import streamlit as st
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report

# Load your data (replace with your actual data loading)
# Assuming you have a CSV file named 'penguins_lter.csv' in your working directory
try:
    df = pd.read_csv('penguins_lter.csv')
except FileNotFoundError:
    st.error("Error: 'penguins_lter.csv' not found. Please upload the file or adjust the path.")
    st.stop()


# Data preprocessing (handle missing values)
numeric_cols = df.select_dtypes(include=['number']).columns
for col in numeric_cols:
    df[col].fillna(df[col].mean(), inplace=True)

categorical_cols = df.select_dtypes(exclude=['number']).columns
for col in categorical_cols:
    df[col].fillna(df[col].mode()[0], inplace=True)


# Model training and prediction (same as your original code)

# Assuming 'Species' is your target variable
X = df.drop('Species', axis=1)
y = df['Species']

# Convert categorical features to numerical using one-hot encoding
X = pd.get_dummies(X, drop_first=True)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a pipeline
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('knn', KNeighborsClassifier(n_neighbors=5))
])

# Train the pipeline
pipeline.fit(X_train, y_train)

# Make predictions
y_pred = pipeline.predict(X_test)


# Streamlit app
st.title("Penguin Species Classification")

st.write("This app predicts the species of a penguin based on its physical characteristics.")

# Display classification report
st.subheader("Classification Report")
st.text(classification_report(y_test, y_pred))

st.dataframe(df.head())