Create app.py

app.py

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+# app.py
+import base64
+import cv2
+import numpy as np
+import requests
+from fastapi import FastAPI
+from pydantic import BaseModel
+import insightface
+import logging
+
+# ---------------------------
+# Reduce InsightFace logging
+# ---------------------------
+logging.getLogger("insightface").setLevel(logging.ERROR)
+
+# ---------------------------
+# Load face detector + recognition model
+# ---------------------------
+model = insightface.app.FaceAnalysis(name="buffalo_l")
+model.prepare(ctx_id=-1, det_size=(640, 640))  # CPU only, avoid CUDA warnings
+
+# ---------------------------
+# FastAPI app
+# ---------------------------
+app = FastAPI(title="Face Compare API")
+
+# ---------------------------
+# Request schema
+# ---------------------------
+class CompareRequest(BaseModel):
+    image1: str   # base64 string OR URL
+    image2: str   # base64 string OR URL
+
+# ---------------------------
+# Helper: Load image from base64 or URL
+# ---------------------------
+def load_image(input_str):
+    if input_str.startswith("http://") or input_str.startswith("https://"):
+        try:
+            resp = requests.get(input_str, timeout=5)
+            if resp.status_code != 200:
+                return None
+            arr = np.frombuffer(resp.content, np.uint8)
+            img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+            return img
+        except:
+            return None
+    else:
+        try:
+            if "," in input_str:
+                b64 = input_str.split(",", 1)[1]
+            else:
+                b64 = input_str
+            data = base64.b64decode(b64)
+            arr = np.frombuffer(data, np.uint8)
+            img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+            return img
+        except:
+            return None
+
+# ---------------------------
+# Helper: Get face embedding
+# ---------------------------
+def get_embedding(img):
+    try:
+        faces = model.get(img)
+        if len(faces) == 0:
+            return None
+        return faces[0].embedding
+    except:
+        return None
+
+# ---------------------------
+# Helper: Cosine similarity
+# ---------------------------
+def cosine_similarity(a, b):
+    a = np.array(a)
+    b = np.array(b)
+    denom = np.linalg.norm(a) * np.linalg.norm(b)
+    if denom == 0:
+        return 0.0
+    return float(np.dot(a, b) / denom)
+
+# ---------------------------
+# API endpoint
+# ---------------------------
+@app.post("/compare")
+async def compare_faces(req: CompareRequest):
+    img1 = load_image(req.image1)
+    img2 = load_image(req.image2)
+
+    if img1 is None or img2 is None:
+        return {"error": "Cannot load one or both images"}
+
+    emb1 = get_embedding(img1)
+    emb2 = get_embedding(img2)
+
+    if emb1 is None or emb2 is None:
+        return {"error": "No face detected in one or both images"}
+
+    similarity = cosine_similarity(emb1, emb2)
+    threshold = 0.55  # adjust threshold for stricter or looser matching
+    match = similarity >= threshold
+
+    return {"similarity": similarity, "match": match}