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119bb4f 52ed03c 119bb4f 52ed03c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | import streamlit as st
import pandas as pd
import numpy as np
import joblib
# --- PAGE CONFIGURATION ---
st.set_page_config(
page_title="Steel Plate Defect Prediction",
page_icon="🏗️",
layout="wide")
@st.cache_resource
def load_artifacts():
artifacts = joblib.load('src/steel_defect_model.pkl')
return artifacts
try:
artifacts = load_artifacts()
models = artifacts['models']
scaler = artifacts['scaler']
feature_names = artifacts['features']
targets = artifacts['targets']
except FileNotFoundError:
st.error("Model file 'steel_defect_model.pkl' not found.")
st.stop()
st.title("🏗️ Steel Plate Defect Prediction AI")
st.markdown("""
This AI model predicts the probability of **7 different types of defects** in steel plates based on their geometric and radiometric properties.
""")
st.sidebar.header("Input Parameters")
st.sidebar.info("Adjust the sliders to simulate different steel plate properties.")
def user_input_features():
data = {}
st.sidebar.subheader("1. Geometry")
data['X_Minimum'] = st.sidebar.number_input('X Minimum', 0, 1700, 0)
data['X_Maximum'] = st.sidebar.number_input('X Maximum', 0, 1700, 50)
data['Y_Minimum'] = st.sidebar.number_input('Y Minimum', 0, 13000000, 600000)
data['Y_Maximum'] = st.sidebar.number_input('Y Maximum', 0, 13000000, 600050)
data['Pixels_Areas'] = st.sidebar.number_input('Pixels Areas', 0, 20000, 200)
data['Steel_Plate_Thickness'] = st.sidebar.slider('Steel Plate Thickness', 40, 300, 80)
st.sidebar.subheader("2. Luminosity")
data['Sum_of_Luminosity'] = st.sidebar.number_input('Sum of Luminosity', 0, 12000000, 20000)
data['Minimum_of_Luminosity'] = st.sidebar.slider('Minimum Luminosity', 0, 200, 80)
data['Maximum_of_Luminosity'] = st.sidebar.slider('Maximum Luminosity', 0, 260, 130)
data['Length_of_Conveyer'] = 1459
data['TypeOfSteel_A300'] = st.sidebar.selectbox('Type of Steel A300', [0, 1], index=0)
data['Edges_Index'] = 0.35
data['Empty_Index'] = 0.4
data['Square_Index'] = 0.57
data['Outside_X_Index'] = 0.03
data['Edges_X_Index'] = 0.61
data['Edges_Y_Index'] = 0.83
data['Outside_Global_Index'] = 0.5
data['LogOfAreas'] = np.log(data['Pixels_Areas']) if data['Pixels_Areas'] > 0 else 0
data['Log_X_Index'] = 0
data['Log_Y_Index'] = 0
data['Orientation_Index'] = 0.1
data['Luminosity_Index'] = -0.13
data['SigmoidOfAreas'] = 0.5
df = pd.DataFrame(data, index=[0])
return df
input_df = user_input_features()
def preprocess_input(df):
df = df.copy()
df['X_Range'] = df['X_Maximum'] - df['X_Minimum']
df['Y_Range'] = df['Y_Maximum'] - df['Y_Minimum']
x_range = df['X_Range'].replace(0, 1)
y_range = df['Y_Range'].replace(0, 1)
df['Density'] = df['Pixels_Areas'] / (x_range * y_range)
df['Aspect_Ratio'] = df['X_Range'] / y_range
df['Luminosity_Range'] = df['Maximum_of_Luminosity'] - df['Minimum_of_Luminosity']
skewed_features = ['Pixels_Areas', 'Sum_of_Luminosity',
'X_Range', 'Y_Range', 'Aspect_Ratio']
for col in feature_names:
if col not in df.columns:
df[col] = 0
for feature in skewed_features:
if feature in df.columns:
df[feature] = np.log1p(df[feature].abs())
df = df[feature_names]
return df
if st.button('🔎 Analyze Steel Plate'):
processed_df = preprocess_input(input_df)
scaled_array = scaler.transform(processed_df)
scaled_df = pd.DataFrame(scaled_array, columns=feature_names)
results = {}
for target in targets:
model = models[target]
prob = model.predict_proba(scaled_df)[0][1]
results[target] = prob
st.subheader("Analysis Results")
results_df = pd.DataFrame(list(results.items()), columns=['Defect Type', 'Probability'])
results_df = results_df.sort_values(by='Probability', ascending=False)
top_defect = results_df.iloc[0]
if top_defect['Probability'] > 0.5:
st.error(f"⚠️ High Risk Detected: **{top_defect['Defect Type']}** ({top_defect['Probability']:.1%})")
else:
st.success("✅ No severe defects detected (All probabilities < 50%)")
st.bar_chart(results_df.set_index('Defect Type'))
with st.expander("See Detailed Probabilities"):
st.dataframe(results_df.style.format({'Probability': '{:.2%}'})) |