instruments model

app.py

@@ -1,22 +1,23 @@
 import gradio as gr
 from fastai.vision.all import *
-
-learn = load_learner('export.pkl')
+model_dir='model/'
+learn = load_learner(model_dir+'jazzinstruments.pkl')
 
 labels = learn.dls.vocab
+labels
 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 = "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']
+title = "Jazz Instruments Classifer"
+description = "A Jazz Instrument trained on a dataset create imagesearch : doublebasses, drumkits, guitars, saxophones, clarinettes"
+article=""
+examples = ["guitare.jpeg","double-bass.jpg","saxophone.jpeg"]
 interpretation='default'
 enable_queue=True
 
 gr.Interface(fn=predict,
              inputs=gr.Image(type="pil"),
-             outputs=gr.Label(num_top_classes=3),
-             examples=["grizzly.jpg"]).launch()
+             outputs=gr.Label(num_top_classes=5),
+             examples=["guitare.jpeg","double-bass.jpg","saxophone.jpeg"]).launch()

export.pkl → model/jazzinstruments.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:062dac4bacf2753c615f86417a633d046be864304909fdd1e18116dd4c7b9a5b
-size 46961933
+oid sha256:e6f0687385bbc380e7f76831d88b69963c30c4cce62dd95ef6e56abb4ee0d1b6
+size 46976566