FirstFace

.gitignore

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+socioverse-2025-firebase-adminsdk-gcc6m-6bfb53e6d9.json
+venv

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/facial_recog.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (facial_recog)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/facial_recog.iml" filepath="$PROJECT_DIR$/.idea/facial_recog.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

Dockerfile

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+FROM python:3.11.4
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN apt-get update && \
+    apt-get install -y python3 python3-pip libgl1-mesa-glx && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip install --upgrade pip
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+ PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "FastAPI:app", "--host", "0.0.0.0", "--port", "7860"]

FastAPI.py

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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()
+
+
+storage.child().download("Faces/pkl/face_encodings.pkl","face_encodings.pkl")
+
+
+app = FastAPI()
+
+class ImgInput(BaseModel):
+    image_url: HttpUrl
+
+class ImgOutput(BaseModel):
+    label: str
+
+def recognize_face(image_url: HttpUrl) -> 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")
+
+    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")
+
+
+
+@app.post('/')
+async def scoring_endpoint(item:ImgInput):
+    result = recognize_face(item.image_url)
+    return result

Procfile

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+web: gunicorn -w 2 -k uvicorn

encode_pkl.py

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+import os
+import cv2
+import face_recognition
+import pickle
+import pyrebase
+
+# 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/"
+
+# Create an array to store encodings and corresponding labels
+face_encodings = []
+labels = []
+
+# List all files in the Firebase Storage folder
+blobs = storage.child(storage_folder).list_files()
+
+
+for blob in blobs:
+    if blob.name.startswith(storage_folder) and (blob.name.endswith(".jpeg") or blob.name.endswith(".jpg") or blob.name.endswith(".png")):
+        # Download the image to a local file
+        url = storage.child(f"{blob.name}").get_url(None)
+
+        storage.child(url).download(f"{blob.name}","temp.jpeg")
+        # Load the image using OpenCV
+        img = cv2.imread("temp.jpeg")
+
+        # Convert the BGR image to RGB
+        rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+        encode = face_recognition.face_encodings(img)[0]
+        face_encodings.append(encode)
+        name_parts = blob.name.split('/')
+        name = name_parts[-1]
+        name_parts = name.split('$')
+        name = name_parts[0]
+        labels.append(name)
+
+        # Delete the temporary downloaded image
+        os.remove("temp.jpeg")
+
+# 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")
+
+print("Face encodings and labels saved to Firebase Storage in 'faces/pkl' folder as 'face_encodings.pkl'.")

predict.py

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+import face_recognition
+import pickle
+import cv2
+import pyrebase
+
+
+# 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()
+
+
+storage.child().download("Faces/pkl/face_encodings.pkl","face_encodings.pkl")
+
+# 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("download.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])
+
+    for i, result in enumerate(results):
+        if result:
+            print(f"Recognised : {labels[i]}")