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e1c192a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | import os
import joblib
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from sklearn.metrics import roc_curve, roc_auc_score, confusion_matrix
import dash
from dash import html, dcc, Input, Output, State
import dash_bootstrap_components as dbc
dash.register_page(__name__, path="/prediction", name="Prediction")
# ----------------------------
# Paths
# ----------------------------
BASE_DIR = os.path.dirname(__file__)
MODEL_PATH = os.path.join(BASE_DIR, "saved_models", "logreg_breastcancer_reduced.pkl")
TRAIN_PATH = os.path.join(BASE_DIR, "saved_models", "X_train_y_train.csv")
# ----------------------------
# Load model and training data
# ----------------------------
if os.path.exists(MODEL_PATH):
model = joblib.load(MODEL_PATH)
else:
raise FileNotFoundError(f"Model file not found at {MODEL_PATH}")
if os.path.exists(TRAIN_PATH):
train_data = pd.read_csv(TRAIN_PATH)
else:
raise FileNotFoundError(f"Training data CSV not found at {TRAIN_PATH}")
# ----------------------------
# Features used in model
# ----------------------------
features = [
'texture error', 'area error', 'smoothness error', 'concavity error',
'symmetry error', 'fractal dimension error', 'worst concavity'
]
feature_labels = [
"Texture Error", "Area Error", "Smoothness Error", "Concavity Error",
"Symmetry Error", "Fractal Dimension Error", "Worst Concavity"
]
X_train = train_data[features]
y_train = train_data['target']
# ----------------------------
# Precompute performance plots
# ----------------------------
y_proba_train = model.predict_proba(X_train)[:, 1]
y_pred_train = model.predict(X_train)
# ROC Curve
fpr, tpr, _ = roc_curve(y_train, y_proba_train)
auc_score = roc_auc_score(y_train, y_proba_train)
fig_roc = go.Figure()
fig_roc.add_trace(go.Scatter(x=fpr, y=tpr, mode='lines', line=dict(color="#e61227", width=3), name=f"AUC={auc_score:.3f}"))
fig_roc.add_trace(go.Scatter(x=[0, 1], y=[0, 1], mode='lines', line=dict(color="gray", dash="dash")))
fig_roc.update_layout(title="ROC Curve", margin=dict(l=20, r=20, t=40, b=20))
# Feature importance
clf = model.named_steps[list(model.named_steps.keys())[-1]]
if hasattr(clf, "coef_"):
coefs = clf.coef_.ravel()
fig_feat = px.bar(x=features, y=coefs, color=coefs, color_continuous_scale="RdPu", title="Feature Importance")
fig_feat.update_layout(margin=dict(l=20, r=20, t=40, b=20))
else:
fig_feat = go.Figure()
# Confusion Matrix
cm = confusion_matrix(y_train, y_pred_train)
fig_cm = go.Figure(data=go.Heatmap(z=cm, x=["Pred Malignant (0)", "Pred Benign (1)"], y=["Actual Malignant (0)", "Actual Benign (1)"], colorscale="RdPu", showscale=False))
fig_cm.update_layout(title="Confusion Matrix", margin=dict(l=20, r=20, t=40, b=20))
# ----------------------------
# Layout
# ----------------------------
layout = html.Div([
html.H2("Breast Cancer Prediction", style={"margin-bottom": "30px"}),
dbc.Card(
dbc.CardBody([
dbc.Row([
dbc.Col([
html.Label(label),
dbc.Input(id=f"input-{feat}", type="number", step=0.0001, value=0)
], width=3, className="mb-2")
for feat, label in zip(features, feature_labels)
], className="mb-3"),
dbc.Button("Run Diagnostic Prediction", id="predict-btn", color="light", className="mb-3"),
html.Div(id="prediction-output")
]),
style={
"background": "linear-gradient(135deg, #ff77b4, #e61227)",
"color": "#fff",
"box-shadow": "0 4px 15px rgba(0,0,0,0.2)",
"padding": "20px",
"border-radius": "10px",
"margin-bottom": "40px"
}
),
dbc.Card(
dbc.CardBody([
dbc.Row([
dbc.Col(dcc.Graph(figure=fig_roc), md=4),
dbc.Col(dcc.Graph(figure=fig_feat), md=4),
dbc.Col(dcc.Graph(figure=fig_cm), md=4),
])
])
)
], style={"margin": "20px 3%"})
# ----------------------------
# Callback for user prediction
# ----------------------------
@dash.callback(
Output("prediction-output", "children"),
Input("predict-btn", "n_clicks"),
[State(f"input-{feat}", "value") for feat in features]
)
def predict_user(n_clicks, *vals):
if n_clicks is None:
return ""
# Sanitize inputs (The Bug Fix)
cleaned_vals = [float(v) if v is not None else 0.0 for v in vals]
try:
x_input = pd.DataFrame([cleaned_vals], columns=features)
# Get binary prediction (0 or 1)
y_pred = model.predict(x_input)[0]
# Get probabilities for both classes
# probas[0] is for class 0 (Malignant), probas[1] is for class 1 (Benign)
probas = model.predict_proba(x_input)[0]
prob_malignant = probas[0]
prob_benign = probas[1]
# Determine label based on Jupyter logic (0=Malignant, 1=Benign)
if y_pred == 0:
result_text = "MALIGNANT"
result_color = "#FFD700" # Warning Gold
badge_color = "danger"
else:
result_text = "BENIGN"
result_color = "#00FF7F" # Spring Green
badge_color = "success"
return html.Div([
html.Hr(style={"borderTop": "1px solid white"}),
html.H3([
f"Model Classification: ",
dbc.Badge(result_text, color=badge_color, className="ms-2")
], style={"fontWeight": "bold"}),
dbc.Row([
dbc.Col([
html.P(f"Probability of Malignant (Class 0): {prob_malignant:.2%}"),
dbc.Progress(value=prob_malignant*100, color="dark", style={"height": "10px"})
], md=6),
dbc.Col([
html.P(f"Probability of Benign (Class 1): {prob_benign:.2%}"),
dbc.Progress(value=prob_benign*100, color="info", style={"height": "10px"})
], md=6),
], className="mt-3")
], style={"color": "white"})
except Exception as e:
return html.Div(f"Prediction Error: {e}", style={"color": "white", "background": "red", "padding": "10px"}) |