Update app.py

app.py

@@ -10,7 +10,7 @@ import re
 import urllib.request
 import yaml 
 
-from msa import run_mmseqs2
+#from msa import run_mmseqs2
 
 CCD_URL = "https://huggingface.co/boltz-community/boltz-1/resolve/main/ccd.pkl"
 MODEL_URL = "https://huggingface.co/boltz-community/boltz-1/resolve/main/boltz1.ckpt"
@@ -52,7 +52,7 @@ def predict(jobname, inputs, recycling_steps, sampling_steps, diffusion_samples)
     # ], "covMods":[]
     # }
     """
-    sequences_for_msa = []
+    #sequences_for_msa = []
     output = {
     "sequences": []
     }
@@ -67,8 +67,9 @@ def predict(jobname, inputs, recycling_steps, sampling_steps, diffusion_samples)
         if entity_type in ["protein", "dna", "rna"]:
             sequence_data[entity_type]["sequence"] = chain["sequence"]
             if entity_type == "protein":
-                sequences_for_msa.append(chain["sequence"])
-                sequence_data[entity_type]["msa"] = f"{jobname}/msa.a3m"
+                #sequences_for_msa.append(chain["sequence"])
+                if chain["msa"] == False:
+                    sequence_data[entity_type]["msa"] = f"empty"
             representations.append({"model":0, "chain":chain["chain"], "style":"cartoon"})
         if entity_type == "ligand":
             if "sdf" in chain.keys():
@@ -76,7 +77,7 @@ def predict(jobname, inputs, recycling_steps, sampling_steps, diffusion_samples)
                     raise gr.Error("Sorry no SDF support yet")
             if "name" in chain.keys():
                  sequence_data[entity_type]["ccd"] = chain["name"]
-            if "smiles" in chain.keys():
+            elif "smiles" in chain.keys():
                  sequence_data[entity_type]["smiles"] = chain["smiles"]
                 
             representations.append({"model":0, "chain":chain["chain"], "style":"stick", "color":"greenCarbon"})
@@ -93,15 +94,15 @@ def predict(jobname, inputs, recycling_steps, sampling_steps, diffusion_samples)
         yaml.dump(output, file, sort_keys=False, default_flow_style=False)
 
     os.system(f"cat {yaml_file_path}")
-    a3m_lines_mmseqs2 = run_mmseqs2(
-                    sequences_for_msa,
-                    f"./{jobname}",
-                    use_templates=False,
-                )
-    with open(f"{jobname}/msa.a3m", "w+") as fp:
-        fp.writelines(a3m_lines_mmseqs2)
-
-    os.system(f"boltz predict {jobname}/{jobname}.yaml --out_dir {jobname} --recycling_steps {recycling_steps} --sampling_steps {sampling_steps} --diffusion_samples {diffusion_samples} --override --output_format pdb")
+    #a3m_lines_mmseqs2 = run_mmseqs2(
+    #                sequences_for_msa,
+    #                f"./{jobname}",
+    #                use_templates=False,
+    #            )
+    #with open(f"{jobname}/msa.a3m", "w+") as fp:
+    #    fp.writelines(a3m_lines_mmseqs2)
+
+    os.system(f"boltz predict {jobname}/{jobname}.yaml --use_msa_server --out_dir {jobname} --recycling_steps {recycling_steps} --sampling_steps {sampling_steps} --diffusion_samples {diffusion_samples} --override --output_format pdb")
     print(os.listdir(jobname))
     print(os.listdir(f"{jobname}/boltz_results_{jobname}/predictions/{jobname}/"))
     return Molecule3D(f"{jobname}/boltz_results_{jobname}/predictions/{jobname}/{jobname}_model_0.pdb", label="Output", reps=representations)