Update app.py

app.py

@@ -1,4 +1,4 @@
-# -*- coding:UTF-8 -*-
+# -*- coding: UTF-8 -*-
 from fastapi import FastAPI, UploadFile, File, HTTPException
 from fastapi.responses import Response
 from fastapi.middleware.cors import CORSMiddleware
@@ -7,66 +7,72 @@ import cv2
 import numpy as np
 from PIL import Image
 import os
-import logging
 import requests
 from pathlib import Path
 import uvicorn
+import logging
 
-# Initialize FastAPI
+# Initialize FastAPI with explicit docs settings
 app = FastAPI(
     title="Face Swap API",
     description="API for swapping faces in images.",
-    docs_url="/docs",
-    redoc_url="/redoc",
+    docs_url="/docs",  # Explicitly set docs URL
+    redoc_url="/redoc",  # Explicitly set redoc URL
 )
 
-# Logging setup
+# Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# CORS setup
+# Add CORS middleware
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # Update with your domain in production
+    allow_origins=["*"],  # Update with your Framer domain in production
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-# Health check route
-@app.get("/")
-async def root():
-    return {"message": "Face Swap API is running. Use /docs to test the API."}
-
+# Health Check Endpoint
 @app.get("/health")
 async def health_check():
     return {"status": "healthy"}
 
-# Prevent multiple downloads
-MODEL_PATH = Path("models/inswapper_128.onnx")
-MODEL_URL = "https://huggingface.co/ezioruan/inswapper_128.onnx/resolve/main/inswapper_128.onnx"
+# Global flag to prevent multiple downloads
+MODEL_DOWNLOADED = False
 
 def download_model():
-    if MODEL_PATH.exists():
-        logger.info("Model already exists, skipping download.")
+    global MODEL_DOWNLOADED
+    if MODEL_DOWNLOADED:
+        logger.info("Model already downloaded, skipping.")
         return
-    logger.info("Downloading model...")
-    MODEL_PATH.parent.mkdir(exist_ok=True)
-    try:
-        response = requests.get(MODEL_URL, stream=True, timeout=30)
-        response.raise_for_status()
-        with open(MODEL_PATH, 'wb') as f:
-            for chunk in response.iter_content(chunk_size=8192):
-                f.write(chunk)
-        logger.info("Model downloaded successfully.")
-    except Exception as e:
-        logger.error(f"Failed to download model: {e}")
-        raise RuntimeError("Could not download inswapper_128.onnx.")
 
-# FastAPI startup event
+    model_dir = Path("models")
+    model_path = model_dir / "inswapper_128.onnx"
+    model_url = "https://huggingface.co/ezioruan/inswapper_128.onnx/resolve/main/inswapper_128.onnx"
+
+    if not model_path.exists():
+        logger.info("Model not found. Downloading inswapper_128.onnx...")
+        model_dir.mkdir(exist_ok=True)
+        try:
+            response = requests.get(model_url, stream=True, timeout=30)
+            response.raise_for_status()
+            with open(model_path, 'wb') as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    f.write(chunk)
+            logger.info("Model downloaded successfully.")
+            MODEL_DOWNLOADED = True
+        except Exception as e:
+            logger.error(f"Failed to download model: {e}")
+            raise RuntimeError("Could not download inswapper_128.onnx. Please check logs.")
+    else:
+        logger.info("Model already exists at: %s", model_path)
+        MODEL_DOWNLOADED = True
+
+# Use lifespan event handler
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    logger.info("Starting application...")
+    logger.info("Starting up application...")
     try:
         download_model()
         logger.info("Startup completed successfully.")
@@ -78,19 +84,10 @@ async def lifespan(app: FastAPI):
 
 app.lifespan = lifespan
 
-# Face detection and swap functions
-def get_faces(image):
-    try:
-        from insightface.app import FaceAnalysis
-        app = FaceAnalysis(name="buffalo_l")
-        app.prepare(ctx_id=0, det_size=(640, 640))
-        return app.get(image) or []
-    except Exception as e:
-        logger.error(f"Face detection failed: {e}")
-        raise
-
 def swap_faces(source_img, target_img):
+    """Perform face swapping using insightface and inswapper model."""
     try:
+        from insightface.app import FaceAnalysis
         from insightface.utils import face_align
         from insightface.model_zoo import face_swapper
 
@@ -101,14 +98,25 @@ def swap_faces(source_img, target_img):
         target_faces = face_analyzer.get(target_img)
 
         if not source_faces or not target_faces:
-            raise ValueError("No faces detected.")
+            raise ValueError("No faces detected in one or both images.")
         if len(source_faces) > 1 or len(target_faces) > 1:
-            raise ValueError("Multiple faces detected. Only one face per image is supported.")
+            raise ValueError("Multiple faces detected; only one face per image is supported.")
+
+        source_face = source_faces[0]
+        target_face = target_faces[0]
+
+        model_path = Path("models/inswapper_128.onnx")
+        if not model_path.exists():
+            raise FileNotFoundError("Model file inswapper_128.onnx not found.")
+        swapper = face_swapper.FaceSwapper(model_path)
 
-        swapper = face_swapper.FaceSwapper(MODEL_PATH)
-        result = swapper.get(target_img, target_faces[0], source_faces[0], paste_back=True)
+        result = swapper.get(target_img, target_face, source_face, paste_back=True)
 
-        return cv2.cvtColor(np.array(Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))), cv2.COLOR_RGB2BGR)
+        target_pil = Image.fromarray(cv2.cvtColor(target_img, cv2.COLOR_BGR2RGB))
+        result_pil = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
+        result_pil = result_pil.resize(target_pil.size, Image.Resampling.LANCZOS)
+
+        return cv2.cvtColor(np.array(result_pil), cv2.COLOR_RGB2BGR)
     except Exception as e:
         logger.error(f"Face swap failed: {e}")
         raise
@@ -120,20 +128,24 @@ async def swap_face(source_file: UploadFile = File(...), target_file: UploadFile
         target_path = "temp_target.jpg"
         output_path = "output.jpg"
 
+        source_content = await source_file.read()
         with open(source_path, "wb") as f:
-            f.write(await source_file.read())
-        with open(target_path, "wb") as f:
-            f.write(await target_file.read())
-
+            f.write(source_content)
         source_img = cv2.imread(source_path)
-        target_img = cv2.imread(target_path)
+        if source_img is None:
+            raise ValueError("Failed to load source image.")
 
-        if source_img is None or target_img is None:
-            raise ValueError("Invalid images provided.")
+        target_content = await target_file.read()
+        with open(target_path, "wb") as f:
+            f.write(target_content)
+        target_img = cv2.imread(target_path)
+        if target_img is None:
+            raise ValueError("Failed to load target image.")
 
         result_img = swap_faces(source_img, target_img)
 
         cv2.imwrite(output_path, result_img)
+
         with open(output_path, "rb") as f:
             image_data = f.read()