Best design renders (#966)

gateway/utils/web3.go

@@ -70,10 +70,12 @@ func BuildTokenMetadata(db *gorm.DB, flow *models.Flow) (string, error) {
 	metadata := map[string]interface{}{
 		"name":        flow.Name,
 		"description": "Research, Reimagined. All Scientists Welcome.",
-		"image":       "ipfs://QmQZLrUPxh4WMmzpQGhUYRsMwU2BXfmFa3YAFhFKkRgHTZ", // Default image is glitchy LabDAO logo gif
+		"image":       "",
 		"flow":        []map[string]interface{}{},
 	}
 
+	var pngCID string
+
 	for _, job := range jobs {
 		var tool models.Tool
 		if err := db.Where("cid = ?", job.ToolID).First(&tool).Error; err != nil {
@@ -150,9 +152,20 @@ func BuildTokenMetadata(db *gorm.DB, flow *models.Flow) (string, error) {
 					"cid":      outputPinataHash,
 					"filename": outputFile.Filename,
 				})
+
+				if filepath.Ext(outputFile.Filename) == ".png" && pngCID == "" {
+					pngCID = outputPinataHash
+				}
 			}
 		}
 
+		if pngCID != "" {
+			metadata["image"] = "ipfs://" + pngCID
+		} else {
+			// Default image is glitchy LabDAO logo gif
+			metadata["image"] = "ipfs://QmQZLrUPxh4WMmzpQGhUYRsMwU2BXfmFa3YAFhFKkRgHTZ"
+		}
+
 		metadata["flow"] = append(metadata["flow"].([]map[string]interface{}), ioObject)
 	}
 

tools/colabdesign/colabdesign.json

@@ -1,5 +1,5 @@
 {
-  "name": "colabdesign v1.7",
+  "name": "colabdesign v1.8",
   "description": "RFdiffusion is a method for structure generation, with or without conditional information (a motif, target etc). It can perform a whole range of protein design challenges. This version includes creating checkpoints for this model",
   "guide": "Note: steps marked (optional) and (advanced) give additional constraints for the designs but it is not necessary to add/edit their input in order to submit an experimental run.\n\n1. Select a .pdb file containing the *target protein* you would like to design a binder against.\n2. (optional) Select a *target chain* from the pdb file (chain A is the default). \n3. (optional) Crop the target structure by specifying the *target start residue* and the *target end residue* on the target chain. \n4. (optional) Define *hotspot* residues on the target as the binding sites, stating the chain (e.g. A) followed by the number of the residue (e.g. A30). You can specify multiple hotspots at once.\n5. Specify the *binder length* (total number of residues) you wish to design, followed by the *number of binders* to design (max 8 per run). \n6. (advanced) Define a *contigs override* that constrains the binder design to a subset of the residues. The contig consists of information on the target and the binder, separated by a colon. Residues which are to be kept fixed, are indicated by the chain label followed by the range of residue numbers. A number without a chain label indicates the number of residues to be designed.",  "author": "sokrypton",
   "github": "https://github.com/sokrypton/ColabDesign",
@@ -12,7 +12,7 @@
   ],
   "arguments": [
   ],
-  "dockerPull": "quay.io/labdao/colabdesign@sha256:9c149aedc355f7b1ef733f984d1966699be9ec9df2e8b758fd3a035303f078f8",
+  "dockerPull": "quay.io/labdao/colabdesign@sha256:cdff5c0870c6cf67f2d3501c5318be9e56c6fab25d714b22259e29934302bb98",
   "gpuBool": true,
   "memoryGB": 12,
   "cpu": 3,

tools/colabdesign/main.py

@@ -107,7 +107,7 @@ def create_and_upload_checkpoints(df_results, result_csv_path, flow_uuid, job_uu
     best_per_design.reset_index(inplace=True)
 
     for index, row in best_per_design.iterrows():
-        plddt_for_checkpoint = row['plddt'] *100
+        plddt_for_checkpoint = row['plddt'] * 100
         i_pae_for_checkpoint = row['i_pae']
         design = row['design']
         n = row['n']
@@ -124,23 +124,27 @@ def create_and_upload_checkpoints(df_results, result_csv_path, flow_uuid, job_uu
         }, index=[0])
         event_csv_filename = f"checkpoint_{index}_summary.csv"
         event_csv_filepath = f"{checkpoint_csv_path}/{event_csv_filename}"
-        new_df.to_csv(event_csv_filepath, index= False)
+        new_df.to_csv(event_csv_filepath, index=False)
 
         bucket_name = "app-checkpoint-bucket"
         object_name = f"checkpoints/{flow_uuid}/{job_uuid}/checkpoint_{index}"
         upload_to_s3(event_csv_filepath, bucket_name, f"{object_name}/{event_csv_filename}")
         upload_to_s3(pdb_path, bucket_name, f"{object_name}/{pdb_file_name}")
         print(f"Checkpoint {index} event CSV and PDB created and uploaded.")
 
-        print("visualising the protein complex")
-        result = visualise_protein_complex(pdb_path, result_csv_path)
-        png_file_path = result.get('png')
-        png_file_name = os.path.basename(png_file_path)
-        bucket_name = "app-record-bucket"
-        object_name = f"visualizations/{flow_uuid}/{job_uuid}"
-        upload_to_s3(png_file_path, bucket_name, f"{object_name}/{png_file_name}")
-        os.remove(event_csv_filepath)
-        print(f"Visualizations uploaded to S3.")
+    overall_best_design = df_sorted.iloc[0]
+    overall_best_design_pdb_file_name = f"design{overall_best_design['design']}_n{overall_best_design['n']}.pdb"
+    overall_best_design_pdb_path = f"{checkpoint_csv_path}/default/all_pdb/{overall_best_design_pdb_file_name}"
+
+    print("Visualizing the protein complex for the overall best design")
+    result = visualise_protein_complex(overall_best_design_pdb_path, result_csv_path)
+    png_file_path = result.get('png')
+    png_file_name = os.path.basename(png_file_path)
+    bucket_name = "app-record-bucket"
+    object_name = f"visualizations/{flow_uuid}/{job_uuid}"
+    upload_to_s3(png_file_path, bucket_name, f"{object_name}/{png_file_name}")
+    os.remove(event_csv_filepath)
+    print(f"Visualization for the overall best design uploaded to S3.")
     return
 
 def enricher(multirun_path, cfg, flow_uuid, job_uuid, checkpoint_compatible):