Update app.py

app.py

@@ -27,13 +27,6 @@ from alphafold.model import data
 from alphafold.model import config
 from alphafold.model import model
 
-data = {'id': [], 'sequence': [],'length': []}
-
-sequencesdf = pd.DataFrame.from_dict(data)
-
-def update_seqs(choice):
-    return gr.Textbox.update(choice)
-
 
 def mk_mock_template(query_sequence):
     """create blank template"""
@@ -127,9 +120,10 @@ def update_protGPT2(inp, length):
     generated_seqs = run_protgpt2(startsequence, seqlen)
     gen_seqs = [x["generated_text"] for x in generated_seqs]
     print(gen_seqs)
-    data = {'id': range(len(gen_seqs)), 'sequence': gen_seqs,'length': [len(x) for x in gen_seqs]}
-    sequencesdf = pd.DataFrame.from_dict(data)
-    return sequencesdf
+    sequencestxt = ""
+    for i, seq in enumerate(gen_seqs):
+        sequencestxt +=f"seq{i}>\n{seq}\n"
+    return sequencestxt
 
 
 def update(inp):
@@ -341,18 +335,16 @@ with proteindream:
             length = gr.Number(value=50, label="Target sequence length")
         btn = gr.Button("Predict sequences using protGPT2")
 
-    results = gr.Dataframe(sequencesdf, label="Results", headers=["id","sequence", "length"])
-    seqChoice = gr.Radio(["sequence 1", "sequence 2", "sequence 3","sequence 4","sequence 5"], label="Choose a sequence")
+    results = gr.Textbox(label="Results", lines=15)
     btn.click(fn=update_protGPT2, inputs=[inp, length], outputs=results)
 
     gr.Markdown("## AlphaFold")
     gr.Markdown(
-        "Select a generated sequence above for structure prediction using AlphaFold2."
+        "Select a generated sequence above and copy it in the field below for structure prediction using AlphaFold2."
     )
     with gr.Group():
         chosenSeq = gr.Textbox(label="Chosen sequence")
         btn2 = gr.Button("Predict structure")
-    seqChoice.change(fn=change_sequence, inputs=seqChoice, outputs=chosenSeq)
     with gr.Group():
         meanpLDDT = gr.Textbox(label="Mean pLDDT of chosen sequence")
         with gr.Row():