evelynmitchell
1 year ago
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GitHub
commit
aa88b11d3f
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"""
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Idea 2 img
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task -> gpt4 text -> dalle3 img -> gpt4vision img + text analyze img -> dalle3 img -> loop
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"""
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from swarms.models.gpt4_vision_api import GPT4VisionAPI
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"""
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Swarm of multi modal autonomous agents for manufacturing!
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---------------------------------------------------------
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Health Security agent: Agent that monitors the health of working conditions: input image of factory output: health safety index 0.0 - 1.0 being the highest
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Quality Control agent: Agent that monitors the quality of the product: input image of product output: quality index 0.0 - 1.0 being the highest
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Productivity agent: Agent that monitors the productivity of the factory: input image of factory output: productivity index 0.0 - 1.0 being the highest
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Safety agent: Agent that monitors the safety of the factory: input image of factory output: safety index 0.0 - 1.0 being the highest
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Security agent: Agent that monitors the security of the factory: input image of factory output: security index 0.0 - 1.0 being the highest
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Sustainability agent: Agent that monitors the sustainability of the factory: input image of factory output: sustainability index 0.0 - 1.0 being the highest
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Efficiency agent: Agent that monitors the efficiency of the factory: input image of factory output: efficiency index 0.0 - 1.0 being the highest
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Flow:
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health security agent -> quality control agent -> productivity agent -> safety agent -> security agent -> sustainability agent -> efficiency agent
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"""
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from swarms.structs import Flow
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import os
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from dotenv import load_dotenv
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from swarms.models import GPT4VisionAPI
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load_dotenv()
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api_key = os.getenv("OPENAI_API_KEY")
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llm = GPT4VisionAPI(
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openai_api_key=api_key
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)
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assembly_line = "playground/demos/swarm_of_mma_manufacturing/assembly_line.jpg"
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red_robots = "playground/demos/swarm_of_mma_manufacturing/red_robots.jpg"
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robots = "playground/demos/swarm_of_mma_manufacturing/robots.jpg"
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tesla_assembly_line = "playground/demos/swarm_of_mma_manufacturing/tesla_assembly.jpg"
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# Define detailed prompts for each agent
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tasks = {
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"health_safety": (
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"Analyze the factory's working environment for health safety. Focus on"
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" cleanliness, ventilation, spacing between workstations, and personal"
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" protective equipment availability."
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),
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"productivity": (
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"Review the factory's workflow efficiency, machine utilization, and"
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" employee engagement. Identify operational delays or bottlenecks."
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),
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"safety": (
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"Analyze the factory's safety measures, including fire exits, safety"
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" signage, and emergency response equipment."
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),
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"security": (
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"Evaluate the factory's security systems, entry/exit controls, and"
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" potential vulnerabilities."
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),
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"sustainability": (
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"Inspect the factory's sustainability practices, including waste"
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" management, energy usage, and eco-friendly processes."
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),
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"efficiency": (
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"Assess the manufacturing process's efficiency, considering the layout,"
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" logistics, and automation level."
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),
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}
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# Define prompts for each agent
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health_safety_prompt = tasks["health_safety"]
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productivity_prompt = tasks["productivity"]
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safety_prompt = tasks["safety"]
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security_prompt = tasks["security"]
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sustainability_prompt = tasks["sustainability"]
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efficiency_prompt = tasks["efficiency"]
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# Health security agent
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health_security_agent = Flow(
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llm=llm,
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sop_list=health_safety_prompt,
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max_loops=2,
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multi_modal=True
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)
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# Quality control agent
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productivity_check_agent = Flow(
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llm=llm,
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sop=productivity_prompt,
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max_loops=2,
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multi_modal=True
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)
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# Security agent
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security_check_agent = Flow(
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llm=llm,
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sop=security_prompt,
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max_loops=2,
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multi_modal=True
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)
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# Efficiency agent
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efficiency_check_agent = Flow(
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llm=llm,
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sop=efficiency_prompt,
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max_loops=2,
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multi_modal=True
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)
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# Add the first task to the health_security_agent
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health_check = health_security_agent.run(
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"Analyze the safety of this factory",
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robots
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)
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# Add the third task to the productivity_check_agent
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productivity_check = productivity_check_agent.run(
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health_check, assembly_line
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)
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# Add the fourth task to the security_check_agent
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security_check = security_check_agent.add(
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productivity_check, red_robots
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)
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# Add the fifth task to the efficiency_check_agent
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efficiency_check = efficiency_check_agent.run(
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security_check, tesla_assembly_line
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)
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@ -0,0 +1,112 @@
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import os
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import base64
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import requests
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from dotenv import load_dotenv
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from typing import List
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load_dotenv()
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class StableDiffusion:
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"""
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A class to interact with the Stable Diffusion API for image generation.
