import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import make_classification, make_moons, make_circles, make_blobs
from sklearn.model_selection import train_test_split, learning_curve
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from mlxtend.plotting import plot_decision_regions

# Image
st.image("https://huggingface.co/spaces/varshitha22/DecisionBoundaries_Learningcurves_Algorithms/resolve/main/logo.png")
st.markdown("<br>", unsafe_allow_html=True)

# Sidebar for dataset selection
st.sidebar.header("Dataset Options")
data_type = st.sidebar.selectbox("Select Data Type:", ["Blobs", "Circles", "Moons", "Classification"])
noise = st.sidebar.slider("Add Noise:", 0.0, 1.0, 0.2, step=0.05)

# Sidebar for model selection
st.sidebar.header("Model Selection")
model_name = st.sidebar.radio("Choose a Model:", ["KNN", "Decision Tree", "Naive Bayes", "Logistic Regression", "SVC"])

# Display KNN specific settings only if KNN is selected
if model_name == "KNN":
    neighbors = st.sidebar.number_input("Neighbors", min_value=1, max_value=25, value=5, step=1)
    knn_weights = st.sidebar.radio("KNN Weights:", ["uniform", "distance"])

    # KNN Algorithm
    st.sidebar.subheader("KNN Algorithm")
    algorithms_selected = []
    if st.sidebar.checkbox("auto", value=True):
        algorithms_selected.append("auto")
    if st.sidebar.checkbox("ball_tree"):
        algorithms_selected.append("ball_tree")
    if st.sidebar.checkbox("kd_tree"):
        algorithms_selected.append("kd_tree")
    if st.sidebar.checkbox("brute"):
        algorithms_selected.append("brute")

    # KNN Metric
    st.sidebar.subheader("KNN Metric")
    metrics_selected = []
    if st.sidebar.checkbox("euclidean", value=True):
        metrics_selected.append("euclidean")
    if st.sidebar.checkbox("manhattan"):
        metrics_selected.append("manhattan")
    if st.sidebar.checkbox("minkowski"):
        metrics_selected.append("minkowski")
else:
    neighbors = None
    knn_weights = None
    algorithms_selected = []
    metrics_selected = []

# Generate dataset
if data_type == "Blobs":
    X, y = make_blobs(n_samples=5000, centers=2, cluster_std=noise, random_state=42)
elif data_type == "Circles":
    X, y = make_circles(n_samples=5000, noise=noise, factor=0.5, random_state=42)
elif data_type == "Moons":
    X, y = make_moons(n_samples=5000, noise=noise, random_state=42)
else:
    X, y = make_classification(n_samples=5000, n_features=2, n_classes=2, n_informative=2, n_redundant=0, random_state=42)

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model selection
if model_name == "KNN":
    model = KNeighborsClassifier(n_neighbors=neighbors, weights=knn_weights, algorithm=algorithms_selected[0] if algorithms_selected else 'auto', metric=metrics_selected[0] if metrics_selected else 'minkowski')
elif model_name == "Decision Tree":
    model = DecisionTreeClassifier(random_state=42)
elif model_name == "Naive Bayes":
    model = GaussianNB()
elif model_name == "Logistic Regression":
    model = LogisticRegression(max_iter=200, random_state=42)
else:
    model = SVC(probability=True, kernel='linear', random_state=42)

# Fit the model
model.fit(X_train, y_train)

# Predict and calculate metrics
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
auc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]) if hasattr(model, "predict_proba") else "N/A"

# --- Display model performance under the radio button ---
with st.sidebar:
    st.subheader(f"{model_name} Model Evaluation Metrics")
    st.write(f" Accuracy: {accuracy:.2f}")
    st.write(f" Precision: {precision:.2f}")
    st.write(f" Recall: {recall:.2f}")
    st.write(f" F1 Score: {f1:.2f}")
    st.write(f" AUC Score: {auc:.2f}")

# Plot dataset
st.subheader("Dataset Visualization")
fig, ax = plt.subplots()
sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, palette="coolwarm", s=50, edgecolor="k")
st.pyplot(fig)

# Decision Boundary
st.subheader("Decision Boundary")
fig, ax = plt.subplots()
plot_decision_regions(X_train, y_train, clf=model, legend=2)
st.pyplot(fig)

# Learning Curve
st.subheader("Learning Curve")
train_sizes, train_scores, test_scores = learning_curve(model, X_train, y_train, cv=5, train_sizes=np.linspace(0.1, 1.0, 10))
train_mean = np.mean(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)

fig, ax = plt.subplots()
ax.plot(train_sizes, train_mean, label='Train Accuracy', marker='o')
ax.plot(train_sizes, test_mean, label='Test Accuracy', marker='s')
ax.set_xlabel("Training Size")
ax.set_ylabel("Accuracy")
ax.legend()
st.pyplot(fig)