Add app file

abalone_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:77eead0e5e422c15d945d25a0e028171e3227f2cd7cec63404c3e0a70958efdb
+size 13028

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+
+# Load the pre-trained model
+with open('abalone_model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Define the prediction function
+def predict_age(sex, length, diameter, height, whole_weight, shucked_weight, viscera_weight, shell_weight):
+    shell_volume = length * diameter * height
+    meat_ratio = whole_weight / shell_weight
+    bmi = whole_weight / (length ** 2)
+    shell_surface_area = 2 * (length * diameter + length * height + diameter * height)
+    volume_to_weight_ratio = shell_volume / whole_weight
+    shell_weight_to_length_ratio = shell_weight / length
+    meat_weight_to_lenth_ratio = whole_weight / length
+    features = [[
+        sex,
+        length,
+        diameter,
+        height,
+        whole_weight,
+        shucked_weight,
+        viscera_weight,
+        shell_weight,
+        shell_volume,
+        meat_ratio,
+        bmi,
+        shell_surface_area,
+        volume_to_weight_ratio,
+        shell_weight_to_length_ratio,
+        meat_weight_to_lenth_ratio
+    ]]
+    columns = [
+        'Sex',
+        'Length',
+        'Diameter',
+        'Height',
+        'Whole_weight',
+        'Shucked_weight',
+        'Viscera_weight',
+        'Shell_weight',
+        'Shell_Volume',
+        'Meat_Ratio',
+        'BMI',
+        'Shell_Surface_Area',
+        'Volume_to Weigh_Ratio',
+        'Shell_Weight_to Length_Ratio',
+        'Mea_Weight_to_Length Ratio'
+    ]
+    features_df = pd.DataFrame(data=features, columns=columns)
+    prediction = model.predict(features_df)
+    age = prediction[0] + 1.5
+    return age
+
+# Streamlit app
+st.title('Abalone Age Predictor')
+
+st.write("Enter the physical measurements of the abalone to predict its age.")
+
+# Input fields
+sex = st.selectbox('Sex', ['M', 'F', 'I'])
+length = st.number_input('Length (mm)', min_value=0.0, value=0.0)
+diameter = st.number_input('Diameter (mm)', min_value=0.0, value=0.0)
+height = st.number_input('Height (mm)', min_value=0.0, value=0.0)
+whole_weight = st.number_input('Whole Weight (grams)', min_value=0.0, value=0.0)
+shucked_weight = st.number_input('Shucked Weight (grams)', min_value=0.0, value=0.0)
+viscera_weight = st.number_input('Viscera Weight (grams)', min_value=0.0, value=0.0)
+shell_weight = st.number_input('Shell Weight (grams)', min_value=0.0, value=0.0)
+
+# Predict button
+if st.button('Predict Age'):
+    try:
+        # Check for valid input values
+        if length <= 0 or diameter <= 0 or height <= 0 or whole_weight <= 0 or shucked_weight <= 0 or viscera_weight <= 0 or shell_weight <= 0:
+            st.error("All input values must be greater than zero.")
+        else:
+            age = predict_age(sex, length, diameter, height, whole_weight, shucked_weight, viscera_weight, shell_weight)
+            st.write(f'The predicted age of the abalone is: {age:.2f} years')
+    except Exception as e:
+        st.error(f"An error occurred: {e}")