Python fastapi.File() Examples

def run_model(model_name: str, input_data: UploadFile = File(...)):
	"""
	Performs a prediction by giving both model name and image file.
	:param model_name: Model name
	:param input_data: An image file
	:return: APIResponse containing the prediction's bounding boxes
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=False, predict_batch=False)
		error_logging.info('request successful;' + str(output))
		return ApiResponse(data=output)
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def predict_image(model_name: str, input_data: UploadFile = File(...)):
	"""
	Draws bounding box(es) on image and returns it.
	:param model_name: Model name
	:param input_data: Image file
	:return: Image file
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=True, predict_batch=False)
		error_logging.info('request successful;' + str(output))
		return FileResponse("/main/result.jpg", media_type="image/jpg")
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def run_model_batch(model_name: str, input_data: List[UploadFile] = File(...)):
	"""
	Performs a prediction by giving both model name and image file(s).
	:param model_name: Model name
	:param input_data: A batch of image files or a single image file
	:return: APIResponse containing prediction(s) bounding boxes
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=False, predict_batch=True)
		error_logging.info('request successful;' + str(output))
		return ApiResponse(data=output)
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		print(e)
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def run_model(model_name: str, input_data: UploadFile = File(...)):
	"""
	Performs a prediction by giving both model name and image file.
	:param model_name: Model name
	:param input_data: An image file
	:return: APIResponse containing the prediction's bounding boxes
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=False, predict_batch=False)
		error_logging.info('request successful;' + str(output))
		return ApiResponse(data=output)
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def detect_custom(model: str = Form(...), image: UploadFile = File(...)):
	"""
	Performs a prediction for a specified image using one of the available models.
	:param model: Model name or model hash
	:param image: Image file
	:return: Model's Bounding boxes
	"""
	draw_boxes = False
	predict_batch = False
	try:
		output = await dl_service.run_model(model, image, draw_boxes, predict_batch)
		error_logging.info('request successful;' + str(output))
		return output
	except ApplicationError as e:
		error_logging.warning(model + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def predict_image(model_name: str, input_data: UploadFile = File(...)):
	"""
	Draws bounding box(es) on image and returns it.
	:param model_name: Model name
	:param input_data: Image file
	:return: Image file
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=True, predict_batch=False)
		error_logging.info('request successful;' + str(output))
		return FileResponse("/main/result.jpg", media_type="image/jpg")
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def run_model_batch(model_name: str, input_data: List[UploadFile] = File(...)):
	"""
	Performs a prediction by giving both model name and image file(s).
	:param model_name: Model name
	:param input_data: A batch of image files or a single image file
	:return: APIResponse containing prediction(s) bounding boxes
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=False, predict_batch=True)
		error_logging.info('request successful;' + str(output))
		return ApiResponse(data=output)
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		print(e)
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def run_model(model_name: str, input_data: UploadFile = File(...)):
	"""
	Performs a prediction by giving both model name and image file.
	:param model_name: Model name
	:param input_data: An image file
	:return: APIResponse containing the prediction's bounding boxes
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=False, predict_batch=False)
		error_logging.info('request successful;' + str(output))
		return ApiResponse(data=output)
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def detect_custom(model: str = Form(...), image: UploadFile = File(...)):
	"""
	Performs a prediction for a specified image using one of the available models.
	:param model: Model name or model hash
	:param image: Image file
	:return: Model's Bounding boxes
	"""
	draw_boxes = False
	predict_batch = False
	try:
		output = await dl_service.run_model(model, image, draw_boxes, predict_batch)
		error_logging.info('request successful;' + str(output))
		return output
	except ApplicationError as e:
		error_logging.warning(model + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def run_model(model_name: str, input_data: UploadFile = File(...)):
	"""
	Draws bounding box(es) on image and returns it.
	:param model_name: Model name
	:param input_data: Image file
	:return: Image file
	"""
	draw_boxes = True
	predict_batch = False
	try:
		output = await dl_service.run_model(model_name, input_data, draw_boxes, predict_batch)
		error_logging.info('request successful;' + str(output))
		return FileResponse("/main/result.jpg", media_type="image/jpg")
	except ApplicationError as e:
		error_logging.warning(model_name+';'+str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model_name+' '+str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def run_model_batch(model_name: str, input_data: List[UploadFile] = File(...)):
	"""
	Performs a prediction by giving both model name and image file(s).
	:param model_name: Model name
	:param input_data: A batch of image files or a single image file
	:return: APIResponse containing prediction(s) bounding boxes
	"""
	draw_boxes = False
	predict_batch = True
	try:
		output = await dl_service.run_model(model_name, input_data, draw_boxes, predict_batch)
		error_logging.info('request successful;' + str(output))
		return ApiResponse(data=output)
	except ApplicationError as e:
		error_logging.warning(model_name+';'+str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		print(e)
		error_logging.error(model_name+' '+str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def run_model(model_name: str, input_data: UploadFile = File(...)):
	"""
	Performs a prediction by giving both model name and image file.
	:param model_name: Model name
	:param input_data: An image file
	:return: APIResponse containing the prediction's bounding boxes
	"""
	draw_boxes = False
	predict_batch = False
	try:
		output = await dl_service.run_model(model_name, input_data, draw_boxes, predict_batch)
		error_logging.info('request successful;' + str(output))
		return ApiResponse(data=output)
	except ApplicationError as e:
		error_logging.warning(model_name+';'+str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model_name+' '+str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def detect_robotron(request: Request, background_tasks: BackgroundTasks, model: str = Form(...), image: UploadFile = File(...)):
	"""
	Performs a prediction for a specified image using one of the available models.
	:param request: Used if background tasks was enabled
	:param background_tasks: Used if background tasks was enabled
	:param model: Model name or model hash
	:param image: Image file
	:return: Model's Bounding boxes
	"""
	draw_boxes = False
	predict_batch = False
	try:
		request_start = time.time()
		output = await dl_service.run_model(model, image, draw_boxes, predict_batch)
		# background_tasks.add_task(metrics_collector,'detect',image, output, request, request_start)
		error_logging.info('request successful;' + str(output))
		return output
	except ApplicationError as e:
		error_logging.warning(model+';'+str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model+' '+str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def create_site_template(
    db: Session = Depends(get_db),
    name: str = Form(...),
    zip_file: UploadFile = File(..., alias='zipFile'),
    remark: Union[str, None] = Form(None),
):
    site_template_profile = dict(
        name=name,
        remark=remark,
        zip_file_name=zip_file.filename,
        zip_file_content=await zip_file.read()
    )
    created_data = crud_site_template.create_site_template(
        db, site_template_profile
    )
    return dict(result=created_data) 

