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.gitignore

@@ -0,0 +1,165 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# Database for Project
+/data/
+/images/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+

main.py

@@ -1,89 +1,36 @@
-import os
-from fastapi import FastAPI, UploadFile
-from fastapi.responses import JSONResponse
-from fastapi.param_functions import File
-from fastapi.middleware.cors import CORSMiddleware
+# main.py
+from fastapi import FastAPI
 from typing import List
-import io
-from facenet_pytorch import MTCNN, InceptionResnetV1
-import torch
-from PIL import Image
-
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
+from users.routes import router as users_router
+from auth.route import router as auth_router
+from orders.routes import order_router, meal_router
+
+app = FastAPI(
+    title="SnapFeast API",
+    description="SnapFeast API for managing users, meals, and orders.",
+    version="1.0.0",
+    docs_url="/docs",
+    redoc_url="/redoc",
+    openapi_url="/openapi.json",
 )
-
-# Set the cache directory to a writable location
-os.environ['TORCH_HOME'] = '/tmp/.cache/torch'
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-mtcnn = MTCNN(keep_all=True, device=device)
-resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
+app.include_router(users_router)
+app.include_router(auth_router)
+app.include_router(meal_router)
+app.include_router(order_router)
 
 
 @app.get("/", tags=["Home"])
 def read_root():
-    return {"message": "Welcome to the face embeddings API!"}
+    return {"message": "Welcome to SnapFeast API!"}
+
 
 @app.get("/health", tags=["Health"])
 def health_check():
     return {"status": "ok"}
 
-@app.post("/extract", tags=["Extract Embeddings"])
-async def extract_embeddings(file: UploadFile = File(...)):
-    # Load the image
-    contents = await file.read()
-    image = Image.open(io.BytesIO(contents)).convert('RGB')
-
-    # Detect faces
-    faces = mtcnn(image)
-    
-    # Check if any faces were detected
-    if faces is None:
-        return JSONResponse(content={"error": "No faces detected in the image"})
-    
-    # If faces is a list, take the first face. If it's a tensor, it's already the first (and only) face
-    if isinstance(faces, list):
-        face = faces[0]
-    else:
-        face = faces
-    
-    # Ensure the face tensor is 4D (batch_size, channels, height, width)
-    if face.dim() == 3:
-        face = face.unsqueeze(0)
-    
-    # Extract the face embeddings
-    with torch.no_grad():
-        embeddings = resnet(face).cpu().numpy().tolist()
-
-    return JSONResponse(content={"embeddings": embeddings})
-
-
-# @app.post("/extract")
-# async def extract_embeddings(file: UploadFile = File(...)):
-#     # Load the image
-#     contents = await file.read()
-#     image = face_recognition.load_image_file(io.BytesIO(contents))
-
-#     # Find all the faces in the image
-#     face_locations = face_recognition.face_locations(image)
 
-#     # Initialize an empty list to store the face embeddings
-#     embeddings = []
+if __name__ == "__main__":
+    import uvicorn
 
-#     # Loop through each face location
-#     for face_location in face_locations:
-#         # Extract the face encoding
-#         face_encoding = face_recognition.face_encodings(image, [face_location])[0]
-        
-#         # Append the face encoding to the embeddings list
-#         embeddings.append(face_encoding.tolist())
+    uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")
 
-#     return JSONResponse(content={"embeddings": embeddings})

requirements.txt

@@ -1,9 +1,19 @@
+# requirements.txt
+fastapi
+uvicorn
+sqlalchemy
+pydantic
+pydantic[email]
+pydantic-settings
+passlib
+python-jose[cryptography]
+python-multipart
+opencv-python
 facenet_pytorch
 torch
-torchvision
 numpy
+scikit-learn
 pandas
-Pillow==8.2.0
-fastapi
-uvicorn
-python-multipart
+surprise
+joblib
+psycopg2-binary