BindingAffinity/main.py at main · AlexanderSychev2005/BindingAffinity · GitHub

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import os
import uuid

from fastapi import FastAPI, Request, Form
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles
from fastapi.responses import HTMLResponse
from utils import (
    get_inference_data,
    get_py3dmol_view,
    save_standalone_ngl_html,
    get_lipinski_properties,
    get_gemini_explanation,
)

app = FastAPI()

os.makedirs("html_results", exist_ok=True)

app.mount("/results", StaticFiles(directory="html_results"), name="results")
app.mount("/static", StaticFiles(directory="static"), name="static")

templates = Jinja2Templates(directory="templates")


@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})


@app.post("/predict", response_class=HTMLResponse)
async def predict(
    request: Request, smiles_ligand: str = Form(...), sequence_protein: str = Form(...)
):
    mol, importance, affinity = get_inference_data(smiles_ligand, sequence_protein)

    atom_list = []
    sorted_indices = sorted(
        range(len(importance)), key=lambda k: importance[k], reverse=True
    )

    for idx in sorted_indices[:15]:
        val = importance[idx]
        symbol = mol.GetAtomWithIdx(idx).GetSymbol()

        icon = ""
        if val >= 0.9:
            icon = "🔥"
        elif val >= 0.7:
            icon = "✨"
        elif val >= 0.5:
            icon = "⭐"
        atom_list.append(
            {"id": idx, "symbol": symbol, "score": f"{val:.3f}", "icon": icon}
        )

    unique_id = str(uuid.uuid4())

    filename_ngl = f"ngl_{unique_id}.html"
    filepath_ngl = os.path.join("html_results", filename_ngl)

    py3dmol_view = get_py3dmol_view(mol, importance)
    py3dmol_content = py3dmol_view._make_html()

    # ngl_view = get_ngl_view(mol, importance)
    # nv.write_html(filepath_ngl, ngl_view)

    save_standalone_ngl_html(mol, importance, filepath_ngl)

    ngl_url_link = f"/results/{filename_ngl}"

    lipinski_properties = get_lipinski_properties(mol)

    ai_explanation = get_gemini_explanation(
        smiles_ligand,
        sequence_protein,
        f"{affinity:.2f}",
        atom_list,
        lipinski_properties,
    )

    return templates.TemplateResponse(
        "index.html",
        {
            "request": request,
            "result_ready": True,
            "smiles": smiles_ligand,
            "protein": sequence_protein,
            "affinity": f"{affinity:.2f}",
            "atom_list": atom_list,
            "html_py3dmol": py3dmol_content,
            "url_ngl": ngl_url_link,
            "lipinski": lipinski_properties,
            "ai_explanation": ai_explanation,
        },
    )