add files

app.py

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+import gradio as gr
+from PIL import Image
+import requests
+import hopsworks
+import joblib
+import pandas as pd
+
+api = 'UtYWT9JBE4jbsOVW.dzfTExU7QMCzzR51EADTOZCXBzl0VmgB2y012yd8nFTG6v1VHgWazdx2a2SuJAY1'
+project = hopsworks.login(api_key_value = api)
+
+
+mr = project.get_model_registry()
+model = mr.get_model("sf_traffic_model_1", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/sf_traffic_model_1.pkl")
+print("Model downloaded")
+    
+# Mapping functions for each categorical feature
+def map_time_cat(time_cat):
+    time_cat_mapping = {
+        '2:01 pm to 6:00 pm': 0, '10:01 am to 2:00 pm': 1, '6:01 pm to 10:00 pm': 2, 
+        '6:01 am to 10:00 am': 3, '10:01 pm to 2:00 am': 4, '2:01 am to 6:00 am': 5
+    }
+    return time_cat_mapping.get(time_cat, -1)  # Default to -1 for unknown categories
+
+def map_party2_move_pre_acc(move):
+    party2_move_mapping = {
+        'Proceeding Straight': 0, 'Stopped In Road': 1, 'Not Stated': 2, 'Making Left Turn': 3,
+        'Parked': 4, 'Other': 5, 'Stopped': 6, 'Making Right Turn': 7, 'Slowing/Stopping': 8,
+        'Entering Traffic': 9, 'Backing': 10, 'Changing Lanes': 11, 'Parking Maneuver': 12,
+        'Passing Other Vehicle': 13, 'Making U Turn': 14, 'Merging': 15, 'Ran Off Road': 16,
+        'Traveling Wrong Way': 17, 'Crossed Into Opposing Lane': 18,
+        'Other Unsafe Turning': 19, 'Crossed Into Opposing Lane - Unplanned': 20
+    }
+    return party2_move_mapping.get(move, -1)
+
+def map_party1_move_pre_acc(move):
+    party1_move_mapping = {
+        'Proceeding Straight': 0, 'Making Left Turn': 1, 'Making Right Turn': 2, 
+        'Changing Lanes': 3, 'Other': 4, 'Entering Traffic': 5, 'Not Stated': 6, 'Backing': 7, 
+        'Stopped In Road': 8, 'Making U Turn': 9, 'Parked': 10, 'Slowing/Stopping': 11, 
+        'Passing Other Vehicle': 12, 'Traveling Wrong Way': 13, 'Stopped': 14, 'Ran Off Road': 15, 
+        'Other Unsafe Turning': 16, 'Parking Maneuver': 17, 'Crossed Into Opposing Lane': 18, 
+        'Merging': 19, 'Crossed Into Opposing Lane - Unplanned': 20
+    }
+    return party1_move_mapping.get(move, -1)
+
+def map_ped_action(action):
+    ped_action_mapping = {
+        'No Pedestrian Involved': 0, 'Crossing in Crosswalk at Intersection': 1,
+        'Crossing Not in Crosswalk': 2, 'In Road, Including Shoulder': 3, 'Not in Road': 4,
+        'Not Stated': 5, 'Crossing in Crosswalk Not at Intersection': 6,
+        'Approaching/Leaving School Bus': 7
+    }
+    return ped_action_mapping.get(action, -1)
+
+def map_party1_type(party_type):
+    party1_type_mapping = {
+        'Driver': 0, 'Bicyclist': 1, 'Pedestrian': 2, 'Other': 3, 'Parked Vehicle': 4, 
+        'Not Stated': 5
+    }
+    return party1_type_mapping.get(party_type, -1)
+
+def map_party2_type(party_type):
+    party2_type_mapping = {
+        'Driver': 0, 'Pedestrian': 1, 'Bicyclist': 2, 'Parked Vehicle': 3, 'Other': 4, 
+        'Not Stated': 5
+    }
+    return party2_type_mapping.get(party_type, -1)
+
+def map_mviw(mviw):
+    mviw_mapping = {
+        'Other Motor Vehicle': 0, 'Pedestrian': 1, 'Bicycle': 2, 'Fixed Object': 3, 
+        'Parked Motor Vehicle': 4, 'Non-Collision': 5, 'Not Stated': 6, 'Other Object': 7, 
+        'Motor Vehicle on Other Roadway': 8, 'Train': 9, 'Animal': 10
+    }
+    return mviw_mapping.get(mviw, -1)
+
+
+def map_intersection(intersection):
+    mapping = {
+        'Intersection <= 20ft': 0,
+        'Midblock > 20ft': 1,
+        'Intersection Rear End <= 150ft': 2
+    }
+    return mapping.get(intersection, -1)
+
+def map_road_cond_1(road_cond):
+    mapping = {
+        'No Unusual Condition': 0,
