hi

app.py

@@ -11,13 +11,35 @@ def set_default_inputs():
             DEFAULT_INPUTS["latitude"],
             DEFAULT_INPUTS["longitude"])
 
+def predict_genus():
+    dna_df = pd.read_csv(dna_file.name)
+    dnaenv_df = pd.read_csv(dnaenv_file.name)
+    
+    results = []
+    
+    # envdna_genuses = predict_genus_dna_env(dnaenv_df)
+    # dna_genuses = predict_genus_dna(dna_df)
+    # images = [get_genus_image(genus) for genus in top_5_genuses]
+
+    genuses = xgboost_infer.infer()
+        
+    results.append({
+        "sequence": dna_df['nucraw'],
+        # "predictions": pd.concat([dna_genuses, envdna_genuses], axis=0)
+        'predictions': genuses
+})
+    
+    return results
+
 
 with gr.Blocks() as demo:
     # Header section
     gr.Markdown("# DNA Identifier Tool")
-    gr.Markdown("TODO short description of the tool...")
+    gr.Markdown("Welcome to Lofi Amazon Beats' DNA Identifier Tool")
+
+    with gr.Tab("Genus Prediction"):
+        gr.Markdown("Input a DNA sequence and the coordinates at which its sample was taken to predict the genus of the DNA. Click 'I'm feeling lucky' to see our predictio for a random sequence.")
 
-    with gr.Tab("Genus prediction"):
         # Collect inputs for app (DNA and location)
         with gr.Row():
             inp_dna = gr.Textbox(label="DNA", placeholder="e.g. AACAATGTA... (will be automatically truncated to 660 characters)")
@@ -33,8 +55,13 @@ with gr.Blocks() as demo:
         
         with gr.Row():
             gr.Markdown('Make plot or table for Top 5 species')
+
+        with gr.Column():
+            genus_out = gr.Dataframe(headers=["DNA", "Coord", "DNA Only Pred Genus", "DNA Only Prob", "DNA & Env Pred Genus", "DNA & Env Prob"])
+            btn_run.click(predict_genus, inputs=[inp_dna, inp_lat, inp_lng], outputs=genus_out)
     
     with gr.Tab('DNA Embedding Space Similarity Visualizer'):
         gr.Markdown("If the highest genus probability is very low for your DNA sequence, we can still examine the DNA embedding of the sequence in relation to known samples or clues.")
+        
 
 demo.launch()