from pathlib import Path
import markdown
from PIL import Image
from starlite import (
Controller,
StaticFilesConfig,
get,
post,
Body,
MediaType,
RequestEncodingType,
Starlite,
UploadFile,
Template,
TemplateConfig,
HTTPException
)
from starlite.contrib.jinja import JinjaTemplateEngine
import io
import os
import json
import torch
from torchvision import transforms # type: ignore
import torch.nn.functional as F
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
def load_model(model_path, device="cpu"):
model = torch.load(model_path, map_location=device, weights_only=False)
model.eval()
return model
DOG_MODEL = load_model("server/models/dogs_model.pth")
CAT_MODEL = load_model("server/models/cats_model.pth")
with open("server/meta/labels_dogs.json", "r") as f:
data_labels = f.read()
labels_dogs = json.loads(data_labels)
with open("server/meta/labels_cats.json", "r") as f:
data_labels = f.read()
labels_cats = json.loads(data_labels)
with open("server/meta/images.json", "r") as f:
IMAGES = json.loads(f.read())
def predict_image(image, model, device="cuda") -> list[tuple]:
img_size = (224, 224)
preprocess = transforms.Compose(
[
transforms.Resize(img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
input_tensor = preprocess(image)
input_batch = input_tensor.unsqueeze(0).to(device) # Добавляем dimension для батча
with torch.no_grad():
output = model(input_batch)
probabilities = torch.nn.functional.softmax(output[0], dim=0)
k = 5
topk_probs, predicted_idx = torch.topk(probabilities, k)
data = []
for i in range(k):
data.append((predicted_idx[i].item(), float(topk_probs[i].item())))
return data
breed_dir = Path("server/meta/breed_descriptions")
DOGS_BEERS = []
# Идем по каждому текстовому файлу с описанием породы
for breed_file in breed_dir.glob("*.txt"):
breed_name = breed_file.stem # имя файла без расширения - название породы
description = breed_file.read_text(encoding="utf-8") # читаем описание из файла
DOGS_BEERS.append({
"name": breed_name.replace("_", " "),
"alias": breed_file.stem,
"description": description.strip()
})
DOGS_BEERS.sort(key=lambda b: b["name"])
class BeerdsController(Controller):
path = "/beerds"
@post("/dogs")
async def beerds_dogs(
self, data: UploadFile = Body(media_type=RequestEncodingType.MULTI_PART)
) -> dict:
body = await data.read()
img_file = Image.open(io.BytesIO(body))
predicted_data = predict_image(img_file, DOG_MODEL, "cpu")
results = {}
images = []
for d in predicted_data:
predicted_idx, probabilities = d
predicted_label = labels_dogs[str(predicted_idx)]
name = predicted_label.replace("_", " ")
images.append({
"name": name,
"url": [f"/static/assets/dog/{predicted_label}/{i}" for i in IMAGES['dog'][predicted_label]]
})
results[probabilities] = name
return {
"results": results,
"images": images,
}
@post("/cats")
async def beerds_cats(
self, data: UploadFile = Body(media_type=RequestEncodingType.MULTI_PART)
) -> dict:
body = await data.read()
img_file = Image.open(io.BytesIO(body))
predicted_data = predict_image(img_file, CAT_MODEL, "cpu")
results = {}
images = []
for d in predicted_data:
predicted_idx, probabilities = d
predicted_label = labels_cats[str(predicted_idx)]
name = predicted_label.replace("_", " ")
images.append({
"name": name,
"url": [f"/static/assets/cat/{predicted_label}/{i}" for i in IMAGES['cat'][predicted_label]]
})
results[probabilities] = predicted_label
return {
"results": results,
"images": images,
}
class BaseController(Controller):
path = "/"
@get("/")
async def dogs(self) -> Template:
return Template(name="dogs.html")
@get("/cats")
async def cats(self) -> Template:
return Template(name="cats.html")
@get("/contacts")
async def contacts(self) -> Template:
return Template(name="contacts.html")
@get("/donate")
async def donate(self) -> Template:
return Template(name="donate.html")
@get("/dogs-characteristics")
async def dogs_characteristics(self) -> Template:
return Template(name="dogs-characteristics.html", context={"breeds": DOGS_BEERS})
@get("/dogs-characteristics/{name:str}")
async def beer_description(self, name: str) -> Template:
data = [b for b in DOGS_BEERS if b.get("alias") == name]
if len(data) == 0:
raise HTTPException(status_code=404, detail="Порода не найдена")
return Template(name="beers-description.html", context={
"text": markdown.markdown(data[0].get("description")),
"title": data[0].get("name"),
"images": [f"/static/assets/dog/{name}/{i}" for i in IMAGES['dog'][name]],
})
@get("/sitemap.xml", media_type=MediaType.XML)
async def sitemaps(self) -> bytes:
lastmod = "2025-10-04T19:01:03+00:00"
beers_url = ""
for b in DOGS_BEERS:
beers_url += f'''
https://xn-----6kcp3cadbabfh8a0a.xn--p1ai/dogs-characteristics/{b.get("alias")}
{lastmod}
'''
return f"""
https://xn-----6kcp3cadbabfh8a0a.xn--p1ai/
{lastmod}
https://xn-----6kcp3cadbabfh8a0a.xn--p1ai/cats
{lastmod}
https://xn-----6kcp3cadbabfh8a0a.xn--p1ai/donate
{lastmod}
https://xn-----6kcp3cadbabfh8a0a.xn--p1ai/dogs-characteristics
{lastmod}
{beers_url}
""".encode()
@get("/robots.txt", media_type=MediaType.TEXT)
async def robots(self) -> str:
return """
User-agent: *
Allow: /
Sitemap: https://xn-----6kcp3cadbabfh8a0a.xn--p1ai/sitemap.xml
"""
app = Starlite(
debug=True,
route_handlers=[BeerdsController, BaseController],
static_files_config=[
StaticFilesConfig(directories=[Path("server/static")], path="/static"),
],
template_config=TemplateConfig(
directory=Path("server/templates"),
engine=JinjaTemplateEngine,
),
)