Update app/backend/main.py

app/backend/main.py

@@ -1,202 +1,203 @@
-from __future__ import annotations
-
-import json
-import os
-import shutil
-import subprocess
-import sys
-import tempfile
-from pathlib import Path
-from typing import Any, Dict
-
-from fastapi import FastAPI, File, HTTPException, UploadFile
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
-from PIL import Image
-
-# ==========
-# Настройки
-# ==========
-
-
-PROJECT_ROOT = Path(
-    os.getenv("POSTER_PROJECT_ROOT", "/Practise/app")
-).resolve()
-SRC_DIR = PROJECT_ROOT / "src"
-
-if str(SRC_DIR) not in sys.path:
-    sys.path.append(str(SRC_DIR))
-
-
-# =========================
-# Инференс через существующий script
-# =========================
-
-def extract_json_from_stdout(stdout: str) -> Dict[str, Any]:
-    """
-    Некоторые библиотеки и модели печатают служебные логи до JSON.
-    Ищем первый валидный JSON-объект.
-    """
-    stdout = safe_text(stdout).strip()
-    if not stdout:
-        raise RuntimeError("Empty stdout: inference script returned no JSON output.")
-
-    start_idx = stdout.find("{")
-    if start_idx == -1:
-        raise RuntimeError(f"JSON not found in stdout:\n{stdout}")
-
-    candidate = stdout[start_idx:]
-
-    try:
-        return json.loads(candidate)
-    except json.JSONDecodeError:
-        end_idx = candidate.rfind("}")
-        if end_idx == -1:
-            raise RuntimeError(f"JSON closing brace not found:\n{stdout}")
-        candidate = candidate[: end_idx + 1]
-        return json.loads(candidate)
-
-def safe_text(value: Any) -> str:
-    if value is None:
-        return ""
-    return str(value)
-
-def run_model_inference(image_path: Path) -> Dict[str, Any]:
-    script_path = PROJECT_ROOT / "src" / "predict_one_image_with_metrics.py"
-
-    if not script_path.exists():
-        raise RuntimeError(
-            f"Inference script not found: {script_path}\n"
-            f"PROJECT_ROOT={PROJECT_ROOT}"
-        )
-
-    cmd = [
-        sys.executable,
-        str(script_path),
-        str(image_path),
-        "--json",
-    ]
-
-    env = os.environ.copy()
-    env["PYTHONIOENCODING"] = "utf-8"
-
-    result = subprocess.run(
-        cmd,
-        capture_output=True,
-        text=True,
-        encoding="utf-8",
-        cwd=str(PROJECT_ROOT),
-        env=env,
-    )
-
-    stdout_text = safe_text(result.stdout).strip()
-    stderr_text = safe_text(result.stderr).strip()
-
-    if result.returncode != 0:
-        raise RuntimeError(
-            "Inference failed.\n"
-            f"CMD: {' '.join(cmd)}\n"
-            f"CWD: {PROJECT_ROOT}\n"
-            f"RETURN CODE: {result.returncode}\n"
-            f"STDOUT:\n{stdout_text}\n"
-            f"STDERR:\n{stderr_text}"
-        )
-
-    if not stdout_text:
-        raise RuntimeError(
-            "Inference returned success code but empty stdout.\n"
-            f"CMD: {' '.join(cmd)}\n"
-            f"CWD: {PROJECT_ROOT}\n"
-            f"RETURN CODE: {result.returncode}\n"
-            f"STDERR:\n{stderr_text}"
-        )
-
-    data = extract_json_from_stdout(stdout_text)
-
-    if not isinstance(data, dict):
-        raise RuntimeError("Inference returned non-dict JSON.")
-
-    if "ai_detection" not in data:
-        data["ai_detection"] = None
-
-    return data
-
-
-# ==========
-# FastAPI
-# ==========
-
-app = FastAPI(
-    title="Poster Design Analyzer API",
-    version="1.0.0",
-    description="API для анализа постеров и возврата 5 оценок и диагностического анализа."
-)
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.get("/")
-def root() -> Dict[str, Any]:
-    return {
-        "service": "Poster Design Analyzer API",
-        "status": "ok",
-        "docs": "/docs",
-        "health": "/health",
-        "analyze": "/analyze",
-    }
-
-
-@app.get("/health")
-def health() -> Dict[str, str]:
-    return {"status": "ok"}
-
-
-@app.post("/analyze")
-async def analyze(file: UploadFile = File(...)) -> JSONResponse:
-    if not file.filename:
-        raise HTTPException(status_code=400, detail="Файл не передан.")
-
-    suffix = Path(file.filename).suffix.lower()
-    if suffix not in {".jpg", ".jpeg", ".png", ".webp", ".bmp"}:
-        raise HTTPException(status_code=400, detail="Неподдерживаемый формат файла.")
-
-    temp_dir = Path(tempfile.mkdtemp(prefix="poster_analyze_"))
-    temp_path = temp_dir / f"input{suffix}"
-
-    try:
-        contents = await file.read()
-        if not contents:
-            raise HTTPException(status_code=400, detail="Пустой файл.")