def predict_image(model_name: str, input_data: UploadFile = File(...)):
	"""
	Draws bounding box(es) on image and returns it.
	:param model_name: Model name
	:param input_data: Image file
	:return: Image file
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=True, predict_batch=False)
		error_logging.info('request successful;' + str(output))
		return FileResponse("/main/result.jpg", media_type="image/jpg")
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def run_model_batch(model_name: str, input_data: List[UploadFile] = File(...)):
	"""
	Performs a prediction by giving both model name and image file(s).
	:param model_name: Model name
	:param input_data: A batch of image files or a single image file
	:return: APIResponse containing prediction(s) bounding boxes
	"""
	try:
		output = await dl_service.run_model(model_name, input_data, draw=False, predict_batch=True)
		error_logging.info('request successful;' + str(output))
		return ApiResponse(data=output)
	except ApplicationError as e:
		error_logging.warning(model_name + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		print(e)
		error_logging.error(model_name + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def detect_custom(model: str = Form(...), image: UploadFile = File(...)):
	"""
	Performs a prediction for a specified image using one of the available models.
	:param model: Model name or model hash
	:param image: Image file
	:return: Model's Bounding boxes
	"""
	draw_boxes = False
	predict_batch = False
	try:
		output = await dl_service.run_model(model, image, draw_boxes, predict_batch)
		error_logging.info('request successful;' + str(output))
		return output
	except ApplicationError as e:
		error_logging.warning(model + ';' + str(e))
		return ApiResponse(success=False, error=e)
	except Exception as e:
		error_logging.error(model + ' ' + str(e))
		return ApiResponse(success=False, error='unexpected server error') 

def config_validator(
    data: fastapi.UploadFile = fastapi.File(...),
):  # pragma: no cover
    try:
        rules.UserConfigurationSchema(await data.read())
    except Exception as e:
        status = 400
        message = str(e)
    else:
        status = 200
        message = "The configuration is valid"

    return responses.PlainTextResponse(message, status_code=status) 

