app.py

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 file path)
df = pd.read_csv('penguins_lter.csv')

# Data preprocessing (same as in your previous code)
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)

# Feature Engineering and Model Training (same as in your previous code)
X = df.drop('Species', axis=1)
y = df['Species']
X = pd.get_dummies(X, drop_first=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('knn', KNeighborsClassifier(n_neighbors=5))
])
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
report = classification_report(y_test, y_pred, output_dict=True)

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

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

# Display the classification report
st.subheader("Classification Report")
st.write(pd.DataFrame(report).transpose())


# Add input fields for user input (example)
st.sidebar.header("Penguin Features")

# Example input fields (replace with your actual features)
bill_length_mm = st.sidebar.number_input("Bill Length (mm)", min_value=0.0, value=40.0)
bill_depth_mm = st.sidebar.number_input("Bill Depth (mm)", min_value=0.0, value=15.0)
# ... Add more input fields for other features ...

#Create a dictionary to store the user inputs
user_input_dict = {
    'bill_length_mm': bill_length_mm,
    'bill_depth_mm': bill_depth_mm,
    # ... Add other features here
}

# Create a dataframe for prediction
user_input_df = pd.DataFrame([user_input_dict])
user_input_df = pd.get_dummies(user_input_df, drop_first=True) # Apply the same one-hot encoding


if st.sidebar.button("Predict"):
    # Align the columns of user_input_df and X_train
    missing_cols = set(X_train.columns) - set(user_input_df.columns)
    for c in missing_cols:
        user_input_df[c] = 0  # Add missing columns with value 0
    user_input_df = user_input_df[X_train.columns] # Reorder the columns

    prediction = pipeline.predict(user_input_df)
    st.write(f"Predicted Species: {prediction[0]}")