Created initial version of a Gradio app.

app.py

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+import protac_degradation_predictor as pdp
+
+from typing import Dict, List, Literal
+import difflib
+
+import torch
+import numpy as np
+from rdkit import Chem
+import gradio as gr
+
+
+def gradio_app(
+        protac_smiles: str | List[str],
+        e3_ligase: str | List[str],
+        target_uniprot: str | List[str],
+        cell_line: str | List[str],
+        use_models_from_cv: bool = False,
+) -> Dict[str, np.ndarray]:
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    avail_uniprots = pdp.avail_uniprots()
+    avail_cells = pdp.avail_cell_lines()
+
+    if target_uniprot not in avail_uniprots:
+        suggestions = difflib.get_close_matches(target_uniprot, avail_uniprots, n=3, cutoff=0.5)
+        suggestion_text = "Did you mean:" + ", ".join(suggestions) + "?" if suggestions else "No close matches found."
+        raise gr.Error(f"Invalid Uniprot ID. {suggestion_text}", duration=None)
+
+    if cell_line not in avail_cells:
+        suggestions = difflib.get_close_matches(cell_line, avail_cells, n=3, cutoff=0.5)
+        suggestion_text = "Did you mean:" + ", ".join(suggestions) + "?" if suggestions else "No close matches found."
+        raise gr.Error(f"Invalid Cell Line. {suggestion_text}", duration=None)
+
+    prediction = pdp.get_protac_active_proba(
+        protac_smiles,
+        e3_ligase,
+        target_uniprot,
+        cell_line,
+        device=device,
+        use_models_from_cv=use_models_from_cv,
+    )
+    mean_pred = {"Active": prediction['mean'], "Inactive": 1 - prediction['mean']}
+    majvote_pred = "Active" if prediction['majority_vote'] else "Inactive"
+    return mean_pred, majvote_pred
+
+demo = gr.Interface(
+    fn=gradio_app,
+    inputs=[
+        gr.Textbox(placeholder="PROTAC SMILES", label="PROTAC SMILES"),
+        gr.Dropdown(pdp.avail_e3_ligases(), label="E3 ligase"),
+        gr.Textbox(placeholder="E.g., Q92769", label="Target Uniprot"),
+        gr.Textbox(placeholder="E.g., HeLa", label="Cell line"),
+        gr.Checkbox(label="Use models trained during cross-validation"),
+    ],
+    outputs=[gr.Label(label="Average probability (confidence)"), gr.Label(label="Majority vote prediction")],
+    title="PROTAC Degradation Predictor",
+    examples=[        [
+            "Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O)[C@@H](NC(=O)COCCCCCCCCCOCC(=O)Nc2ccc(C(=O)Nc3ccc(F)cc3N)cc2)C(C)(C)C)cc1",
+            "VHL",
+            "Q92769",
+            "HeLa",
+        ],
+    ],
+    description="Predict whether a PROTAC is active or inactive.",
+)
+
+demo.launch()