gen

app.py

@@ -54,11 +54,11 @@ def generate_peptide(protein_seq, peptide_length, top_k, num_binders):
 
         # Compute PPL for the generated binder
         ppl_value = compute_pseudo_perplexity(model, tokenizer, protein_seq, generated_binder)
-        binders_with_ppl.append((generated_binder, ppl_value))
 
-    # Formatting the output
-    output = "\n".join([f"Binder: {binder}, PPL: {ppl:.2f}" for binder, ppl in binders_with_ppl])
-    return output
+        # Add the generated binder and its PPL to the results list
+        binders_with_ppl.append([generated_binder, ppl_value])
+
+    return binders_with_ppl
 
 # Define the Gradio interface
 interface = gr.Interface(
@@ -67,9 +67,13 @@ interface = gr.Interface(
         gr.Textbox(label="Protein Sequence", info="Enter protein sequence here", type="text"),
         gr.Slider(3, 50, value=15, label="Peptide Length", step=1, info='Default value is 15'),
         gr.Slider(1, 10, value=3, label="Top K Value", step=1, info='Default value is 3'),
-        gr.Dropdown(choices=[1, 2, 4, 8, 16, 32], label="Number of Binders", value=4)
+        gr.Dropdown(choices=[1, 2, 4, 8, 16, 32], label="Number of Binders", default=1)
     ],
-    outputs=gr.outputs.Textbox(label="Binders (with Perplexity)"),
+    outputs=gr.Dataframe(
+        headers=["Binder", "Perplexity"],
+        datatype=["str", "number"],
+        col_count=(2, "fixed")
+    ),
     title="PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling"
 )