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import gradio as gr
import joblib
import pandas as pd
# Load the model
model = joblib.load('accident_prediction_model_Final.m5')
# Load the encoder
encoder = joblib.load('encoder.pkl')
# Define classes for accident prediction
classes = ["No", "Yes"]
# Create the inputs list with dropdown menus and sliders
inputs = [
gr.Dropdown(
choices=['Sunny/Clear', 'Rainy', 'Hail/Sleet', 'Foggy/Misty', 'Others'],
label="Weather Conditions"
),
gr.Dropdown(
choices=['Pedestrian', 'Bicycles', 'Two Wheelers', 'Auto Rickshaws', 'Cars, Taxis, Vans & LMV', 'Trucks, Lorries', 'Buses', 'Non-motorized Vehicles', 'Others'],
label="Impact Type"
),
gr.Dropdown(
choices=['Speeding', 'Jumping Red Light', 'Distracted Driving', 'Drunk Driving', 'Other'],
label="Traffic Violations"
),
gr.Dropdown(
choices=['Straight Road', 'Curved Road', 'Bridge', 'Culvert', 'Pot Holes', 'Steep Grade', 'Ongoing Road Works/Under Construction', 'Others'],
label="Road Features"
),
gr.Dropdown(
choices=['T-Junction', 'Y-Junction', 'Four arm Junction', 'Staggered Junction', 'Round about Junction', 'Others'],
label="Junction Types"
),
gr.Dropdown(
choices=['Traffic Light Signal', 'Police Controlled', 'Stop Sign', 'Flashing Signal/Blinker', 'Uncontrolled', 'Others'],
label="Traffic Controls"
),
gr.Dropdown(
choices=['morning', 'afternoon', 'evening', 'night'],
label="Time of Day"
),
gr.Dropdown(
choices=['13-17', '18-25', '26-40', '41-60', '60-80', '80 above'],
label="Age Group"
),
gr.Dropdown(
choices=['Killed', 'Grievously Injured', 'Minor Injury'],
label="Injury Type"
),
gr.Dropdown(
choices=['Yes', 'No'],
label="Safety Features"
),
gr.Slider(minimum=-90, maximum=90, label="Latitude"),
gr.Slider(minimum=-180, maximum=180, label="Longitude"),
gr.Slider(minimum=1, maximum=10, step= 1, label="Person Count"),
]
# Define output label
output_label = gr.Label(num_top_classes=4)
# Create a function to make predictions
def predict_accident(
weather_conditions,
impact_type,
traffic_violations,
road_features,
junction_types,
traffic_controls,
time_day,
age_group,
injury,
safety_features,
Latitude,
Longitude,
person_count
):
data = {
'selectedWeatherCondition': weather_conditions,
'selectedImpactType': impact_type,
'selectedTrafficViolationType': traffic_violations,
'selectedRoadFeaturesType': road_features,
'selectedRoadJunctionType': junction_types,
'selectedTrafficControl': traffic_controls,
'selectedTimeOfDay': time_day,
'selectedAge': age_group,
'selectedInjuryType': injury,
'selectedSafetyFeature': safety_features,
'Latitude': Latitude,
'Longitude': Longitude,
'personCount': person_count
}
num_input = {'Latitude': data['Latitude'], 'Longitude': data['Longitude'], 'person_count': data['personCount']}
cat_input = {'weather_conditions': data['selectedWeatherCondition'], 'impact_type': data['selectedImpactType'],
'traffic_voilations': data['selectedTrafficViolationType'],
'road_features': data['selectedRoadFeaturesType'],
'junction_types': data['selectedRoadJunctionType'],
'traffic_controls': data['selectedTrafficControl'], 'time_day': data['selectedTimeOfDay'],
'age_group': data['selectedAge'], 'safety_features': data['selectedSafetyFeature'],
'injury': data['selectedInjuryType']}
input_df = pd.DataFrame([cat_input])
encoded_input = encoder['encoder'].transform(input_df)
encoded_input_df = pd.DataFrame(encoded_input, columns=encoder['encoded_columns'])
num_df = pd.DataFrame([num_input])
input_with_coords = pd.concat([num_df, encoded_input_df], axis=1)
# Make a prediction using the trained model
prediction = model.predict(input_with_coords)
label = f"Accident Prediction: {classes[int(prediction[0])]}"
return label
# Create the Gradio interface
title = "Accident Prediction"
description = "Predict the severity of an accident based on input features."
output_label = [gr.Label(num_top_classes=4)]
gr.Interface(
fn=predict_accident,
inputs=inputs,
outputs=output_label,
title=title,
description=description,
).launch() |