File size: 2,717 Bytes
b85e25b | 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 | import sys
# SHIM FOR PYTHON 3.13: fake audioop module before any imports
try:
import audioop
except ImportError:
import types
sys.modules["audioop"] = types.ModuleType("audioop")
import gradio as gr
import xgboost as xgb
import joblib
import json
import numpy as np
# --- Load Assets ---
MODEL_PATH = "severity_model.json"
SCALER_PATH = "feature_scaler.pkl"
FEATURES_PATH = "feature_list.json"
def load_resources():
model = xgb.XGBRegressor()
model.load_model(MODEL_PATH)
scaler = joblib.load(SCALER_PATH)
with open(FEATURES_PATH) as f:
features = json.load(f)
return model, scaler, features
model, scaler, feature_names = load_resources()
def get_label(score):
if score < 0.33: return "Low 🟢"
if score < 0.66: return "Medium 🟡"
return "High 🔴"
def predict(*args):
input_dict = dict(zip(feature_names, args))
row = np.array([[input_dict[f] for f in feature_names]], dtype=np.float32)
scaled_row = scaler.transform(row)
prediction = float(model.predict(scaled_row)[0])
score = max(0, min(1, prediction))
return round(score, 4), get_label(score)
# --- UI ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🕳️ Pothole Severity Predictor (Civic AI)")
gr.Markdown("Adjust the sliders below to simulate pothole features and predict repair priority.")
with gr.Row():
with gr.Column():
a = gr.Slider(0, 1, value=0.1, label="Area Ratio (A)", info="Size of pothole")
d = gr.Slider(0, 1, value=0.1, label="Density (D)", info="Fragmentation")
c = gr.Slider(0, 1, value=0.5, label="Centrality (C)", info="0=Edge, 1=Center")
q = gr.Slider(0, 1, value=0.9, label="Confidence (Q)", info="CV Model Certainty")
m = gr.Slider(0, 1, value=0.1, label="Confirmations (M)", info="User reports")
with gr.Column():
t = gr.Slider(0, 1, value=0.1, label="Persistence (T)", info="Wait time")
r = gr.Slider(0, 1, value=0.4, label="Road Type (R)", info="0.4:Local, 1.0:Highway")
p = gr.Slider(0, 1, value=0.1, label="Critical Infra (P)", info="Proximity to hospitals/schools")
f = gr.Slider(0, 1, value=0.1, label="Recurrence (F)", info="Historical failure")
x = gr.Slider(0, 1, value=0.0, label="Reopen Count (X)", info="Failed repairs")
btn = gr.Button("Calculate Severity Score", variant="primary")
with gr.Row():
out_score = gr.Number(label="Severity Score (0-1)")
out_label = gr.Textbox(label="Priority Level")
btn.click(predict, inputs=[a, d, c, q, m, t, r, p, f, x], outputs=[out_score, out_label])
if __name__ == "__main__":
demo.launch()
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