Spaces:
Sleeping
Sleeping
File size: 2,847 Bytes
6dd5319 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
import pickle
import random
import pandas as pd
import gradio as gr
from fastai.vision.all import *
zone_lookup = pd.read_csv('./data/zone_lookup.csv')
with open('./models/lin_reg.bin', 'rb') as handle:
dv, model = pickle.load(handle)
def prepare_features(pickup, dropoff, trip_distance):
pickupId = zone_lookup[zone_lookup["borough_zone"] == pickup].LocationID
dropoffId = zone_lookup[zone_lookup["borough_zone"] == dropoff].LocationID
trip_distance = round(trip_distance, 4)
features = {}
features['PU_DO'] = '%s_%s' % (pickupId, dropoffId)
features['trip_distance'] = trip_distance
return features
def predict(pickup, dropoff, trip_distance):
features = prepare_features(pickup, dropoff, trip_distance)
X = dv.transform(features)
preds = model.predict(X)
duration = float(preds[0])
return "The predicted duration is %.4f minutes." % duration
with gr.Blocks() as demo:
gr.Markdown("Predict Taxi Duration or Classify dog breeds using this demo")
with gr.Tab("Predict Taxi Duration"):
with gr.Row():
pickup = gr.Dropdown(
choices=list(zone_lookup["borough_zone"]),
label="Pickup Location",
info="The location where the passenger(s) were picked up",
value=lambda: random.choice(zone_lookup["borough_zone"])
)
dropoff = gr.Dropdown(
choices=list(zone_lookup["borough_zone"]),
label="Dropoff Location",
info="The location where the passenger(s) were dropped off",
value=lambda: random.choice(zone_lookup["borough_zone"])
)
trip_distance = gr.Slider(
minimum=0.0,
maximum=100.0,
step=0.1,
label="Trip Distance",
info="The trip distance in miles calculated by the taximeter",
value=lambda: random.uniform(0.0, 100.0)
)
with gr.Column():
output = gr.Textbox(label="Output Box")
predict_btn = gr.Button("Predict")
with gr.Tab("Classify Dog Breed"):
def is_cat(x): return x[0].isupper()
learn = load_learner('./models/model.pkl')
categories = ('Dog', 'Cat')
def classify_image(img):
pred, idx, probs = learn.predict(img)
return dict(zip(categories, map(float,probs)))
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
classify_btn = gr.Button("Predict")
predict_btn.click(fn=predict, inputs=[pickup, dropoff, trip_distance], outputs=output, api_name="predict-duration")
classify_btn.click(fn=classify_image, inputs=image, outputs=label, api_name="classify-dog-breed")
demo.launch() |