Upload appgradio.py

appgradio.py

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+from pathlib import Path
+from time import sleep
+from time import time
+
+from fastai.vision.all import *
+from fastai.vision.widgets import *
+from fastbook import *
+from fastcore.parallel import *
+from fastdownload import download_url
+from fastai.learner import load_learner
+
+import gradio as gr
+from huggingface_hub import from_pretrained_fastai, notebook_login, push_to_hub_fastai
+import timm
+from torchvision.models import resnet18
+import os
+
+learn = load_learner('model.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))}
+
+demo=gr.Interface(fn=predict, inputs=gr.Image(), outputs=gr.Label(num_top_classes=2)).launch(share=True)
+
+
+if __name__ == "__main__":
+    demo.launch()