Delete app.py

app.py

@@ -1,72 +0,0 @@
-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, OneHotEncoder
-from sklearn.compose import ColumnTransformer
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.metrics import accuracy_score
-
-# Load your data (replace with your actual data loading)
-# Assuming penguins.csv is in the same directory as your Streamlit app
-try:
-    penguins = pd.read_csv('penguins_lter.csv')
-except FileNotFoundError:
-    st.error("Error: penguins_lter.csv not found. Please make sure the file is in the same directory as the app.")
-    st.stop()
-
-# Preprocessing steps (same as your original code)
-penguins = penguins.dropna()
-penguins.drop_duplicates(inplace=True)
-
-
-# Streamlit app
-st.title('Penguin Species Prediction')
-
-# Sidebar for user input
-st.sidebar.header('Input Features')
-
-island = st.sidebar.selectbox('Island', penguins['Island'].unique())
-culmen_length = st.sidebar.slider('Culmen Length (mm)', float(penguins['Culmen Length (mm)'].min()), float(penguins['Culmen Length (mm)'].max()), float(penguins['Culmen Length (mm)'].mean()))
-culmen_depth = st.sidebar.slider('Culmen Depth (mm)', float(penguins['Culmen Depth (mm)'].min()), float(penguins['Culmen Depth (mm)'].max()), float(penguins['Culmen Depth (mm)'].mean()))
-flipper_length = st.sidebar.slider('Flipper Length (mm)', float(penguins['Flipper Length (mm)'].min()), float(penguins['Flipper Length (mm)'].max()), float(penguins['Flipper Length (mm)'].mean()))
-body_mass = st.sidebar.slider('Body Mass (g)', float(penguins['Body Mass (g)'].min()), float(penguins['Body Mass (g)'].max()), float(penguins['Body Mass (g)'].mean()))
-sex = st.sidebar.selectbox('Sex', penguins['Sex'].unique())
-
-# Create input DataFrame
-input_data = pd.DataFrame({
-    'Island': [island],
-    'Culmen Length (mm)': [culmen_length],
-    'Culmen Depth (mm)': [culmen_depth],
-    'Flipper Length (mm)': [flipper_length],
-    'Body Mass (g)': [body_mass],
-    'Sex': [sex]
-})
-
-# Prepare the model (same as before, including your pipeline)
-X = penguins.drop('Species', axis=1)
-y = penguins['Species']
-
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-numerical_features = ['Culmen Length (mm)', 'Culmen Depth (mm)', 'Flipper Length (mm)', 'Body Mass (g)']
-categorical_features = ['Island', 'Sex']
-
-numerical_transformer = Pipeline(steps=[('scaler', StandardScaler())])
-categorical_transformer = Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))])
-
-preprocessor = ColumnTransformer(
-    transformers=[
-        ('num', numerical_transformer, numerical_features),
-        ('cat', categorical_transformer, categorical_features)
-    ])
-
-pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', KNeighborsClassifier())])
-pipeline.fit(X_train, y_train)
-
-# Make prediction
-prediction = pipeline.predict(input_data)
-
-# Display prediction
-st.subheader('Prediction')
-st.write(f"Predicted Penguin Species: {prediction[0]}")