add

app.py

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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)

export_facecount.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5efe5147266f9987fb611afa32cf842ce7c989cb6be08ea17ac66c1d4b7855bf
+size 47044205

requirements.txt

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+fastai
+