app.py

# app.py
# --- IMPORTS ---
import os
import re
import esm
import gradio as gr
import torch
from torch.utils.data import DataLoader
import lightning.pytorch as pl
from protobind_diff.model import ModelGenerator
from protobind_diff.data_loader import InferenceDataset
from huggingface_hub import hf_hub_download
import spaces
from pathlib import Path
import uuid, json, hashlib
from huggingface_hub import CommitScheduler
from datetime import datetime


# Hugging Face Hub details
REPO_ID = "ai-gero/ProtoBind-Diff"
MODEL_FILENAME = "model.ckpt"
TOKENIZER_FILENAME = "tokenizer_smiles_diffusion.json"
Request = gr.Request

@spaces.GPU(duration=120) 
def generate_smiles_for_sequence(protein_sequence: str, num_samples: int, request: Request):
    """
    The main prediction function that runs the full pipeline.
    """
    log_run(request.client.host or "unknown", protein_sequence)
    if not protein_sequence:
        raise gr.Error("Protein sequence cannot be empty.")
    protein_sequence = re.sub(r"[^A-Z]", "", protein_sequence.upper())
    if len(protein_sequence) < 10:
        raise gr.Error("Protein sequence is too short.")

    device = "cuda" 
    esm_model.to(device)
    protobind_model.to(device) 

    with torch.no_grad():
        batch_converter = alphabet.get_batch_converter()
        _, _, tokens = batch_converter([("protein", protein_sequence)])
        tokens = tokens.to(device)
        embedding = esm_model(tokens, repr_layers=[33])["representations"][33][:, 1:-1, :]
        embedding = embedding.float() if device == "cpu" else embedding.bfloat16()

    n_batches = num_samples // 10
    dataset = InferenceDataset(embedding, batch_size=10, n_batches=n_batches)
    loader = DataLoader(dataset, batch_size=None)

    trainer = pl.Trainer(
        accelerator="auto",
        devices=1,
        logger=False,
        precision="16-mixed" if device == "cuda" else "32-true"
    )

    predictions_batches = trainer.predict(model=protobind_model, dataloaders=loader)

    all_smiles = [smi for batch in predictions_batches for smi in batch[0]]
    unique_smiles = list(set(all_smiles))

    return ",\n".join(unique_smiles)

def enable_btn(seq: str):
    return gr.update(interactive=len(seq) >= 10)


def log_run(client_ip: str, seq: str):
    rec = {
        "ts": datetime.utcnow().isoformat(timespec="seconds"),
        "client": client_ip,
        "seq": seq,
        "seq_len": len(seq)
    }
    (LOG_FOLDER / f"{uuid.uuid4()}.json").write_text(json.dumps(rec))


LOG_FOLDER = Path("usage_logs"); LOG_FOLDER.mkdir(exist_ok=True)

scheduler = CommitScheduler(
    repo_id="ai-gero/protobind_usage",
    repo_type="dataset",
    folder_path=str(LOG_FOLDER),
    every=1,
    token=os.getenv("HF_TOKEN")
)
# --- GRADIO APP DEFINITION ---

# Load models on app startup
device = "cpu"
esm_model, alphabet = esm.pretrained.load_model_and_alphabet('esm2_t33_650M_UR50D')
esm_model.eval()
esm_model = esm_model.to(device)

tokenizer_path = hf_hub_download(
    repo_id=REPO_ID,
    filename=TOKENIZER_FILENAME,
)
ckpt_path = hf_hub_download(
    repo_id=REPO_ID,
    filename=MODEL_FILENAME,
)
protobind_model = ModelGenerator.load_from_checkpoint(
    ckpt_path,
    map_location=device,
    tokenizer_path=tokenizer_path,
    seq_embedding_dim=1280,
    load=True,
)
protobind_model.eval()
protobind_model.to(device)
# Define the UI
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # ProtoBind-Diff: Protein-Conditioned Ligand Generation
        This Space demonstrates **ProtoBind-Diff**, a diffusion model for generating novel drug-like molecules (ligands)
        conditioned on a target protein sequence. Provide a protein's amino acid sequence to generate potential binding molecules in SMILES format.
        """
    )

    with gr.Row():
        with gr.Column(scale=2):
            protein_sequence = gr.Textbox(
                lines=10,
                label="Protein Amino Acid Sequence",
                placeholder="Enter your protein sequence here (e.g., MGY...)"
            )
            num_samples = gr.Slider(
                minimum=10,
                maximum=200,
                value=50,
                step=10,
                label="Generation Attempts",
                info=(
                    "Upper limit on generation attempts. Duplicates and invalid molecules "
                    "are discarded, so the final count of unique molecules may be lower. "
                    "More attempts increase runtime but can improve diversity."
                )
            )
            submit_btn = gr.Button("Generate Molecules", variant="primary", interactive=False)

        with gr.Column(scale=3):
            output_smiles = gr.Textbox(
                lines=15,
                label="Generated SMILES",
                info="A list of unique, valid SMILES strings generated for the target protein.",
                interactive=True
            )


    gr.Markdown("### Examples")
    gr.Examples(
        examples=[
            ["MAAAAAAGAGPEMVRGQVFDVGPRYTNLSYIGEGAYGMVCSAYDNVNKVRVAIKKISPFEHQTYCQRTLREIKILLRFRHENIIGINDIIRAPTIEQMKDVYIVQDLMETDLYKLLKTQHLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLLNTTCDLKICDFGLARVADPDHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINLKARNYLLSLPHKNKVPWNRLFPNADSKALDLLDKMLTFNPHKRIEVEQALAHPYLEQYYDPSDEPIAEAPFKFDMELDDLPKEKLKELIFEETARFQPGYRS",
             50],
            ["MDILCEENTSLSSTTNSLMQLNDDTRLYSNDFNSGEANTSDAFNWTVDSENRTNLSCEGCLSPSCLSLLHLQEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGISMPIPVFGLQDDSKVFKEGSCLLADDNFVLIGSFVSFFIPLTIMVITYFLTIKSLQKEATLCVSDLGTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNEDVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENKKPLQLILVNTIPALAYKSSQLQMGQKKNSKQDAKTTDNDCSMVALGKQHSEEASKDNSDGVNEKVSCV",
             100]
        ],
        inputs=[protein_sequence, num_samples],
        cache_examples=False,
    )

    gr.Markdown(
        """
        ---
        *Model developed by Gero AI. For more details, check out the [original repository](https://github.com/gero-science/ProtoBind-Diff).*
        """
    )
    protein_sequence.change(enable_btn, inputs=protein_sequence, outputs=submit_btn)

    submit_btn.click(
        fn=generate_smiles_for_sequence,
        inputs=[protein_sequence, num_samples],
        outputs=output_smiles
    )

# Launch the app
if __name__ == "__main__":
    demo.queue(max_size=10)
    demo.launch(share=True)