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Attributes:
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-----------
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api_key : str
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The API key for accessing the Stable Diffusion API.
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api_host : str
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The host URL of the Stable Diffusion API.
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engine_id : str
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The ID of the Stable Diffusion engine.
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headers : dict
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The headers for the API request.
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output_dir : str
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Directory where generated images will be saved.
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Methods:
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--------
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generate_image(prompt: str, cfg_scale: int, height: int, width: int, samples: int, steps: int) -> List[str]:
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Generates images based on a text prompt and returns a list of file paths to the generated images.
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"""
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def __init__(self, api_key: str, api_host: str = "https://api.stability.ai"):
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"""
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Initializes the StableDiffusion class with the provided API key and host.
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Parameters:
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-----------
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api_key : str
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The API key for accessing the Stable Diffusion API.
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api_host : str
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The host URL of the Stable Diffusion API. Default is "https://api.stability.ai".
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"""
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self.api_key = api_key
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self.api_host = api_host
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self.engine_id = "stable-diffusion-v1-6"
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self.headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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"Accept": "application/json"
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}
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self.output_dir = "images"
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os.makedirs(self.output_dir, exist_ok=True)
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def generate_image(self, prompt: str, cfg_scale: int = 7, height: int = 1024, width: int = 1024, samples: int = 1, steps: int = 30) -> List[str]:
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"""
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Generates images based on a text prompt.
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Parameters:
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-----------
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prompt : str
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The text prompt based on which the image will be generated.
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cfg_scale : int
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CFG scale parameter for image generation. Default is 7.
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height : int
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Height of the generated image. Default is 1024.
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width : int
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Width of the generated image. Default is 1024.
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samples : int
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Number of images to generate. Default is 1.
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steps : int
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Number of steps for the generation process. Default is 30.
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Returns:
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--------
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List[str]:
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A list of paths to the generated images.
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Raises:
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-------
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Exception:
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If the API response is not 200 (OK).
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"""
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response = requests.post(
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f"{self.api_host}/v1/generation/{self.engine_id}/text-to-image",
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headers=self.headers,
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json={
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"text_prompts": [{"text": prompt}],
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"cfg_scale": cfg_scale,
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"height": height,
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"width": width,
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"samples": samples,
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"steps": steps,
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},
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)
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if response.status_code != 200:
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raise Exception(f"Non-200 response: {response.text}")
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data = response.json()
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image_paths = []
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for i, image in enumerate(data["artifacts"]):
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image_path = os.path.join(self.output_dir, f"v1_txt2img_{i}.png")
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with open(image_path, "wb") as f:
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f.write(base64.b64decode(image["base64"]))
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image_paths.append(image_path)
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return image_paths
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# Usage example:
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# sd = StableDiffusion("your-api-key")
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# images = sd.generate_image("A scenic landscape with mountains")
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# print(images)
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@ -0,0 +1,30 @@
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import logging
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import os
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import warnings
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def disable_logging():
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warnings.filterwarnings("ignore", category=UserWarning)
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# disable tensorflow warnings
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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# Set the logging level for the entire module
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logging.basicConfig(level=logging.WARNING)
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try:
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log = logging.getLogger("pytorch")
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log.propagate = False
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log.setLevel(logging.ERROR)
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except Exception as error:
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print(f"Pytorch logging not disabled: {error}")
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for logger_name in [
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"tensorflow",
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"h5py",
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"numexpr",
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"git",
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"wandb.docker.auth",
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]:
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logger = logging.getLogger(logger_name)
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logger.setLevel(logging.WARNING) # Supress DEBUG and info logs
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