Select features for model training",
    options=data.columns[:-1],
    default=data.columns[0]
)

if len(features) > 0:
    # Model parameters adjustment
    st.subheader("Model Parameters")
    n_estimators = st.slider("Number of trees", 1, 100, 10)
    max_depth = st.slider("Maximum tree depth", 1, 20, 5)
    
    # Model training
    if st.button("Train Model"):
        X = data[features]
        y = data['target']
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
        
        model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42)
        model.fit(X_train, y_train)
        
        # Performance evaluation
        y_pred = model.predict(X_test)
        accuracy = accuracy_score(y_test, y_pred)
        
        st.success(f"Model accuracy: {accuracy:.4f}")
        
        # Visualization of feature importance
        if len(features) > 1:
            st.subheader("Feature Importance")
            fig, ax = plt.subplots()
            ax.bar(features, model.feature_importances_)
            plt.xticks(rotation=45)
            st.pyplot(fig)
else:
    st.warning("Please select at least one feature for model training")