main.py

# main.py

from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
import uvicorn
import torch

from models import AnalysisRequest, AnalysisResponse
import logic 

app = FastAPI(title="SEO AI Editor MVP")

# Подключаем папку с шаблонами
templates = Jinja2Templates(directory="templates")

@app.on_event("startup")
async def startup_event():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"🚀 Application starting. ML Device: {device}")
    logic.load_models() # spaCy preload (optional)

# --- НОВЫЙ РОУТ ДЛЯ ГЛАВНОЙ СТРАНИЦЫ ---
@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    # Рендерим файл index.html
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/analyze", response_model=AnalysisResponse)
async def analyze_text(request: AnalysisRequest):
    # Логика та же самая, что и была
    ngram_stats_result = logic.calculate_ngram_stats(
        request.target_text, 
        request.competitors, 
        request.language
    )
    
    key_phrases, key_words_unigrams = logic.parse_keywords(request.keywords, request.language)
    
    bm25_recs = logic.calculate_bm25_recommendations(
        request.target_text,
        request.competitors,
        request.keywords,  # <-- ИЗМЕНЕНИЕ ЗДЕСЬ (было key_words_unigrams)
        request.language
    )
    
    bert_results = logic.perform_bert_analysis(
        request.target_text,
        request.competitors,  # <-- ДОБАВИЛИ ЭТОТ АРГУМЕНТ
        key_phrases, 
        request.language
    )

    return AnalysisResponse(
        ngram_stats=ngram_stats_result,
        bm25_recommendations=bm25_recs,
        bert_analysis=bert_results
    )

if __name__ == "__main__":
    uvicorn.run("main:app", host="127.0.0.1", port=8001, reload=True)