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87 lines
2.4 KiB
87 lines
2.4 KiB
import os
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from dotenv import load_dotenv
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import swarms.prompts.urban_planning as upp
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from swarm_models import GPT4VisionAPI, OpenAIChat
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from swarms.structs import Agent, SequentialWorkflow
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# Load environment variables
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load_dotenv()
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api_key = os.getenv("OPENAI_API_KEY")
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stability_api_key = os.getenv("STABILITY_API_KEY")
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# Initialize language model
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llm = OpenAIChat(
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openai_api_key=api_key, temperature=0.5, max_tokens=3000
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)
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# Initialize Vision model
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vision_api = GPT4VisionAPI(api_key=api_key)
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# Initialize agents for urban planning tasks
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architecture_analysis_agent = Agent(
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llm=llm, max_loops=1, sop=upp.ARCHITECTURE_ANALYSIS_PROMPT
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)
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infrastructure_evaluation_agent = Agent(
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llm=llm, max_loops=1, sop=upp.INFRASTRUCTURE_EVALUATION_PROMPT
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)
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traffic_flow_analysis_agent = Agent(
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llm=llm, max_loops=1, sop=upp.TRAFFIC_FLOW_ANALYSIS_PROMPT
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)
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environmental_impact_assessment_agent = Agent(
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llm=llm,
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max_loops=1,
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sop=upp.ENVIRONMENTAL_IMPACT_ASSESSMENT_PROMPT,
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)
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public_space_utilization_agent = Agent(
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llm=llm, max_loops=1, sop=upp.PUBLIC_SPACE_UTILIZATION_PROMPT
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)
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socioeconomic_impact_analysis_agent = Agent(
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llm=llm, max_loops=1, sop=upp.SOCIOECONOMIC_IMPACT_ANALYSIS_PROMPT
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)
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# Initialize the final planning agent
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final_plan_agent = Agent(
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llm=llm, max_loops=1, sop=upp.FINAL_URBAN_IMPROVEMENT_PLAN_PROMPT
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)
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# Create Sequential Workflow
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workflow = SequentialWorkflow(max_loops=1)
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# Add tasks to workflow with personalized prompts
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workflow.add(architecture_analysis_agent, "Architecture Analysis")
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workflow.add(
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infrastructure_evaluation_agent, "Infrastructure Evaluation"
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)
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workflow.add(traffic_flow_analysis_agent, "Traffic Flow Analysis")
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workflow.add(
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environmental_impact_assessment_agent,
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"Environmental Impact Assessment",
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)
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workflow.add(
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public_space_utilization_agent, "Public Space Utilization"
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)
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workflow.add(
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socioeconomic_impact_analysis_agent,
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"Socioeconomic Impact Analysis",
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)
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workflow.add(
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final_plan_agent,
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(
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"Generate the final urban improvement plan based on all"
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" previous agent's findings"
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),
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)
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# Run the workflow for individual analysis tasks
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# Execute the workflow for the final planning
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workflow.run()
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# Output results for each task and the final plan
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for task in workflow.tasks:
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print(
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f"Task Description: {task.description}\nResult:"
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f" {task.result}\n"
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)
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