Update app.py

app.py

@@ -1,28 +1,21 @@
 import gradio as gr
 from fastai.vision.all import *
 
-# Train or load your fastai Learner object (replace this with your actual model training or loading code)
-path = untar_data(URLs.PETS)
-dls = ImageDataLoaders.from_folder(path/"images", valid_pct=0.2, seed=42)
-learn = cnn_learner(dls, resnet34)
-learn.fine_tune(1)
+path = Path()
+path.ls(file_exts='.pkl')
 
-# Save the model using PyTorch's torch.save
-torch.save(learn.model, "model.pth")
+# Specify the path to your fastai model file
+learn_inf = load_learner(path/'export.pkl')
 
 # Define the predict function
 def predict(image):
     # Load the input image
     img = PILImage.create(image)
 
-    # Load the model using PyTorch's torch.load
-    model = torch.load("model.pth")
-    model.eval()
+    # Perform inference using the loaded fastai Learner
+    pred, pred_idx, probs = learn_inf.predict(img)
 
-    # Perform inference
-    predicted_class, _, _ = learn.predict(img)
-
-    return predicted_class
+    return f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
 
 # Create Gradio interface
 iface = gr.Interface(