def upload_site_template_file(
    db: Session = Depends(get_db),
    *,
    site_template_id: int,
    zip_file: UploadFile = File(..., alias='zipFile'),
):
    update_result = crud_site_template.update_site_template(
        db_session=db,
        template_id=site_template_id,
        zip_file_name=zip_file.filename,
        zip_file_content=await zip_file.read()
    )
    return dict(result=bool(update_result)) 

def create_c2_profile(
    db: Session = Depends(get_db),
    name: str = Form(...),
    profile: UploadFile = File(...),
    remark: str = Form(None),
):
    c2_profile_obj = C2ProfileCreate(
        name=name,
        remark=remark,
        profile_name=profile.filename,
        profile_content=await profile.read()
    )
    created_data = crud_c2.create(db, obj_in=c2_profile_obj)
    return dict(result=created_data) 

def process_pdf(file: UploadFile = File(None)):
    global sectlabel_model
    if sectlabel_model is None:
        sectlabel_model = SectLabel()

    file_handle = file.file
    file_name = file.filename
    file_contents = file_handle.read()

    pdf_save_location = pdf_store.save_pdf_binary_string(
        pdf_string=file_contents, out_filename=file_name
    )

    # noinspection PyTypeChecker
    pdf_reader = PdfReader(filepath=pdf_save_location)

    # read pdf lines
    lines = pdf_reader.read_pdf()
    all_labels = []
    all_lines = []

    for batch_lines in chunks(lines, 64):
        labels = sectlabel_model.predict_for_text_batch(texts=batch_lines)
        all_labels.append(labels)
        all_lines.append(batch_lines)

    all_lines = itertools.chain.from_iterable(all_lines)
    all_lines = list(all_lines)

    all_labels = itertools.chain.from_iterable(all_labels)
    all_labels = list(all_labels)

    response_tuples = []
    for line, label in zip(all_lines, all_labels):
        response_tuples.append((line, label))

    # remove the saved pdf
    pdf_store.delete_file(str(pdf_save_location))

    return {"labels": response_tuples} 

def create_file(
    file: bytes = File(...), fileb: UploadFile = File(...), token: str = Form(...)
):
    return {
        "file_size": len(file),
        "token": token,
        "fileb_content_type": fileb.content_type,
    } 

def create_upload_file(file: UploadFile = File(...)):
    return {"filename": file.filename} 

def create_file(file: bytes = File(...)):
    return {"file_size": len(file)} 

def create_upload_files(files: List[UploadFile] = File(...)):
    return {"filenames": [file.filename for file in files]} 

def create_files(files: List[bytes] = File(...)):
    return {"file_sizes": [len(file) for file in files]} 

def get_prediction(
    image: bytes = File(..., description="Image to perform inference on")
):
    """Runs the last saved weights to infer on the given image"""

    prediction_path: Path = working_dir / "predictions"
    training_path: Path = trainn_dir
    weights_path: Path = training_path / "weights"
    last_weights: list = list(weights_path.glob("*_last.weights"))

    if not last_weights:
        result: dict = {
            "success": True,
            "start_time": get_time(),
            "message": "No predictions yet",
        }
        return result

    if not prediction_path.exists():
        # Create folder in working directory symlinked to darknet/data/labels because it is needed by darknet to label the bounding boxes
        Path.mkdir(prediction_path)
        os.chdir(prediction_path)
        os.mkdir(Path("data"))
        os.symlink(
            working_dir / "darknet/data/labels", working_dir / "predictions/data/labels"
        )
    try:
        img: Image = Image.open(BytesIO(image)).convert("RGB")
        img.save("image.jpg")
        config_file_path: Path = training_path / "config"
        data_path: str = str(list(config_file_path.glob("*.data"))[0])
        cfg_path: str = str(list(config_file_path.glob("*.cfg"))[0])
        last_weights: str = str(last_weights[0])
        darknet_exec_path: Path = working_dir / "darknet/darknet"
        command: list = [
            darknet_exec_path,
            "detector",
            "test",
            data_path,
            cfg_path,
            last_weights,
            "-dont_show",
        ]
        command.append(str(working_dir / "predictions/image.jpg"))

        with open(os.devnull, "w") as DEVNULL:
            subprocess.call(command, stdout=DEVNULL, stderr=DEVNULL)

    except Exception as ex:
        raise HTTPException(
            422,
            detail="Error while reading request image. Please make sure it is a valid image {}".format(
                str(ex)
            ),
        )

    return FileResponse("predictions.jpg", media_type="image/jpg")