Create app.py

app.py

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+import os
+import base64
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+import cv2
+import numpy as np
+from huggingface_hub import hf_hub_download
+from realesrgan import RealESRGAN
+from mediapipe import solutions as mp_solutions
+
+# ✅ Cache dir for Hugging Face
+CACHE_DIR = "/tmp/hf_cache"
+os.environ["HF_HOME"] = CACHE_DIR
+os.makedirs(CACHE_DIR, exist_ok=True)
+
+app = FastAPI(title="Face Beautification API")
+
+# ✅ Load Mediapipe face detector
+mp_face = mp_solutions.face_detection.FaceDetection(
+    model_selection=1, min_detection_confidence=0.5
+)
+
+# ✅ Download ESRGAN weights from Hugging Face (free)
+model_path = hf_hub_download(
+    repo_id="eugenesiow/real-esrgan",
+    filename="RealESRGAN_x4plus.pth",
+    cache_dir=CACHE_DIR
+)
+
+# ✅ Load ESRGAN model
+device = "cuda" if cv2.cuda.getCudaEnabledDeviceCount() > 0 else "cpu"
+model = RealESRGAN(device, scale=4)
+model.load_weights(model_path)
+
+@app.get("/")
+async def root():
+    return {"message": "Free Face Beautification API is running!"}
+
+@app.post("/beautify")
+async def beautify(image: UploadFile = File(...)):
+    try:
+        # Read image
+        contents = await image.read()
+        npimg = np.frombuffer(contents, np.uint8)
+        img = cv2.imdecode(npimg, cv2.IMREAD_COLOR)
+
+        # Detect faces
+        results = mp_face.process(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+        if not results.detections:
+            return JSONResponse({"error": "No face detected"}, status_code=400)
+
+        for detection in results.detections:
+            bbox = detection.location_data.relative_bounding_box
+            h, w, _ = img.shape
+            x1 = int(bbox.xmin * w)
+            y1 = int(bbox.ymin * h)
+            x2 = int((bbox.xmin + bbox.width) * w)
+            y2 = int((bbox.ymin + bbox.height) * h)
+            face = img[y1:y2, x1:x2]
+
+            # Beautify (enhance + smooth)
+            face_upscaled = model.predict(face)
+            face_smooth = cv2.bilateralFilter(face_upscaled, 9, 75, 75)
+
+            # Blend face back
+            face_smooth = cv2.resize(face_smooth, (x2 - x1, y2 - y1))
+            img[y1:y2, x1:x2] = face_smooth
+
+        # Encode final image as Base64
+        _, buffer = cv2.imencode(".jpg", img)
+        img_base64 = base64.b64encode(buffer).decode("utf-8")
+
+        return JSONResponse({
+            "status": "success",
+            "message": "Beautification complete!",
+            "image_base64": img_base64
+        })
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=500)
+
+@app.get("/health")
+async def health():
+    return {"ready": True}