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import os
import hydra
from flows.flow_launchers import FlowLauncher
from flows.backends.api_info import ApiInfo
from flows.utils.general_helpers import read_yaml_file
from flows import logging
from flows.flow_cache import CACHING_PARAMETERS, clear_cache
CACHING_PARAMETERS.do_caching = False # Set to True in order to disable caching
# clear_cache() # Uncomment this line to clear the cache
logging.set_verbosity_debug() # Uncomment this line to see verbose logs
from flows import flow_verse
dependencies = [
{"url": "aiflows/VisionFlowModule", "revision": os.getcwd()},
]
flow_verse.sync_dependencies(dependencies)
if __name__ == "__main__":
# ~~~ Set the API information ~~~
# OpenAI backend
api_information = [ApiInfo(backend_used="openai",
api_key = os.getenv("OPENAI_API_KEY"))]
# # Azure backend
# api_information = ApiInfo(backend_used = "azure",
# api_base = os.getenv("AZURE_API_BASE"),
# api_key = os.getenv("AZURE_OPENAI_KEY"),
# api_version = os.getenv("AZURE_API_VERSION") )
root_dir = "."
cfg_path = os.path.join(root_dir, "demo.yaml")
cfg = read_yaml_file(cfg_path)
cfg["flow"]["backend"]["api_infos"] = api_information
# ~~~ Instantiate the Flow ~~~
flow_with_interfaces = {
"flow": hydra.utils.instantiate(cfg['flow'], _recursive_=False, _convert_="partial"),
"input_interface": (
None
if cfg.get( "input_interface", None) is None
else hydra.utils.instantiate(cfg['input_interface'], _recursive_=False)
),
"output_interface": (
None
if cfg.get( "output_interface", None) is None
else hydra.utils.instantiate(cfg['output_interface'], _recursive_=False)
),
}
url_image = {"type": "url",
"image": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"}
local_image = {"type": "local_path", "image": "PATH TO YOUR LOCAL IMAGE"}
video = {"video_path": "PATH TO YOUR LOCAL VIDEO", "resize": 768, "frame_step_size": 30, "start_frame": 0, "end_frame": None }
# ~~~ Get the data ~~~
## FOR SINGLE IMAGE
data = {"id": 0, "query": "What’s in this image?", "data": {"images": [url_image]}} # This can be a list of samples
## FOR MULTIPLE IMAGES
# data = {"id": 0, "question": "What are in these images? Is there any difference between them?", "data": {"images": [url_image,local_image]}} # This can be a list of samples
## FOR VIDEO
# data = {"id": 0,
# "question": "These are frames from a video that I want to upload. Generate a compelling description that I can upload along with the video.",
# "data": {"video": video}} # This can be a list of samples
# ~~~ Run inference ~~~
path_to_output_file = None
# path_to_output_file = "output.jsonl" # Uncomment this line to save the output to disk
_, outputs = FlowLauncher.launch(
flow_with_interfaces=flow_with_interfaces,
data=data,
path_to_output_file=path_to_output_file
)
# ~~~ Print the output ~~~
flow_output_data = outputs[0]
print(flow_output_data)
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