import streamlit as st
import pickle
import numpy as np

# Load the pre-trained model
with open('model.pkl', 'rb') as file:
    model = pickle.load(file)

# Define the mapping for encoded species values   
# Bream', 'Roach', 'Whitefish', 'Parkki', 'Perch', 'Pike', 'Smelt
species_mapping = {
    0: 'Bream',
    1: 'Roach',
    2: 'Whitefish',
    3: 'Parkki',
    4: 'Perch',
    5: 'Pike',
    6: 'Smelt',
    # Add other species mappings as needed
}

# Create reverse mapping from species name to encoded value
reverse_species_mapping = {v: k for k, v in species_mapping.items()}

# Streamlit app
st.title('Fish Weight Prediction')

# Select box for species
species_options = list(species_mapping.values())
selected_species = st.selectbox('Select Species', species_options)

# Convert selected species to encoded value
species_encoded = reverse_species_mapping.get(selected_species, 0)  # Default to 0 if not found

# Input fields for the user to enter data as text inputs
length1 = st.text_input('Length1 (cm) [Range: 0.0 - 100.0]', '0.0')
length2 = st.text_input('Length2 (cm) [Range: 0.0 - 100.0]', '0.0')
length3 = st.text_input('Length3 (cm) [Range: 0.0 - 100.0]', '0.0')
height = st.text_input('Height (cm) [Range: 0.0 - 30.0]', '0.0')
width = st.text_input('Width (cm) [Range: 0.0 - 30.0]', '0.0')

# Convert text inputs to floats and handle errors
try:
    length1 = float(length1)
    length2 = float(length2)
    length3 = float(length3)
    height = float(height)
    width = float(width)
except ValueError:
    st.error("Please enter valid numerical values.")
    length1 = length2 = length3 = height = width = 0.0

# Button to make prediction
if st.button('Predict'):
    # Prepare the input data for the model
    input_data = np.array([[length1, length2, length3, height, width, species_encoded]])
    
    # Make prediction
    predicted_weight = model.predict(input_data)
    
    # Display the result
    st.write(f'The predicted weight is: {predicted_weight[0]:.2f} grams')

st.markdown("""
    <div style='text-align: center; padding: 20px;'>
        <h4>Created by Sukhman</h4>
        <p>This Streamlit app predicts the weight of Fish based on its features.</p>
    </div>
""", unsafe_allow_html=True)