app.py

import gradio as gr
from fastai.vision.all import *


def get_headcount(filename):
    #print(filename)
    filename = str(filename)
    filename = filename.split("/")[-1]
    return df[df["Name"]==filename]["HeadCount"].values[0]



learn = load_learner("export_facecount.pkl")

# labels = learn.dls.vocab


def predict(img):
    img = PILImage.create(img)
    op = learn.predict(img)
    return int(op[0][0])


title = "Face count"
description = "A Car or Bike or not classifier trained with downloaded data from internet. Created as a demo for Gradio and HuggingFace Spaces."

examples = ["conf.jpeg"]
interpretation = "default"
enable_queue = True

gr.Interface(
    fn=predict,
    inputs=gr.inputs.Image(shape=(512, 512)),
    outputs=gr.outputs.Textbox(type="number", label="Number of faces"),
    title=title,
    description=description,
    examples=examples,
    interpretation=interpretation,
    enable_queue=enable_queue,
).launch(share=False)