Upload 2 files

app (1).py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
+
+# %% auto 0
+__all__ = ['plt', 'learn', 'interface_options', 'demo', 'label_func', 'predict']
+
+# %% ../app.ipynb 2
+from fastai.vision.all import *
+import gradio 
+#import pathlib
+#temp = pathlib.PosixPath
+#pathlib.PosixPath = pathlib.WindowsPath
+import pathlib
+plt = platform.system()
+if plt == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
+
+def label_func(filepath):
+    return filepath.parent.name
+
+# %% ../app.ipynb 3
+learn = load_learner('model.pkl')
+
+# %% ../app.ipynb 5
+def predict(image):
+    img = PILImage.create(img)
+    _pred, _pred_w_idx, probs = learn.predict(img)
+    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
+    return labels_probs
+
+# %% ../app.ipynb 6
+interface_options = {
+    "title": "KhetAi",
+    "description": "An web app that predicts the disease based on Image",
+    "interpretation": "default",
+    "layout": "horizontal",
+    "allow_flagging": "never",
+    "enable_queue": True 
+}
+
+"""demo = gradio.Interface(fn=predict,
+                      inputs=gradio.inputs.Image(shape=(512, 512)),
+                      outputs=gradio.outputs.Label(num_top_classes=5),
+                      **interface_options)"""
+
+demo = gradio.Interface(fn=predict,
+                      inputs="image",
+                      outputs="label")
+
+# demo_options = {
+#     "inline": True,
+#     "inbrowser": True,
+#     "share": True,
+#     "show_error": True,
+#     "server_name": "0.0.0.0",
+#     "server_port": 5000,
+#     "enable_queue": True,
+# }
+
+#demo.launch(**demo_options)
+demo.launch(share=True)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8e95cce3cc8edac095f1f7cf15650ac7d00b1408f9c542be6d1cde3cdcb31e9e
+size 103048469