add class and object prediction

app.py

@@ -11,21 +11,41 @@ inputs = gr.components.Image()
 outputs = gr.components.Label()
 examples = "examples"
 
-model = load_learner("model/model.pkl")
-labels = model.dls.vocab
+model_class = load_learner("models/model.class.pkl")
+labels_class = model_class.dls.vocab
 
+model_object = load_learner("models/model.object.pkl")
+labels_object = model_object.dls.vocab
 
-def predict(img):
-    pred, pred_idx, probs = model.predict(img)
-    return dict(zip(labels, map(float, probs)))
 
+def predict_class(img):
+    pred, pred_idx, probs = model_class.predict(img)
+    return dict(zip(labels_class, map(float, probs)))
+
+
+def predict_object(img):
+    pred, pred_idx, probs = model_object.predict(img)
+    return dict(zip(labels_object, map(float, probs)))
+
+
+with gr.Blocks() as demo:
+    with gr.Tab("Class Prediction"):
+        gr.Interface(
+            fn=predict_class,
+            inputs=inputs,
+            outputs=outputs,
+            examples=examples,
+            title=title,
+            description=description,
+        ).queue(default_concurrency_limit=5)
+    with gr.Tab("Object Prediction"):
+        gr.Interface(
+            fn=predict_object,
+            inputs=inputs,
+            outputs=outputs,
+            examples=examples,
+            title=title,
+            description=description,
+        ).queue(default_concurrency_limit=5)
 
-demo = gr.Interface(
-    fn=predict,
-    inputs=inputs,
-    outputs=outputs,
-    examples=examples,
-    title=title,
-    description=description,
-).queue(default_concurrency_limit=5)
 demo.launch()