new day

.gradio/flagged/dataset1.csv

@@ -0,0 +1,2 @@
+img,output,timestamp
+.gradio/flagged/img/2bf96196995f897eb82f/amanite.jpg,"{""label"": null, ""confidences"": null}",2025-05-24 11:07:40.000039

app.py

@@ -1,20 +1,18 @@
-from huggingface_hub import from_pretrained_fastai
+from fastai.vision.all import *
 import gradio as gr
 
-def which_champi(x): return x[0].isupper()
-
 # Load the model
-learn = from_pretrained_fastai('./modelchampi.pkl')
+learn = load_learner('modelchampi.pkl')
 
 categories = ('amanite','bolet', 'morille', 'truffe')
 
 def classify_image(img):
-    pred_idx_probs = learn.predict(img)
+    pred,idx,probs = learn.predict(img)
     return dict(zip(categories, map(float,probs)))
 
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-examples = ['amanite.png', 'truffe.png']
+image = gr.Image(height=192, width=192)
+label = gr.Label()
+examples = ['amanite.jpg', 'truffe.jpg']
 
 intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
 intf.launch(inline=False)