First commit of app

README.md

@@ -1,14 +1,27 @@
 ---
 title: Biomed.sm.mv Te 84m
-emoji: 🐢
-colorFrom: yellow
-colorTo: yellow
+emoji: 👁‍🗨
+colorFrom: blue
+colorTo: blue
 sdk: gradio
 sdk_version: 5.4.0
 app_file: app.py
 pinned: false
 license: apache-2.0
 short_description: Prediction task tests for biomed-multi-view models
+preload_from_hub:
+    - ibm/biomed.sm.mv-te-84m
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BACE-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BBBP-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-CLINTOX-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-ESOL-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-FREESOLV-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-HIV-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-LIPOPHILICITY-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-MUV-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-QM7-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-SIDER-101
+    - ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-TOX21-101
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,120 @@
+from bmfm_sm.api.smmv_api import SmallMoleculeMultiViewModel
+from bmfm_sm.core.data_modules.namespace import LateFusionStrategy
+from bmfm_sm.api.dataset_registry import DatasetRegistry
+
+import gradio as gr
+
+
+examples = [
+    ["CC(C)CC1=CC=C(C=C1)C(C)C(=O)O", "BACE"],
+    ["CC(C)CC1=CC=C(C=C1)C(C)C(=O)O", "BBBP"],
+    ["[N+](=O)([O-])[O-]", "CLINTOX"],
+    ["OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O", "ESOL"],
+    ["CN(C)C(=O)c1ccc(cc1)OC", "FREESOLV"],
+    ["CC(C)CC1=CC=C(C=C1)C(C)C(=O)O", "HIV"],
+    ["Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14", "LIPOPHILICITY"],
+    ["Cc1cccc(N2CCN(C(=O)C34CC5CC(CC(C5)C3)C4)CC2)c1C", "MUV"],
+    ["C([H])([H])([H])[H]", "QM7"],
+    ["C(CNCCNCCNCCN)N", "SIDER"],
+    ["CCOc1ccc2nc(S(N)(=O)=O)sc2c1", "TOX21"],
+    ["CSc1nc(N)nc(-c2cccc(-c3ccc4[nH]ccc4c3)c2)n1", "Pretrained"]
+]
+
+base_huggingface_path = 'ibm/biomed.sm.mv-te-84m'
+finetuned_huggingface_path = "-MoleculeNet-ligand_scaffold-"
+
+available_datasets = {
+    "BACE": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BACE-101",
+    "BBBP": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-BBBP-101",
+    "CLINTOX": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-CLINTOX-101",
+    "ESOL": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-ESOL-101",
+    "FREESOLV": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-FREESOLV-101",
+    "HIV": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-HIV-101",
+    "LIPOPHILICITY": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-LIPOPHILICITY-101",
+    "MUV": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-MUV-101",
+    "QM7": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-QM7-101",
+    "SIDER": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-SIDER-101",
+    "TOX21": "ibm/biomed.sm.mv-te-84m-MoleculeNet-ligand_scaffold-TOX21-101",
+}
+
+
+class PretrainedSMMVPipeline:
+    def __init__(self, pretrained_model_name_or_path: str):
+        self.model = SmallMoleculeMultiViewModel.from_pretrained(
+            LateFusionStrategy.ATTENTIONAL,
+            model_path=pretrained_model_name_or_path,
+            huggingface=True
+        )
+
+    def __call__(self, smiles: str) -> float:
+        emb = SmallMoleculeMultiViewModel.get_embeddings(
+            smiles=smiles,
+            pretrained_model=self.model
+        )
+        return str(emb.tolist())
+
+
+class FinetunedSMMVPipeline:
+    def __init__(self, dataset:str, pretrained_model_name_or_path: str):
+        dataset_registry = DatasetRegistry()
+        self.ds = dataset_registry.get_dataset_info(dataset)
+        self.model = SmallMoleculeMultiViewModel.from_finetuned(
+            self.ds,
+            model_path=pretrained_model_name_or_path,
+            inference_mode=True,
+            huggingface=True
+        )
+
+    def __call__(self, smiles: str) -> float:
+        prediction = SmallMoleculeMultiViewModel.get_predictions(
+            smiles,
+            self.ds,
+            finetuned_model=self.model
+        )
+        return str(prediction.tolist())
+
+
+def deploy():
+    print(f"Loading checkpoint: Pretrained from {base_huggingface_path}")
+    pipeline_pretrained = PretrainedSMMVPipeline(base_huggingface_path)
+
+    pipelines_finetuned = {}
+    pipelines_finetuned["Pretrained"] = pipeline_pretrained
+
+    for dataset, huggingface_path in available_datasets.items():
+        print(f"Loading checkpoint: {dataset} from {huggingface_path}")
+        pipelines_finetuned[dataset] = FinetunedSMMVPipeline(
+            dataset=dataset,
+            pretrained_model_name_or_path=huggingface_path
+        )
+    
+    def pipeline(
+        smiles: str,
+        dataset: str
+    ):
+        return pipelines_finetuned[dataset](smiles)
+    
+    smiles_input = gr.Textbox(placeholder="SMILES", label="SMILES")
+    datasets_input = gr.Dropdown(
+        choices=list(pipelines_finetuned.keys()),
+        label="Checkpoint",
+    )
+    text_output = gr.Textbox(
+        max_lines=10,
+        label="Prediction",
+    )
+
+    gradio_app = gr.Interface(
+        pipeline,
+        inputs=[smiles_input, datasets_input],
+        outputs=text_output,
+        examples=examples,
+        examples_per_page=20,
+        title="ibm/biomed.sm.mv-te-84m property prediction tasks",
+        description="Predictions for Pretrained show embedding vector of base model. Predictions for datasets show output of model finetuned on that task",
+    )
+
+    gradio_app.launch()
+
+if __name__ == "__main__":
+    deploy()

pre-requirements.txt

@@ -0,0 +1,2 @@
+gradio
+git+https://github.com/BiomedSciAI/biomed-multi-view@main

requirements.txt

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+pyg_lib
+torch_scatter
+torch_cluster
+torch_spline_conv
+-f https://data.pyg.org/whl/torch-2.1.0+cu121.html
+pytorch-fast-transformers==0.4.0