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e33b6c9 13188b8 e33b6c9 34886ef e33b6c9 34886ef e33b6c9 13188b8 e33b6c9 13188b8 e33b6c9 13188b8 e33b6c9 13188b8 e33b6c9 34886ef 543ad41 13188b8 543ad41 13188b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | 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,
},
)
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