Update app.py

app.py

@@ -1,35 +1,20 @@
-import streamlit as st
-import os
-from PIL import Image
-import time
+import gradio as gr
 from fastai.vision.all import *
-from fastai.learner import load_learner
+import skimage
 
-def GetLabel(img):
-    return img.split('-')[0]
-    
-
-#Load the Learner (Exported from ipnyb file with learn.export() )
 learn = load_learner('export.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))}
 
-#Classify image
-def classify_image(cl_img):
-    img = Image.open(cl_img)
-    st.image(img)
-    pred, _ , _ = learn.predict(img)
-    return pred
-
-
-    
-    
-st.set_page_config(page_title="PyTorch Food Classifier - FastAI 2022", page_icon=":robot:")
-st.header("PyTorch Food Classifier")
-
-file_up = st.file_uploader("Upload Your Food Image Below", type=["jpg","png"])
+title = "Pet Breed Classifier"
+description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ['siamese.jpg']
+interpretation='default'
+enable_queue=True
 
-if st.button('Run Model'):
-    st.write("Button Pressed")
-    cl_done = classify_image(file_up)
-    
-    st.write(f"Your food is:  {cl_done}")
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()