Updating model

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,23 @@
+import gradio as gr
+
+categories = 'grizzly','black','teddy'
+learn_inf = load_learner('model/trained_image_model.pkl')
+
+def classify_image(img):
+    pred, idx, probs = learn_inf.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+image = gr.Image()
+label = gr.Label()
+examples = ["images/grizzly.jpg"]
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(analytics_enabled=False, inline=False, share=False, auth=("admin", "pass1234"))
+
+# demo = gr.Interface(
+#     fn=greet,
+#     inputs=["text", "slider"],
+#     outputs=["text"],
+#     analytics_enabled=False
+# )
+
+# demo.launch(auth=("admin", "pass1234"), share=True)

app.py

@@ -1,13 +1,23 @@
 import gradio as gr
 
-def greet(name, intensity):
-    return "Hello, " + name + "!" * int(intensity)
+categories = 'grizzly','black','teddy'
+learn_inf = load_learner('model/trained_image_model.pkl')
 
-demo = gr.Interface(
-    fn=greet,
-    inputs=["text", "slider"],
-    outputs=["text"],
-    analytics_enabled=False
-)
+def classify_image(img):
+    pred, idx, probs = learn_inf.predict(img)
+    return dict(zip(categories, map(float, probs)))
 
-demo.launch(auth=("admin", "pass1234"), share=True)
+image = gr.Image()
+label = gr.Label()
+examples = ["images/grizzly.jpg"]
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(analytics_enabled=False, inline=False, share=False, auth=("admin", "pass1234"))
+
+# demo = gr.Interface(
+#     fn=greet,
+#     inputs=["text", "slider"],
+#     outputs=["text"],
+#     analytics_enabled=False
+# )
+
+# demo.launch(auth=("admin", "pass1234"), share=True)