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133 lines
3.2 KiB
133 lines
3.2 KiB
"""
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Todo
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- Add more data in RAG for hydroponic based solutions with images and very detailed captions
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- Introduce JSON function calling for the diagnoser -> good / bad -> if bad then disease detecter agent
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- List of common desases -> if agent picks one of those diseases -> select another of available treatments
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- Fix error choice
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"""
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import os
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from dotenv import load_dotenv
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from examples.demos.plant_biologist_swarm.prompts import (
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diagnoser_agent,
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disease_detector_agent,
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growth_predictor_agent,
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harvester_agent,
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treatment_recommender_agent,
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)
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from swarms import Agent
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from swarms.models.gpt_o import GPT4VisionAPI
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# Load the OpenAI API key from the .env file
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load_dotenv()
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# Initialize the OpenAI API key
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api_key = os.environ.get("OPENAI_API_KEY")
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# llm = llm,
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llm = GPT4VisionAPI(
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max_tokens=3000, openai_api_key=os.getenv("OPENAI_API_KEY")
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)
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# Initialize Diagnoser Agent
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diagnoser_agent = Agent(
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agent_name="Diagnoser Agent",
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system_prompt=diagnoser_agent(),
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llm=llm,
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max_loops=1,
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dashboard=False,
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# streaming_on=True,
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# verbose=True,
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# saved_state_path="diagnoser.json",
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multi_modal=True,
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autosave=True,
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streaming_on=True,
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)
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# Initialize Harvester Agent
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harvester_agent = Agent(
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agent_name="Harvester Agent",
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system_prompt=harvester_agent(),
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llm=llm,
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max_loops=1,
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dashboard=False,
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# streaming_on=True,
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# verbose=True,
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# saved_state_path="harvester.json",
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multi_modal=True,
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autosave=True,
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streaming_on=True,
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)
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# Initialize Growth Predictor Agent
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growth_predictor_agent = Agent(
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agent_name="Growth Predictor Agent",
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system_prompt=growth_predictor_agent(),
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llm=llm,
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max_loops=1,
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dashboard=False,
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# streaming_on=True,
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# verbose=True,
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# saved_state_path="growth_predictor.json",
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multi_modal=True,
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autosave=True,
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streaming_on=True,
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)
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# Initialize Treatment Recommender Agent
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treatment_recommender_agent = Agent(
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agent_name="Treatment Recommender Agent",
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system_prompt=treatment_recommender_agent(),
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llm=llm,
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max_loops=1,
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dashboard=False,
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# streaming_on=True,
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# verbose=True,
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# saved_state_path="treatment_recommender.json",
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multi_modal=True,
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autosave=True,
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streaming_on=True,
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)
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# Initialize Disease Detector Agent
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disease_detector_agent = Agent(
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agent_name="Disease Detector Agent",
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system_prompt=disease_detector_agent(),
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llm=llm,
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max_loops=1,
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dashboard=False,
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# streaming_on=True,
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# verbose=True,
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# saved_state_path="disease_detector.json",
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multi_modal=True,
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autosave=True,
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streaming_on=True,
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)
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agents = [
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diagnoser_agent,
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disease_detector_agent,
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treatment_recommender_agent,
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growth_predictor_agent,
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harvester_agent,
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]
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task = "Conduct a diagnosis on the plants's symptoms, this wasn't grown in dirt, it grew from hydroponics"
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img = "bad_tomato.jpg"
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loop = 0
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for i in range(len(agents)):
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if i == 0:
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output = agents[i].run(task, img)
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print(output)
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else:
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output = agents[i].run(output, img)
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print(output)
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# Add extensive logging for each agent
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print(f"Agent {i+1} - {agents[i].agent_name}")
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print("-----------------------------------")
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