Cats and dogs

app.py

@@ -1,7 +1,70 @@
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+def is_cat(x):
+    return x[0].isupper()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+learn = load_learner('model.pkl')
+
+categories = ('Cat', 'Dog')
+
+prompts = [
+    "# Definitely a {}!",
+    "# Well, that must be a {}!",
+    "# Oh, that's a {}!",
+    "# That's a {}!",
+    "# Looks like a {} to me!",
+]
+
+failure_prompts = [
+    "# I'm not sure what that is.",
+    "# I don't know what that thing is.",
+    "# I've never seen that before.",
+    "# Looks familiar, but unsure.",
+    "# Something, something?",
+    "# Beats me.",
+]
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+def calculate(confidence_threshold, img):
+    classifications = classify_image(img)
+    classification = random.choice(failure_prompts)
+    for key, value in classifications.items():
+        if value > confidence_threshold:
+            classification = random.choice(prompts).format(key)
+            break
+
+    return [classification, classifications]
+
+
+with gr.Blocks() as ui:
+
+    heading = gr.Markdown(" # Dog or Cat?", render=False)
+    results = gr.Label(value="Waiting to receive image.", label="Details", show_label=False, render=False)
+
+    with gr.Row(equal_height=True):
+
+        with gr.Column():
+            gr.Markdown("Upload an image of a cat or a dog.")
+
+            with gr.Group():
+                image = gr.Image(show_label=False, height=300)
+                confidence = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, label="Confidence Threshold")
+                btn = gr.Button(value="Classify")
+                btn.click(calculate, inputs=[confidence, image], outputs=[heading, results])
+
+        with gr.Column():
+            gr.Markdown("Then wait for the magic to happen")
+            with gr.Group():
+                results.render()
+                heading.render()
+
+    gr.Markdown(" # Examples")
+    with gr.Group():
+        gr.Examples(inputs=image, examples=['images/cat1.jpeg', 'images/cat2.jpeg', 'images/cat3.jpeg', 'images/dog1.jpeg', 'images/dog2.jpeg', 'images/dog3.jpeg'])
+
+if __name__ == "__main__":
+    ui.launch()

requirements.txt

@@ -0,0 +1,2 @@
+fastai
+gradio