First model version

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+#%reload_ext autoreload
+#%autoreload 2
+#%matplotlib inline
+#!pip install fastai --upgrade 
+#!pip install voila
+#!jupyter serverextension enable --sys-prefix voila
+#!pip install gradio
+
+from fastai import *
+from fastai.vision import *
+from fastai.vision.all import *
+from fastai.metrics import error_rate, accuracy
+from fastai.imports import *
+import gdown
+import gradio as gr
+from gradio.themes.base import Base
+
+## Export the trained ResNet classifer model 
+path = Path()
+learn_inf = load_learner(path/'export.pkl')
+learn_inf.dls.vocab
+image = gr.Image(shape=(180,180))
+
+## Define predicting action
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn_inf.predict(img)
+    return f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
+
+## Define title, description and emoji
+note_text = '\U00002728' + 'This plant disease detector targets on diagnosing the leaves disease of fruit and vegetables. Upload the leaf picture here! ' + '\U0001FA84'
+
+## Set Gradio interface 
+gr_interface = gr.Interface(fn=predict, inputs=gr.Image(shape=(180, 180)),outputs=gr.Label(num_top_classes=len(learn_inf.dls.vocab)), interpretation="default",title = '\U0001F31D'+'Plant Disease Detector'+'\U0001FAB4',description = note_text, theme='gradio/seafoam')
+
+## Use interface launch
+gr_interface.launch(share=True)
+
+
+
+
+

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6ddbf0376c62b8d550a826810b8e37d85ebbda1688c6779c984f7c110b1616ff
+size 90784031

requirements.txt

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+fastai==2.7.13
+gdown==4.5.1
+gradio==3.50.2