Added Application file, requirements.txt and model

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+import pathlib
+
+plt = platform.system()
+if plt == 'Linux' : pathlib.WindowsPath = pathlib.PosixPath
+
+learn = load_learner('xres18.pkl')
+
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i] : float(probs[i]) for i in range(len(labels))}
+
+title = 'Cell Identifier'
+description = 'A cell identifier detector trained on custom dataset with fastai. Created using Gradio and HuggingFace Spaces.'
+examples = []
+enable_queue = True
+
+gr.Interface(fn=predict, input=gr.inputs.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3), title=title, description=description, examples=examples, enable_queue=enable_queue).launch()

requirements.txt

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+fastai
+scikit-image

xres18.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ec3060d2e3a37f7f1ca417e7103c5d395da2578b46c5c2fa5c7f8f3575491bb
+size 47134138