Update app.py to use model repository

app.py

@@ -1,25 +1,35 @@
-#cell
 from fastai.vision.all import *
+from fastai.vision.core import PILImage
 import gradio as gr
+from transformers import pipeline
 
-learn = load_learner('model.pkl')
-labels = learn.dls.vocab
+# Load the pre-trained model and tokenizer
+model_name = "jijinAI/bird-detection"
+classifier = pipeline("image-classification", model=model_name)
+
+# Get labels from the model
+labels = classifier.model.config.id2label
 
 def classify_image(img):
     img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    predictions = classifier(img)
+    return {labels[pred['label']]: float(pred['score']) for pred in predictions}
 
- # Cell
+# Create Gradio interface
 image = gr.Image()
 
-
-label_list ="<ul>\n"
-label_list += "\n".join(["<li>" + str(s) + "</li>" for s in labels])
+label_list = "<ul>\n"
+label_list += "\n".join(["<li>" + str(s) + "</li>" for s in labels.values()])
 label_list += "\n</ul>"
-description= "Try out this bird classifier by uploading an image of a species listed below and hit submit" + label_list
+description = "Try out this bird classifier by uploading an image of a species listed below and hit submit" + label_list
 
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=image,
+    outputs=gr.outputs.Label(num_top_classes=3),
+    title="Bird Species Classifier",
+    description=description
+)
 
-intf = gr.Interface(fn=classify_image, title="NZ Endangered Bird Classifier", inputs=image, article=description, outputs=gr.Label(num_top_classes=len(labels)))
-intf.launch(inline=False)
-    
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

@@ -1,2 +1,3 @@
 fastai
+transformers
 flask