+        'Not Stated': 1,
+        'Other': 2,
+        'Construction or Repair Zone': 3,
+        'Holes, Deep Ruts': 4,
+        'Obstruction on Roadway': 5,
+        'Loose Material on Roadway': 6,
+        'Holes, Deep Rut': 7,
+        'Reduced Roadway Width': 8,
+        'Flooded': 9
+    }
+    return mapping.get(road_cond, -1)
+
+def map_control_device(device):
+    mapping = {
+        'Functioning': 0,
+        'None': 1,
+        'Not Stated': 2,
+        'Not Functioning': 3,
+        'Obscured': 4
+    }
+    return mapping.get(device, -1)
+
+def map_lighting(lighting):
+    mapping = {
+        'Daylight': 0,
+        'Dark - Street Lights': 1,
+        'Dusk - Dawn': 2,
+        'Not Stated': 3,
+        'Dark - No Street Lights': 4,
+        'Dark - Street Lights Not Functioning': 5
+    }
+    return mapping.get(lighting, -1)
+
+def map_road_surface(surface):
+    mapping = {
+        'Dry': 0,
+        'Wet': 1,
+        'Not Stated': 2,
+        'Slippery': 3,
+        'Snowy or Icy': 4
+    }
+    return mapping.get(surface, -1)
+
+def map_type_of_collision(collision):
+    mapping = {
+        'Broadside': 0,
+        'Vehicle/Pedestrian': 1,
+        'Rear End': 2,
+        'Sideswipe': 3,
+        'Head-On': 4,
+        'Other': 5,
+        'Hit Object': 6,
+        'Not Stated': 7,
+        'Overturned': 8
+    }
+    return mapping.get(collision, -1)
+
+def map_weather_condition(conditions):
+    # If 'conditions' is a list with one element, take the first element
+    if isinstance(conditions, list) and len(conditions) == 1:
+        condition = conditions[0]
+    else:
+        condition = conditions  # Otherwise, assume it's a single value
+    
+    weather_mapping = {
+        'Clear': 0, 
+        'Cloudy': 1, 
+        'Raining': 2, 
+        'Fog': 3, 
+        'Other': 4, 
+        'Not Stated': 5, 
+        'Unknown': 6
+    }
+    return weather_mapping.get(condition, -1)  # Default to -1 for unknown categories
+
+
+
+# Add other mapping functions as necessary
+
+def traffic_predict(weather, intersection, road_cond, control_device, 
+                    lighting, road_surface, collision_type, time_cat, 
+                    party2_move, party1_move, ped_action, party1_type, 
+                    party2_type, mviw):
+    # Apply mappings to each input
+    weather_mapped = map_weather_condition(weather)
+    intersection_mapped = map_intersection(intersection)
+    road_cond_mapped = map_road_cond_1(road_cond)
+    control_device_mapped = map_control_device(control_device)
+    lighting_mapped = map_lighting(lighting)
+    road_surface_mapped = map_road_surface(road_surface)
+    collision_type_mapped = map_type_of_collision(collision_type)
+    time_cat_mapped = map_time_cat(time_cat)
+    party2_move_mapped = map_party2_move_pre_acc(party2_move)
+    party1_move_mapped = map_party1_move_pre_acc(party1_move)
+    ped_action_mapped = map_ped_action(ped_action)
+    party1_type_mapped = map_party1_type(party1_type)
+    party2_type_mapped = map_party2_type(party2_type)
+    mviw_mapped = map_mviw(mviw)
+
+    # Prepare DataFrame for prediction
+    df = pd.DataFrame([[weather_mapped, intersection_mapped, road_cond_mapped, control_device_mapped, 
+                        lighting_mapped, road_surface_mapped, collision_type_mapped, time_cat_mapped, 
+                        party2_move_mapped, party1_move_mapped, ped_action_mapped, party1_type_mapped, 
+                        party2_type_mapped, mviw_mapped]],
+                      columns=['weather_1_mapped', 'intersection_mapped', 'road_cond_1_mapped', 
+                               'control_device_mapped', 'lighting_mapped', 'road_surface_mapped', 
