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app.txt

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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()

requirements.txt

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+gradio
+pandas
+joblib
+scikit-learn