-
-        temp_path.write_bytes(contents)
-
-        # Валидация изображения
-        try:
-            with Image.open(temp_path) as img:
-                img.verify()
-        except Exception as e:
-            raise HTTPException(
-                status_code=400,
-                detail=f"Файл не является корректным изображением: {e}"
-            )
-
-        inference_result = run_model_inference(temp_path)
-
-        response = {
-            "filename": file.filename,
-            **inference_result,
-        }
-        return JSONResponse(content=response)
-
-    except HTTPException:
-        raise
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Ошибка инференса: {e}")
-    finally:
+from __future__ import annotations
+
+import json
+import os
+import shutil
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+from typing import Any, Dict
+
+from fastapi import FastAPI, File, HTTPException, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from PIL import Image
+
+# ==========
+# Настройки
+# ==========
+
+
+PROJECT_ROOT = Path(
+    os.getenv("POSTER_PROJECT_ROOT", str(Path(__file__).resolve().parents[1]))
+).resolve()
+
+SRC_DIR = PROJECT_ROOT / "src"
+
+if str(SRC_DIR) not in sys.path:
+    sys.path.append(str(SRC_DIR))
+
+
+# =========================
+# Инференс через существующий script
+# =========================
+
+def extract_json_from_stdout(stdout: str) -> Dict[str, Any]:
+    """
+    Некоторые библиотеки и модели печатают служебные логи до JSON.
+    Ищем первый валидный JSON-объект.
+    """
+    stdout = safe_text(stdout).strip()
+    if not stdout:
+        raise RuntimeError("Empty stdout: inference script returned no JSON output.")
+
+    start_idx = stdout.find("{")
+    if start_idx == -1:
+        raise RuntimeError(f"JSON not found in stdout:\n{stdout}")
+
+    candidate = stdout[start_idx:]
+
+    try:
+        return json.loads(candidate)
+    except json.JSONDecodeError:
+        end_idx = candidate.rfind("}")
+        if end_idx == -1:
+            raise RuntimeError(f"JSON closing brace not found:\n{stdout}")
+        candidate = candidate[: end_idx + 1]
+        return json.loads(candidate)
+
+def safe_text(value: Any) -> str:
+    if value is None:
+        return ""
+    return str(value)
+
+def run_model_inference(image_path: Path) -> Dict[str, Any]:
+    script_path = PROJECT_ROOT / "src" / "predict_one_image_with_metrics.py"
+
+    if not script_path.exists():
+        raise RuntimeError(
+            f"Inference script not found: {script_path}\n"
+            f"PROJECT_ROOT={PROJECT_ROOT}"
+        )
+
+    cmd = [
+        sys.executable,
+        str(script_path),
+        str(image_path),
+        "--json",
+    ]
+
+    env = os.environ.copy()
+    env["PYTHONIOENCODING"] = "utf-8"
+
+    result = subprocess.run(
+        cmd,
+        capture_output=True,
+        text=True,
+        encoding="utf-8",
+        cwd=str(PROJECT_ROOT),
+        env=env,
+    )
+
+    stdout_text = safe_text(result.stdout).strip()
+    stderr_text = safe_text(result.stderr).strip()
+
+    if result.returncode != 0:
+        raise RuntimeError(
+            "Inference failed.\n"
+            f"CMD: {' '.join(cmd)}\n"
+            f"CWD: {PROJECT_ROOT}\n"
+            f"RETURN CODE: {result.returncode}\n"
+            f"STDOUT:\n{stdout_text}\n"
+            f"STDERR:\n{stderr_text}"
+        )
+
+    if not stdout_text:
+        raise RuntimeError(
+            "Inference returned success code but empty stdout.\n"
+            f"CMD: {' '.join(cmd)}\n"
+            f"CWD: {PROJECT_ROOT}\n"
+            f"RETURN CODE: {result.returncode}\n"
+            f"STDERR:\n{stderr_text}"
+        )
+
+    data = extract_json_from_stdout(stdout_text)
+
+    if not isinstance(data, dict):
+        raise RuntimeError("Inference returned non-dict JSON.")
+
+    if "ai_detection" not in data:
+        data["ai_detection"] = None
+
+    return data
+
+
+# ==========
+# FastAPI
+# ==========
+
+app = FastAPI(
+    title="Poster Design Analyzer API",
+    version="1.0.0",
+    description="API для анализа постеров и возврата 5 оценок и диагностического анализа."
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+def root() -> Dict[str, Any]:
+    return {
+        "service": "Poster Design Analyzer API",
+        "status": "ok",
+        "docs": "/docs",
+        "health": "/health",
+        "analyze": "/analyze",
+    }
+
+
+@app.get("/health")
+def health() -> Dict[str, str]:
+    return {"status": "ok"}
+
+
+@app.post("/analyze")
+async def analyze(file: UploadFile = File(...)) -> JSONResponse:
+    if not file.filename:
+        raise HTTPException(status_code=400, detail="Файл не передан.")
+
+    suffix = Path(file.filename).suffix.lower()
+    if suffix not in {".jpg", ".jpeg", ".png", ".webp", ".bmp"}:
+        raise HTTPException(status_code=400, detail="Неподдерживаемый формат файла.")
+
+    temp_dir = Path(tempfile.mkdtemp(prefix="poster_analyze_"))
+    temp_path = temp_dir / f"input{suffix}"
+
+    try:
+        contents = await file.read()
+        if not contents:
+            raise HTTPException(status_code=400, detail="Пустой файл.")
+
+        temp_path.write_bytes(contents)
+
+        # Валидация изображения
+        try:
+            with Image.open(temp_path) as img:
+                img.verify()
+        except Exception as e:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Файл не является корректным изображением: {e}"
+            )
+
+        inference_result = run_model_inference(temp_path)
+
+        response = {
+            "filename": file.filename,
+            **inference_result,
+        }
+        return JSONResponse(content=response)
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Ошибка инференса: {e}")
+    finally:
         shutil.rmtree(temp_dir, ignore_errors=True)