app.py

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()