Upload 4 files

app.py

@@ -0,0 +1,81 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from sklearn.ensemble import RandomForestClassifier
+from prediction import get_prediction, ordinal_encoder
+
+model = joblib.load(r'Model/random_forest_final.joblib')
+
+st.set_page_config(page_title="Accident Severity Prediction App",
+                   page_icon="🚧", layout="wide")
+
+# creating option list for dropdown menu
+options_day = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"]
+options_age = ['18-30', '31-50', 'Over 51', 'Unknown', 'Under 18']
+
+options_acc_area = ['Other', 'Office areas', 'Residential areas', ' Church areas',
+       ' Industrial areas', 'School areas', '  Recreational areas',
+       ' Outside rural areas', ' Hospital areas', '  Market areas',
+       'Rural village areas', 'Unknown', 'Rural village areasOffice areas',
+       'Recreational areas']
+
+options_cause = ['No distancing', 'Changing lane to the right',
+       'Changing lane to the left', 'Driving carelessly',
+       'No priority to vehicle', 'Moving Backward',
+       'No priority to pedestrian', 'Other', 'Overtaking',
+       'Driving under the influence of drugs', 'Driving to the left',
+       'Getting off the vehicle improperly', 'Driving at high speed',
+       'Overturning', 'Turnover', 'Overspeed', 'Overloading', 'Drunk driving',
+       'Unknown', 'Improper parking']
+options_vehicle_type = ['Automobile', 'Lorry (41-100Q)', 'Other', 'Pick up upto 10Q',
+       'Public (12 seats)', 'Stationwagen', 'Lorry (11-40Q)',
+       'Public (13-45 seats)', 'Public (> 45 seats)', 'Long lorry', 'Taxi',
+       'Motorcycle', 'Special vehicle', 'Ridden horse', 'Turbo', 'Bajaj', 'Bicycle']
+options_driver_exp = ['5-10yr', '2-5yr', 'Above 10yr', '1-2yr', 'Below 1yr', 'No Licence', 'unknown']
+options_lanes = ['Two-way (divided with broken lines road marking)', 'Undivided Two way',
+       'other', 'Double carriageway (median)', 'One way',
+       'Two-way (divided with solid lines road marking)', 'Unknown']
+
+features = ['hour','day_of_week','casualties','accident_cause','vehicles_involved','vehicle_type','driver_age','accident_area','driving_experience','lanes']
+
+st.markdown("<h1 style='text-align: center;'>Accident Severity Prediction App 🚧</h1>", unsafe_allow_html=True)
+def main():
+    with st.form('prediction_form'):
+
+        st.subheader("Enter the input for following features:")
+        
+        hour = st.slider("Pickup Hour: ", 0, 23, value=0, format="%d")
+        day_of_week = st.selectbox("Select Day of the Week: ", options=options_day)
+        casualties = st.slider("Hour of Accident: ", 1, 8, value=0, format="%d")
+        accident_cause = st.selectbox("Select Accident Cause: ", options=options_cause)
+        vehicles_involved = st.slider("Pickup Hour: ", 1, 7, value=0, format="%d")
+        vehicle_type = st.selectbox("Select Vehicle Type: ", options=options_vehicle_type)
+        driver_age = st.selectbox("Select Driver Age: ", options=options_age)
+        accident_area = st.selectbox("Select Accident Area: ", options=options_acc_area)
+        driving_experience = st.selectbox("Select Driving Experience: ", options=options_driver_exp)
+        lanes = st.selectbox("Select Lanes: ", options=options_lanes)
+        
+        
+        submit = st.form_submit_button("Predict")
+
+
+    if submit:
+        day_of_week = ordinal_encoder(day_of_week, options_day)
+        accident_cause = ordinal_encoder(accident_cause, options_cause)
+        vehicle_type = ordinal_encoder(vehicle_type, options_vehicle_type)
+        driver_age =  ordinal_encoder(driver_age, options_age)
+        accident_area =  ordinal_encoder(accident_area, options_acc_area)
+        driving_experience = ordinal_encoder(driving_experience, options_driver_exp) 
+        lanes = ordinal_encoder(lanes, options_lanes)
+
+
+        data = np.array([hour,day_of_week,casualties,accident_cause,vehicles_involved, 
+                            vehicle_type,driver_age,accident_area,driving_experience,lanes]).reshape(1,-1)
+
+        pred = get_prediction(data=data, model=model)
+
+        st.write(f"The predicted severity is:  {pred[0]}")
+
+if __name__ == '__main__':
+    main()

prediction.py

@@ -0,0 +1,16 @@
+import joblib
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+
+def ordinal_encoder(input_val, feats):
+    feat_val = list(1+np.arange(len(feats)))
+    feat_key = feats
+    feat_dict = dict(zip(feat_key, feat_val))
+    value = feat_dict[input_val]
+    return value
+
+def get_prediction(data,model):
+    """
+    Predict the class of a given data point.
+    """
+    return model.predict(data)

requirements.txt

@@ -0,0 +1,7 @@
+joblib==1.0.1
+numpy==1.20.2
+pandas==1.2.4
+Pillow==8.2.0
+scikit-learn==0.24.2
+scipy
+streamlit