hi

app.py

@@ -215,7 +215,7 @@ with gr.Blocks() as demo:
 
     with gr.Tab("Genus Prediction"):
         gr.Markdown("""
-        # Genus prediction
+        ## Genus prediction
           
         A demo of predicting the genus of a DNA sequence using multiple
         approaches (method dropdown):
@@ -228,35 +228,55 @@ with gr.Blocks() as demo:
           that we precomputed and stored in a Pinecone index. Thie method
           DOES NOT examine ecological layer data.
         """)
-        gr.Interface(
+        # gr.Interface(
+        #     fn=predict_genus,
+        #     inputs=[
+        #         gr.Dropdown(choices=["cosine", "fine_tuned_model"], value="fine_tuned_model"),
+        #         inp_dna,
+        #         inp_lat,
+        #         inp_lng,
+        #     ],
+        #     outputs=["image"],
+        #     allow_flagging="never",
+        # )
+        
+        method_dropdown = gr.Dropdown(choices=["cosine", "fine_tuned_model"], value="fine_tuned_model")
+        predict_button = gr.Button("Predict Genus")
+        genus_output = gr.Image()
+        
+        predict_button.click(
             fn=predict_genus,
-            inputs=[
-                gr.Dropdown(choices=["cosine", "fine_tuned_model"], value="fine_tuned_model"),
-                inp_dna,
-                inp_lat,
-                inp_lng,
-            ],
-            outputs=["image"],
-            allow_flagging="never",
+            inputs=[method_dropdown, inp_dna, inp_lat, inp_lng],
+            outputs=genus_output
         )
 
     with gr.Tab("DNA Embedding Space Visualizer"):
         gr.Markdown("""
-        # DNA Embedding Space Visualizer
+        ## DNA Embedding Space Visualizer
                     
         We show a 2D t-SNE plot of the DNA embeddings of the five most common
         genera in our dataset. This shows that the DNA Transformer model is
         learning to cluster similar DNA sequences together.
         """)
 
-        gr.Interface(
+        # gr.Interface(
+        #     fn=cluster_dna,
+        #     inputs=[
+        #         gr.Slider(minimum=1, maximum=10, step=1, value=5,
+        #                   label="Number of top genera to visualize")
+        #     ],
+        #     outputs=["image"],
+        #     allow_flagging="never",
+        # )
+
+        top_k_slider = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Number of top genera to visualize")
+        visualize_button = gr.Button("Visualize Embedding Space")
+        visualize_output = gr.Image()
+        
+        visualize_button.click(
             fn=cluster_dna,
-            inputs=[
-                gr.Slider(minimum=1, maximum=10, step=1, value=5,
-                          label="Number of top genera to visualize")
-            ],
-            outputs=["image"],
-            allow_flagging="never",
+            inputs=top_k_slider,
+            outputs=visualize_output
         )
 
 demo.launch()