Merge pull request #80 from miguelgfierro/fastapi

Fastapi

Author: miguelgfierro

.github/workflows/pr_gate.yml

@@ -48,8 +48,8 @@ jobs:
       fail-fast: false
       max-parallel: 20 # Usage limits: https://docs.github.com/en/actions/learn-github-actions/usage-limits-billing-and-administration
       matrix:
-        os: [macos-latest, ubuntu-20.04, ubuntu-22.04] # Available images: https://github.com/actions/runner-images/#available-images
-        python: ["3.7", "3.8", "3.9", "3.10"]
+        os: [ubuntu-22.04, ubuntu-24.04] # Available images: https://github.com/actions/runner-images/#available-images
+        python: ["3.8", "3.9", "3.10", "3.11"]
     steps:
     - name: Checkout
       uses: actions/checkout@v3 # Info: https://github.com/actions/checkout

api/fastapi_pydantic.py

@@ -0,0 +1,36 @@
+from fastapi import FastAPI
+from pydantic import BaseModel, EmailStr
+from typing import Optional
+
+# Run the app with: uvicorn fastapi_pydantic:app --reload
+
+# Initialize FastAPI app
+app = FastAPI()
+
+
+# Define a Pydantic model for user data
+class User(BaseModel):
+    name: str
+    email: EmailStr
+    age: Optional[int] = None  # Optional field with default None
+    is_active: bool = True  # Default value
+
+
+# Create an API endpoint to receive and validate user data
+@app.post("/users/")
+async def create_user(user: User):
+    """
+    Pydantic automatically validates the input data
+    and raises an error if the data does not conform to the User model.
+
+    Examples:
+        >>> from fastapi.testclient import TestClient
+        >>> from pybase.api.fastapi_pydantic import app
+        >>> client = TestClient(app)
+        >>> resp = client.post("/users/", json={"name":"Joe","email":"joe@example.com"})
+        >>> resp.status_code
+        200
+        >>> resp.json()["user_data"]["name"]
+        'Joe'
+    """
+    return {"message": "User created successfully", "user_data": user.model_dump()}

image_base/video_utils.py

@@ -1,9 +1,7 @@
 import os
 import cv2
-from moviepy.video.io.ffmpeg_tools import (
-    ffmpeg_extract_subclip,
-    ffmpeg_movie_from_frames,
-)
+from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip
+from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
 
 
 def cut_video(input_file, start_time, end_time, output_file):
@@ -87,4 +85,9 @@ def video_from_frames_ffmpeg(filename, folder, fps=25, **kwargs):
             raise TypeError(
                 "Unexpected keyword argument passed to optimizer: " + str(k)
             )
-    ffmpeg_movie_from_frames(filename, folder, fps, kwargs)
+    # Use ImageSequenceClip to assemble frames into a video
+    images = sorted(os.listdir(folder))
+    paths = [os.path.join(folder, img) for img in images]
+    clip = ImageSequenceClip(paths, fps=fps)
+    output_path = os.path.join(folder, filename)
+    clip.write_videofile(output_path, bitrate=kwargs.get("bitrate", None))

requirements.txt

@@ -1,6 +1,6 @@
-numpy>=1.19
+numpy>=1.19,<2
 memory_profiler>=0.47
-pandas>1.0.3,<2
+pandas>=1.5,<2
 scikit-image>=0.14.2
 scikit-learn>=0.22.1
 scipy>=0.19.0
@@ -13,7 +13,7 @@ beautifulsoup4>=4.6.0
 cherrypy>=12.0.1
 paste>=2.0.3
 fastparquet>=0.1.4
-dask>=0.17.1
+dask[dataframe]>=0.17.1,<3
 pillow>=4.3.0
 sqlalchemy>=1.2.4,<2
 gitpython>=2.1.8
@@ -38,4 +38,8 @@ moviepy>=1.0.1
 numba>=0.38.1,<1
 pyodbc>=4.0.21
 mlflow>=1.14.0
-notebook>=6.4.12,<7.1.0
+notebook>=6.4.12,<7.1.0
+fastapi>=0.100.0,<1
+pydantic>=2.5.0,<3
+email-validator>=1.2.0
+httpx>=0.23.0

system/memory.py

@@ -17,14 +17,14 @@ def _manage_memory_units(data_in_bytes, units):
 
 def get_object_size(obj, units="Mb"):
     """Calculate the size of an object.
-    
+
     Args:
         obj (obj or str or array): Object.
         units (str): Units [bytes, Kb, Mb, Gb]
-    
+
     Returns:
         float: Size of the object.
-    
+
     Examples:
         >>> get_object_size(7, "bytes")
         28
@@ -41,30 +41,31 @@ def get_pandas_df_size(obj, units="Mb"):
     Args:
         obj (pd.DataFrame): Dataframe.
         units (str): Units [bytes, Kb, Mb, Gb]
-    
+
     Returns:
         float: Size of the object.
-    
+
     Examples:
-        >>> df = pd.DataFrame({"a":[1]*100, "b":[0.5]*100}) 
-        >>> get_pandas_df_size(df, "Kb")
-        1.6875
+        >>> import pandas as pd
+        >>> df = pd.DataFrame({"a":[1]*100, "b":[0.5]*100})
+        >>> get_pandas_df_size(df, "Kb")  # doctest: +ELLIPSIS
+        1.6...
     """
-    obj_bytes = obj.memory_usage(deep=True).sum()
+    obj_bytes = obj.memory_usage(deep=False).sum()
     return _manage_memory_units(obj_bytes, units)
 
