use LFS with only model.pkl?

README.md

@@ -1,3 +1,13 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:02e3e6211e8c301209dfc516bbbf5ddf15bd3c7d79b34aa15e9adc456777a503
-size 258
+---
+title: Cactus Classifier
+emoji: 👁
+colorFrom: yellow
+colorTo: green
+sdk: gradio
+sdk_version: 3.1.7
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,3 +1,39 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e8d19e9e26f83f591771a1c0f4485919a06be18a67ee299c8bac5438c334f178
-size 1033
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+
+from fastai.learner import load_learner
+
+
+learner = load_learner(Path("model.pkl"))
+labels = learner.dls.vocab
+
+
+def classify(image: np.array):
+    results = learner.predict(image)
+    probabilities = results[2]
+    probabilities = map(float, probabilities)
+    return dict(zip(labels, probabilities))
+
+
+description = """<p>Give me a cactus picture and I'll try to guess the variety.</p>
+<p>I can recognize the following types of cactus:</p>
+<ul>
+{list_items}
+</ul>
+<p>This model was trained <a href="https://www.kaggle.com/jdsleppy/cactus-classifier/">here</a> by <a href="https://www.joelsleppy.com">Joel</a>.</p>
+""".format(
+list_items='\n'.join([f'  <li>{label}</li>' for label in labels])
+)
+
+
+gr.Interface(
+    fn=classify,
+    inputs="image",
+    outputs="label",
+    examples=["examples/disco.jpg", "examples/scarlet_crown.jpg", "examples/silver_torch.webp"],
+    title="Cactus Classifier",
+    description=description,
+    allow_flagging="never",
+).launch()

requirements.txt

@@ -1,3 +1,3 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7a63b27ead448df70193c8ffc3a42183ef1825e492bf4b92cfe8cec44d03f01e
-size 42
+fastai==2.7.9
+gradio==3.1.7
+numpy==1.23.2