Update app.py

app.py

@@ -1,7 +1,137 @@
+import random
 import gradio as gr
+from joblib import load
 
-def hello_world(name):
-    return f"Hello, {name}!"
+# Model URL for each
+rf_model_url = 'random_forest_model.joblib'
 
-iface = gr.Interface(fn=hello_world, inputs="text", outputs="text")
-iface.launch(share=True)
+
+# Load Model method
+def load_model(url):
+    return load(rf_model_url)
+
+
+def bool_value(val):
+    if not val:
+        return 0
+    else:
+        return 1
+
+
+def gender(val):
+    if val == 'Male':
+        return 1
+    elif val == 'Female':
+        return 0
+    else:
+        return 2
+
+
+def race(val):
+    if val == 'Hispanic':
+        return 2
+    elif val == 'Black':
+        return 1
+    elif val == 'White':
+        return 5
+    elif val == 'Other':
+        return 4
+    elif val == 'Asian':
+        return 0
+    else:
+        return 3
+
+
+def search_outcome(val, end_range):
+    if not val:
+        return 2  # Which mean no search was conducted
+    else:
+        return random.randrange(0, end_range)
+
+
+def search_reason(val, end_range):
+    if not val:
+        return 726  # Which mean no search was conducted
+    else:
+        return random.randrange(0, end_range)
+
+
+# Make Prediction Model but would also like to add Gender and Race
+def make_predication(year_stopped, offences, search, doc, car_year, alcohol, safety, genders, speeding,
+                     races, accident, actual_accident, damage, road_signs, injury, belt, disobedience, bad_driving,
+                     phone):
+    input_features = {
+        'encoded_Search Outcome': search_outcome(search, 6),
+        'encoded_Search Reason For Stop': search_reason(search, 727),
+        'Number Of Offences': offences,
+        'Search Conducted': bool_value(search),
+        'Year Stopped': year_stopped,
+        'encoded_SubAgency': random.randrange(0, 7),
+        'encoded_Arrest Type': random.randrange(0, 18),
+        'Invalid Documentation': bool_value(doc),
+        'Year': car_year,  # int
+        'encoded_Driver City': random.randrange(0, 8114),  # int
+        'encoded_Make': random.randrange(0, 57),  # int
+        'Alcohol': bool_value(alcohol),  # bool
+        'encoded_Color': random.randrange(0, 25),  # int
+        'Vehicle Safety And Standards': bool_value(safety),  # bool
+        'Speeding': bool_value(speeding),
+        'encoded_Race': race(races),
+        'Contributed To Accident': bool_value(accident),
+        'Accident': bool_value(actual_accident),
+        'encoded_VehicleType': random.randrange(0, 31),
+        'Property Damage': bool_value(damage),
+        'Road Signs And Markings': bool_value(road_signs),
+        'Personal Injury': bool_value(injury),
+        'encoded_Gender': gender(genders),
+        'Belts': bool_value(belt),
+        'Disobedience': bool_value(disobedience),
+        'encoded_DL State': random.randrange(0, 70),
+        'encoded_State': random.randrange(0, 68),
+        'Negligent Driving': bool_value(bad_driving),
+        'encoded_Driver State': random.randrange(0, 67),
+        'Mobile Phone': bool_value(phone)
+    }
+
+    x_input_feature = [[input_features[feature] for feature in sorted(input_features)]]
+    rfc_model = load_model(rf_model_url)
+    prd = rfc_model.predict(x_input_feature)
+
+    if prd == 0:
+        return 'Citation'
+    elif prd == 1:
+        return 'SERO'
+    else:
+        return 'Warning'
+
+
+iface = gr.Interface(fn=make_predication,
+                     inputs=[gr.components.Slider(minimum=2010, maximum=2023, step=1, label='Citation Year'),
+                             gr.components.Slider(minimum=1, step=1, label='Number of offences found for stop'),
+                             gr.components.Checkbox(label='Was a search conducted'),
+                             gr.components.Checkbox(label='Any invalid documents'),
+                             gr.components.Slider(minimum=1990, maximum=2023, step=1, label='Make Year'),
+                             gr.components.Checkbox(label='Was alcohol involved'),
+                             gr.components.Checkbox(label='Safety Standard issues in Vehicle'),
+                             gr.components.Dropdown(label='Gender', choices=['Female',
+                                                                             'Male',
+                                                                             'Unknown']),
+                             gr.components.Checkbox(label='Speeding'),
+                             gr.components.Dropdown(label='Race', choices=['Asian',
+                                                                           'Black',
+                                                                           'Hispanic',
+                                                                           'Native American',
+                                                                           'Other',
+                                                                           'White']),
+                             gr.components.Checkbox(label='Irregularities which could contribute to accident'),
+                             gr.components.Checkbox(label='Was the stop actual for an accident'),
+                             gr.components.Checkbox(label='Any Property damage'),
+                             gr.components.Checkbox(label='Road sign violation involvement'),
+                             gr.components.Checkbox(label='Any injuries'),
+                             gr.components.Checkbox(label='Seat Belts irregulation'),
+                             gr.components.Checkbox(label='Any disobedience'),
+                             gr.components.Checkbox(label='Bad driving'),
+                             gr.components.Checkbox(label='Mobile phone')],
+                     outputs=["text"])
+
+iface.launch(debug=True)