Update app.py

app.py

@@ -96,7 +96,7 @@ def run_protgpt2(startsequence, length, repetitionPenalty, top_k_poolsize, max_s
     print("Cleaning up after protGPT2")
     print(gpu_usage())
     del protgpt2
-    torch.cuda.empty_cache()
+    #torch.cuda.empty_cache()
     device = cuda.get_current_device()
     device.reset()
     print(gpu_usage())
@@ -105,6 +105,8 @@ def run_protgpt2(startsequence, length, repetitionPenalty, top_k_poolsize, max_s
 
 def run_alphafold(startsequence):
     print(gpu_usage())
+    device = cuda.get_current_device()
+    device.reset()
     model_runners = {}
     models = ["model_1"]  # ,"model_2","model_3","model_4","model_5"]
     for model_name in models:
@@ -127,8 +129,8 @@ def run_alphafold(startsequence):
     plddts = predict_structure("test", feature_dict, model_runners)
     print("Cleaning up after AF2")
     print(gpu_usage())
-    backend = jax.lib.xla_bridge.get_backend()
-    for buf in backend.live_buffers(): buf.delete()
+    #backend = jax.lib.xla_bridge.get_backend()
+    #for buf in backend.live_buffers(): buf.delete()
     #device = cuda.get_current_device()
     #device.reset()
     #print(gpu_usage())
@@ -145,7 +147,7 @@ def update_protGPT2(inp, length,repetitionPenalty, top_k_poolsize, max_seqs):
     for i, seq in enumerate(gen_seqs):
         s = seq.replace("\n","")
         s = "\n".join([s[i:i+70] for i in range(0, len(s), 70)])
-        sequencestxt +=f">seq{i}\n{seq}\n"
+        sequencestxt +=f">seq{i}\n{s}\n"
     return sequencestxt