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146 lines
3.5 KiB
146 lines
3.5 KiB
import os
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from dotenv import load_dotenv
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from termcolor import colored
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from swarms.models import OpenAIChat
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from swarms.prompts.code_interpreter import CODE_INTERPRETER
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from swarms.prompts.programming import DOCUMENTATION_SOP, TEST_SOP
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from swarms.structs import Agent
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load_dotenv()
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FEATURE = "Implement an all-new signup system in typescript using supabase"
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CODEBASE = """
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import React, { useState } from 'react';
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import UpperPanel from './UpperPanel';
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import LowerPanel from './LowerPanel';
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const MainPanel = () => {
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const [promptInstructionForLowerPanel, setPromptInstructionForLowerPanel] = useState('');
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const [formData, setFormData] = useState('');
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const [isLoading, setIsLoading] = useState(false);
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return (
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<div className="flex h-screen">
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<UpperPanel setPromptInstructionForLowerPanel={setPromptInstructionForLowerPanel}
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isLoading={isLoading}
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setIsLoading={setIsLoading}
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/>
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<LowerPanel promptInstruction={promptInstructionForLowerPanel} isLoading={isLoading} />
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</div>
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);
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};
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export default MainPanel;
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"""
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# Load the environment variables
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api_key = os.getenv("OPENAI_API_KEY")
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# Initialize the language agent
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llm = OpenAIChat(
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model_name="gpt-4",
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openai_api_key=api_key,
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temperature=0.5,
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max_tokens=4000,
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)
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# Product Manager Agent init
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product_manager_agent = Agent(
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llm=llm, max_loops=1, sop=CODE_INTERPRETER, autosave=True
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)
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# Initialize the agent with the language agent
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feature_implementer_frontend = Agent(
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llm=llm, max_loops=1, sop=CODE_INTERPRETER, autosave=True
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)
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# Create another agent for a different task
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feature_implementer_backend = Agent(
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llm=llm, max_loops=1, sop=CODE_INTERPRETER, autosave=True
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)
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# Create another agent for a different task
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tester_agent = Agent(llm=llm, max_loops=1, sop=TEST_SOP, autosave=True)
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# Create another agent for a different task
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documenting_agent = Agent(
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llm=llm, max_loops=1, sop=DOCUMENTATION_SOP, autosave=True
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)
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# Product Agent prompt
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def feature_codebase_product_agentprompt(
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feature: str, codebase: str
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) -> str:
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prompt = (
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"Create an algorithmic pseudocode for an all-new feature:"
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f" {feature} based on this codebase: {codebase}"
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)
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return prompt
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# Product Manager Agent
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product_manager_out = product_manager_agent.run(
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feature_codebase_product_agentprompt(FEATURE, CODEBASE)
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)
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print(
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colored(
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(
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"---------------------------- Product Manager Plan:"
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f" {product_manager_out}"
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),
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"cyan",
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)
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)
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# Feature Implementer Agent
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agent1_out = feature_implementer_frontend.run(
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f"Create the backend code for {FEATURE} in markdown based off of"
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f" this algorithmic pseudocode: {product_manager_out} the logic"
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f" based on the following codebase: {CODEBASE}"
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)
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print(
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colored(
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(
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"--------------------- Feature Implementer Code logic:"
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f" {agent1_out}"
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),
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"cyan",
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)
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)
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# Tester agent
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tester_agent_out = tester_agent.run(
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f"Create tests for the following code: {agent1_out}"
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)
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print(
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colored(
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(
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"---------------------------- Tests for the logic:"
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f" {tester_agent_out}"
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),
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"green",
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)
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)
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# Documentation Agent
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documenter_agent_out = documenting_agent.run(
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f"Document the following code: {agent1_out}"
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)
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print(
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colored(
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(
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"---------------------------- Documentation for the"
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f" logic: {documenter_agent_out}"
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),
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"yellow",
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)
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)
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