Update app.py

app.py

@@ -1,32 +1,28 @@
-import requests
-
 import gradio as gr
-import torch
-from timm import create_model
-from timm.data import resolve_data_config
-from timm.data.transforms_factory import create_transform
-
-IMAGENET_1k_URL = "https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt"
-LABELS = requests.get(IMAGENET_1k_URL).text.strip().split('\n')
-
-model = create_model('resnet50', pretrained=True)
+from fastai.vision.all import *
+import skimage
+from huggingface_hub import from_pretrained_fastai
 
-transform = create_transform(
-    **resolve_data_config({}, model=model)
-)
-model.eval()
 
-def predict_fn(img):
-    img = img.convert('RGB')
-    img = transform(img).unsqueeze(0)
+def label_func(f): return f.name[:2]
 
-    with torch.no_grad():
-        out = model(img)
-    
-    probabilites = torch.nn.functional.softmax(out[0], dim=0)
+learn = from_pretrained_fastai("smaciu/bee-wings-classifier")
 
-    values, indices = torch.topk(probabilites, k=5)
+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))}
 
-    return {LABELS[i]: v.item() for i, v in zip(indices, values)}
+title="Honey Bee Wing Classifier"
+description = "A bee wings classifier trained with fastai"
+examples = ['ES-0003-2092-0.dw.png','HU-0001-2019-000007-R.dw.png','PL-0002-000187-R.dw.png']
+enable_queue=True
 
-gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label').launch()
+gr.Interface(fn=predict,
+             inputs=gr.inputs.Image(type='pil'),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             title=title,
+             description=description,
+             examples=examples,
+             enable_queue=enable_queue).launch()