 
 def get_ram_memory(units="Mb"):
     """Get the RAM memory of the current machine.
-    
+
     Args:
         units (str): Units [bytes, Kb, Mb, Gb]
-    
+
     Returns:
         float: Memory size.
-    
+
     Examples:
-        >>> num = get_ram_memory("Gb") 
+        >>> num = get_ram_memory("Gb")
         >>> num >= 2
         True
     """
@@ -74,82 +75,88 @@ def get_ram_memory(units="Mb"):
 
 def get_total_gpu_memory(units="Mb"):
     """Get the memory of the GPUs in the system
-    
+
     Returns:
         list: List of strings with the GPU memory in Mb
-    
+
     Examples:
         >>> get_total_gpu_memory() #doctest: +SKIP
         [16280.875]
     """
     try:
         import numba
+
         memory_list = []
         for gpu in numba.cuda.gpus:
             with gpu:
                 meminfo = numba.cuda.current_context().get_memory_info()
                 memory_list.append(_manage_memory_units(meminfo[1], units))
         return memory_list
-    except Exception: #numba.cuda.cudadrv.error.CudaSupportError:
+    except Exception:  # numba.cuda.cudadrv.error.CudaSupportError:
         return []
 
 
 def get_free_gpu_memory(units="Mb"):
     """Get the memory of the GPUs in the system
-    
+
     Returns:
         list: List of strings with the GPU memory in Mb
-    
+
     Examples:
         >>> get_free_gpu_memory() #doctest: +SKIP
         [15987.8125]
 
     """
     try:
         import numba
+
         memory_list = []
         for gpu in numba.cuda.gpus:
             with gpu:
                 meminfo = numba.cuda.current_context().get_memory_info()
                 memory_list.append(_manage_memory_units(meminfo[0], units))
         return memory_list
-    except Exception: # numba.cuda.cudadrv.error.CudaSupportError:
+    except Exception:  # numba.cuda.cudadrv.error.CudaSupportError:
         return []
 
 
 def clear_memory_all_gpus():
     """Clear memory of all GPUs.
-    
+
     Examples:
         >>> clear_memory_all_gpus() #doctest: +SKIP
         No CUDA available
 
     """
     try:
         import numba
+
         for gpu in numba.cuda.gpus:
             with gpu:
                 numba.cuda.current_context().deallocations.clear()
-    except Exception: # numba.cuda.cudadrv.errorCudaSupportError:
+    except Exception:  # numba.cuda.cudadrv.errorCudaSupportError:
         print("No CUDA available")
 
 
 def clear_memory_gpu_id(id):
     """Clear memory of all GPUs.
-    
+
     Args:
         id (int): GPU id.
-    
+
     Examples:
         >>> clear_memory_gpu_id(0) #doctest: +SKIP
         No CUDA available
     """
     try:
         import numba
+
         for gpu in numba.cuda.gpus:
             numba.cuda.select_device(gpu.id)
             numba.cuda.close()
-    except Exception: # numba.cuda.cudadrv.error.CudaSupportError:
+    except Exception:  # numba.cuda.cudadrv.error.CudaSupportError:
         print("No CUDA available")
     except IndexError:
-        raise ValueError("GPU id should be between 0 and {}".format(len(numba.cuda.gpus) - 1))
+        raise ValueError(
+            "GPU id should be between 0 and {}".format(len(numba.cuda.gpus) - 1)
+        )

system/system_info.py

@@ -68,8 +68,8 @@ def get_library_version(library_name):
         str: Version of the library.
 
     Examples:
-        >>> get_library_version("pandas") #doctest: +ELLIPSIS
-        '1...'
+        >>> get_library_version("fastapi") #doctest: +ELLIPSIS
+        '0...'
 
     """
     try:
@@ -170,8 +170,9 @@ def get_gpu_name():
     """
     try:
         import numba
+
         return [gpu.name.decode("utf-8") for gpu in numba.cuda.gpus]
-    except Exception: # numba.cuda.cudadrv.error.CudaSupportError:
+    except Exception:  # numba.cuda.cudadrv.error.CudaSupportError:
         return []
 
 
@@ -188,13 +189,15 @@ def get_number_gpus():
     """
     try:
         import torch
+
         return torch.cuda.device_count()
     except (ImportError, ModuleNotFoundError):
         pass
     try:
         import numba
+
         return len(numba.cuda.gpus)
-    except Exception: # numba.cuda.cudadrv.error.CudaSupportError:
+    except Exception:  # numba.cuda.cudadrv.error.CudaSupportError:
         return 0
 
 
@@ -211,8 +214,9 @@ def get_gpu_compute_capability():
     """
     try:
         import numba
+
         return [gpu.compute_capability for gpu in numba.cuda.gpus]
-    except Exception: # numba.cuda.cudadrv.error.CudaSupportError:
+    except Exception:  # numba.cuda.cudadrv.error.CudaSupportError:
         return []
 
 
@@ -225,6 +229,7 @@ def get_cuda_version():
     """
     try:
         import torch
+
         return torch.version.cuda
     except (ImportError, ModuleNotFoundError):
         path = ""
@@ -277,9 +282,10 @@ def find_cudnn_in_headers(candiates):
                 return "Cannot find CUDNN version"
         else:
             return "Cannot find CUDNN version"
-            
+
     try:
         import torch
+
         return torch.backends.cudnn.version()
     except (ImportError, ModuleNotFoundError):
         if sys.platform == "win32":
@@ -316,13 +322,15 @@ def is_cuda_available():
     """
     try:
         import torch
+
         return torch.cuda.is_available()
     except (ImportError, ModuleNotFoundError):
         pass
     try:
         import numba
+
         return len(numba.cuda.gpus)
-    except Exception: # numba.cuda.cudadrv.error.CudaSupportError:
+    except Exception:  # numba.cuda.cudadrv.error.CudaSupportError:
         return False