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app (2).py

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+import os
+import pickle
+import tempfile
+import numpy as np
+from numpy.linalg import norm
+
+from fastapi import FastAPI, File, UploadFile
+from deepface import DeepFace
+
+# =====================================================
+# PATH CONFIG (VERY IMPORTANT FOR DOCKER)
+# =====================================================
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+PKL_PATH = os.path.join(BASE_DIR, "student_embeddings.pkl")
+
+MODEL_NAME = "Facenet512"
+THRESHOLD = 0.58
+
+# =====================================================
+# FASTAPI APP
+# =====================================================
+app = FastAPI(
+    title="Face Recognition API",
+    description="DeepFace FaceNet512 based Student Recognition API",
+    version="1.0"
+)
+
+# =====================================================
+# UTILITY FUNCTIONS
+# =====================================================
+def cosine_similarity(a, b):
+    return float(np.dot(a, b) / (norm(a) * norm(b)))
+
+
+def l2_normalize(x):
+    return x / norm(x)
+
+
+# =====================================================
+# LOAD EMBEDDINGS ONCE (SAFE)
+# =====================================================
+if not os.path.exists(PKL_PATH):
+    raise FileNotFoundError(f"❌ student_embeddings.pkl not found at {PKL_PATH}")
+
+with open(PKL_PATH, "rb") as f:
+    db = pickle.load(f)
+
+print(f"✅ Loaded {len(db)} student embeddings")
+
+# =====================================================
+# HEALTH CHECK (RENDER FRIENDLY)
+# =====================================================
+@app.get("/")
+def health():
+    return {
+        "status": "running",
+        "model": MODEL_NAME,
+        "students_loaded": len(db)
+    }
+
+# =====================================================
+# FACE RECOGNITION ENDPOINT
+# =====================================================
+@app.post("/recognize")
+async def recognize_face(file: UploadFile = File(...)):
+    img_path = None
+
+    try:
+        # Save uploaded image to temp file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
+            tmp.write(await file.read())
+            img_path = tmp.name
+
+        # Extract embedding (lazy call)
+        result = DeepFace.represent(
+            img_path=img_path,
+            model_name=MODEL_NAME,
+            enforce_detection=True
+        )
+
+        test_emb = np.array(result[0]["embedding"])
+        test_emb = l2_normalize(test_emb)
+
+        best_match = None
+        best_score = -1.0
+
+        # Compare with database
+        for name, db_emb in db.items():
+            score = cosine_similarity(db_emb, test_emb)
+            if score > best_score:
+                best_score = score
+                best_match = name
+
+        decision = "MATCH" if best_score >= THRESHOLD else "NO MATCH"
+
+        return {
+            "student": best_match,
+            "similarity": round(best_score, 4),
+            "cosine_distance": round(1 - best_score, 4),
+            "decision": decision
+        }
+
+    except Exception as e:
+        return {"error": str(e)}
+
+    finally:
+        # Always clean temp file
+        if img_path and os.path.exists(img_path):
+            os.remove(img_path)

requirements.txt

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+fastapi
+uvicorn
+python-multipart
+
+numpy==1.23.5
+opencv-python-headless==4.8.1.78
+
+tensorflow-cpu==2.13.0
+deepface==0.0.79

student_embeddings (1).pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dd967195b48d5b82cdf86f3ac5178d2eb34dc574eb224488ce57bee6db3f968c
+size 78827

test_api.py

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+import requests
+import sys
+
+# Test the health endpoint first
+print("Testing health endpoint...")
+response = requests.get("http://127.0.0.1:8000/")
+print(f"Status: {response.status_code}")
+print(f"Response: {response.json()}")
+
+# Test face recognition with an image
+if len(sys.argv) > 1:
+    img_path = sys.argv[1]
+    print(f"\nTesting face recognition with: {img_path}")
+    
+    with open(img_path, "rb") as f:
+        files = {"file": f}
+        response = requests.post("http://127.0.0.1:8000/recognize", files=files)
+    
+    print(f"Status: {response.status_code}")
+    print(f"Response: {response.json()}")
+else:
+    print("\nTo test face recognition, provide an image path:")
+    print("python test_api.py <image_path>")