run.py

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)