Trained Model added

app.py

@@ -1,3 +1,45 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: dog-or-cat.ipynb.
+
+# %% auto 0
+__all__ = ['learner', 'categories', 'example_imgs_path', 'img', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
+
+# %% dog-or-cat.ipynb 2
+# Imports
+from fastai.vision.all import PILImage
+from fastai.vision.all import load_learner
+from pathlib import Path
 import gradio as gr
-def greet(name): return f"Hello {name}"
-gr.Interface(fn=greet, inputs="text", outputs="text").launch()
+from nbdev.export import nb_export
+
+# %% dog-or-cat.ipynb 3
+# Function to check if it is a cat
+def is_cat(x): return x[0].isupper()
+
+# %% dog-or-cat.ipynb 5
+# Load the trained model
+learner = load_learner(fname="./dog_or_cat.pkl")
+
+# %% dog-or-cat.ipynb 6
+# Test the model
+learner.predict(image)
+
+# %% dog-or-cat.ipynb 7
+categories = ("Cat", "Dog")
+
+def classify_image(img):
+    pred, idx, probs = learner.predict(img)
+    print(pred, idx)
+    return dict(zip(categories, map(float, probs)))
+
+# %% dog-or-cat.ipynb 9
+example_imgs_path = Path("../data/test_images/examples")
+img = gr.Image()
+label = gr.Label()
+examples = [
+    example_imgs_path/f"dog.jpg",
+    example_imgs_path/f"cat.jpg",
+    example_imgs_path/f"dunno.jpg"
+]
+
+intf = gr.Interface(fn=classify_image, inputs=img, outputs=label, examples=examples)
+intf.launch(inline=False)

dog_or_cat.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:793bbbbf65f9bccf33ab0755b5e4c4b0d2fb4477ffb5d63cc0703ef4db7b86c8
+size 46967175