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70 lines
1.9 KiB
70 lines
1.9 KiB
import os
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from dotenv import load_dotenv
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import swarms.prompts.education as edu_prompts
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from swarms import Agent, SequentialWorkflow
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from swarm_models import OpenAIChat
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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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# User preferences (can be dynamically set in a real application)
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user_preferences = {
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"subjects": "Cognitive Architectures",
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"learning_style": "Visual",
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"challenge_level": "Moderate",
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}
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# Formatted prompts from user preferences
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curriculum_prompt = edu_prompts.CURRICULUM_DESIGN_PROMPT.format(
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**user_preferences
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)
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interactive_prompt = edu_prompts.INTERACTIVE_LEARNING_PROMPT.format(
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**user_preferences
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)
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sample_prompt = edu_prompts.SAMPLE_TEST_PROMPT.format(
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**user_preferences
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)
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image_prompt = edu_prompts.IMAGE_GENERATION_PROMPT.format(
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**user_preferences
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)
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# Initialize agents for different educational tasks
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curriculum_agent = Agent(llm=llm, max_loops=1, sop=curriculum_prompt)
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interactive_learning_agent = Agent(
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llm=llm, max_loops=1, sop=interactive_prompt
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)
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sample_lesson_agent = Agent(llm=llm, max_loops=1, sop=sample_prompt)
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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(curriculum_agent, "Generate a curriculum")
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workflow.add(
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interactive_learning_agent, "Generate an interactive lesson"
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)
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workflow.add(sample_lesson_agent, "Generate a practice test")
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# Execute the workflow for text-based tasks
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workflow.run()
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# Generate an image using Stable Diffusion
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# Output results for each task
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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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# Output image result
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print("Image Generation Task: Generate an image for the interactive")
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