+                               'type_of_collision_mapped', 'time_cat_mapped', 'party2_move_pre_acc_mapped', 
+                               'party1_move_pre_acc_mapped', 'ped_action_mapped', 'party1_type_mapped', 
+                               'party2_type_mapped', 'mviw_mapped'])
+
+    # Predict with model
+    prediction = model.predict(df)
+    return prediction[0]
+
+# Now, 'mapped_incident_df' contains only the mapped versions of your original categorical variables
+
+demo = gr.Interface(
+    fn=traffic_predict,
+    inputs=[
+        gr.inputs.Dropdown(['Clear', 'Cloudy', 'Raining', 'Fog', 'Other', 'Not Stated', 'Unknown'], label="Weather Condition"),
+        gr.inputs.Dropdown(['Intersection <= 20ft', 'Midblock > 20ft', 'Intersection Rear End <= 150ft'], label="Intersection"),
+        gr.inputs.Dropdown(['No Unusual Condition', 'Not Stated', 'Other', 'Construction or Repair Zone', 'Holes, Deep Ruts', 'Obstruction on Roadway', 'Loose Material on Roadway', 'Holes, Deep Rut', 'Reduced Roadway Width', 'Flooded'], label="Road Condition 1"),
+        gr.inputs.Dropdown(['Functioning', 'None', 'Not Stated', 'Not Functioning', 'Obscured'], label="Control Device"),
+        gr.inputs.Dropdown(['Daylight', 'Dark - Street Lights', 'Dusk - Dawn', 'Not Stated', 'Dark - No Street Lights', 'Dark - Street Lights Not Functioning'], label="Lighting"),
+        gr.inputs.Dropdown(['Dry', 'Wet', 'Not Stated', 'Slippery', 'Snowy or Icy'], label="Road Surface"),
+        gr.inputs.Dropdown(['Broadside', 'Vehicle/Pedestrian', 'Rear End', 'Sideswipe', 'Head-On', 'Other', 'Hit Object', 'Not Stated', 'Overturned'], label="Type of Collision"),
+        gr.inputs.Dropdown(['2:01 pm to 6:00 pm', '10:01 am to 2:00 pm', '6:01 pm to 10:00 pm', '6:01 am to 10:00 am', '10:01 pm to 2:00 am', '2:01 am to 6:00 am'], label="Time Category"),
+        gr.inputs.Dropdown(['Proceeding Straight', 'Stopped In Road', 'Not Stated', 'Making Left Turn', 'Parked', 'Other', 'Stopped', 'Making Right Turn', 'Slowing/Stopping', 'Entering Traffic', 'Backing', 'Changing Lanes', 'Parking Maneuver', 'Passing Other Vehicle', 'Making U Turn', 'Merging', 'Ran Off Road', 'Traveling Wrong Way', 'Crossed Into Opposing Lane', 'Other Unsafe Turning', 'Crossed Into Opposing Lane - Unplanned'], label="Party 2 Movement Pre-Accident"),
+        gr.inputs.Dropdown(['Proceeding Straight', 'Making Left Turn', 'Making Right Turn', 'Changing Lanes', 'Other', 'Entering Traffic', 'Not Stated', 'Backing', 'Stopped In Road', 'Making U Turn', 'Parked', 'Slowing/Stopping', 'Passing Other Vehicle', 'Traveling Wrong Way', 'Stopped', 'Ran Off Road', 'Other Unsafe Turning', 'Parking Maneuver', 'Crossed Into Opposing Lane', 'Merging', 'Crossed Into Opposing Lane - Unplanned'], label="Party 1 Movement Pre-Accident"),
+        gr.inputs.Dropdown(['No Pedestrian Involved', 'Crossing in Crosswalk at Intersection', 'Crossing Not in Crosswalk', 'In Road, Including Shoulder', 'Not in Road', 'Not Stated', 'Crossing in Crosswalk Not at Intersection', 'Approaching/Leaving School Bus'], label="Pedestrian Action"),
+        gr.inputs.Dropdown(['Driver', 'Bicyclist', 'Pedestrian', 'Other', 'Parked Vehicle', 'Not Stated'], label="Party 1 Type"),
+        gr.inputs.Dropdown(['Driver', 'Pedestrian', 'Bicyclist', 'Parked Vehicle', 'Other', 'Not Stated'], label="Party 2 Type"),
+        gr.inputs.Dropdown(['Other Motor Vehicle', 'Pedestrian', 'Bicycle', 'Fixed Object', 'Parked Motor Vehicle', 'Non-Collision', 'Not Stated', 'Other Object', 'Motor Vehicle on Other Roadway', 'Train', 'Animal'], label="MVIW")
+    ],
+    outputs=gr.outputs.Textbox(label="Predicted Severity")
+)
+
+demo.launch()
+
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn==1.1.1
+transformers
+gradio