Update app.py

app.py

@@ -1,16 +1,33 @@
 import gradio as gr
 
-# Define your model prediction function
-def predict(selected_option):
-    # Implement your model prediction logic here based on the selected_option
-    result = f"You selected: {selected_option}"
-    return result
 
-# Create a Dropdown input component
-input_dropdown = gr.inputs.Dropdown(choices=["Option 1", "Option 2", "Option 3"])
+def sentence_builder(quantity, animal, countries, place, activity_list, morning):
+    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
 
-# Create the Gradio interface
-iface = gr.Interface(fn=predict, inputs=input_dropdown, outputs="text")
 
-# Launch the interface
-iface.launch()
+demo = gr.Interface(
+    sentence_builder,
+    [
+        gr.Slider(2, 20, value=4, label="Count", info="Choose between 2 and 20"),
+        gr.Dropdown(
+            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
+        ),
+        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
+        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
+        gr.Dropdown(
+            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
+        ),
+        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
+    ],
+    "text",
+    examples=[
+        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
+        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
+        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
+        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()
+