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from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl
import requests
import face_recognition
import pickle
import cv2
import pyrebase
import os
# Initialize Firebase using Pyrebase
config = {
"apiKey": "AIzaSyClnRJAnrJgAgkYjuYnlvu-CJ6Cxyklebo",
"databaseURL": "https://console.firebase.google.com/project/socioverse-2025/database/socioverse-2025-default-rtdb/data/~2F",
"authDomain": "socioverse-2025.firebaseapp.com",
"projectId": "socioverse-2025",
"storageBucket": "socioverse-2025.appspot.com",
"messagingSenderId": "689574504641",
"appId": "1:689574504641:web:a22f6a2fa343e4221acc40",
"serviceAccount":"socioverse-2025-firebase-adminsdk-gcc6m-6bfb53e6d9.json"
}
firebase = pyrebase.initialize_app(config)
storage = firebase.storage()
# Define the folder containing face images in the Firebase Storage bucket
storage_folder = "Faces/"
app = FastAPI()
class ImgSave(BaseModel):
image_url: HttpUrl
user_name: str
class ImgInput(BaseModel):
image_url: HttpUrl
class ImgOutput(BaseModel):
label: str
def recognize_face(image_url: HttpUrl) -> ImgOutput:
storage.child().download("Faces/pkl/face_encodings.pkl","face_encodings.pkl")
# Downloading image
response = requests.get(image_url)
with open("examp.jpg", 'wb') as file:
file.write(response.content)
# Load the stored face encodings and labels from the pickle file
with open("face_encodings.pkl", "rb") as file:
data = pickle.load(file)
face_encodings = data["encodings"]
labels = data["labels"]
# Load a new image you want to recognize
new_image = cv2.imread("examp.jpg")
new_face_encoding = face_recognition.face_encodings(new_image)
if len(new_face_encoding) == 0:
print("No faces found in the new image.")
else:
# Compare the new face encoding to the stored encodings
results = face_recognition.compare_faces(face_encodings, new_face_encoding[0])
os.remove("examp.jpg")
for i, result in enumerate(results):
if result:
return ImgOutput(label=labels[i])
return ImgOutput(label="unable to detect")
def add_face(image_url: HttpUrl,user_name : str) -> ImgOutput:
# Downloading image
response = requests.get(image_url)
with open("examp.jpg", 'wb') as file:
file.write(response.content)
# Load the stored face encodings and labels from the pickle file
with open("face_encodings.pkl", "rb") as file:
data = pickle.load(file)
face_encodings = data["encodings"]
labels = data["labels"]
# Load a new image you want to recognize
new_image = cv2.imread("examp.jpg")
# Convert the BGR image to RGB
rgb_img = cv2.cvtColor(new_image, cv2.COLOR_BGR2RGB)
encode = face_recognition.face_encodings(new_image)[0]
face_encodings.append(encode)
labels.append(user_name)
# Delete the temporary downloaded image
os.remove("examp.jpg")
# Save the encodings and labels to a pickle file
data = {"encodings": face_encodings, "labels": labels}
with open("face_encodings.pkl", "wb") as file:
pickle.dump(data, file)
# Upload the pickle file to Firebase Storage
pkl_blob = storage.child(f"{storage_folder}pkl/face_encodings.pkl")
pkl_blob.put("face_encodings.pkl")
@app.post('/')
async def scoring_endpoint(item:ImgInput):
result = recognize_face(item.image_url)
return result
@app.post('/user/')
async def scoring_endpoint(item:ImgSave):
add_face(item.image_url, item.user_name)
return ImgOutput(label="User Saved") |