Spaces:
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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)
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