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239 lines
7.9 KiB
239 lines
7.9 KiB
"""
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Todo
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- You send structured data to the swarm through the users form they make
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- then connect rag for every agent using llama index to remember all the students data
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- structured outputs
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"""
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import os
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from dotenv import load_dotenv
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from swarms import Agent, AgentRearrange
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from swarm_models import OpenAIChat, OpenAIFunctionCaller
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from pydantic import BaseModel
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from typing import List
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class CollegeLog(BaseModel):
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college_name: str
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college_description: str
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college_admission_requirements: str
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class CollegesRecommendation(BaseModel):
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colleges: List[CollegeLog]
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reasoning: str
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load_dotenv()
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# Get the API key from environment variable
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api_key = os.getenv("GROQ_API_KEY")
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# Initialize the model
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model = OpenAIChat(
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openai_api_base="https://api.groq.com/openai/v1",
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openai_api_key=api_key,
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model_name="llama-3.1-70b-versatile",
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temperature=0.1,
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)
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FINAL_AGENT_PROMPT = """
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You are a college selection final decision maker. Your role is to:
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1. Synthesize all previous analyses and discussions
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2. Weigh competing factors and trade-offs
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3. Create a final ranked list of recommended colleges
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4. Provide clear rationale for each recommendation
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5. Include specific action items for each selected school
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6. Outline next steps in the application process
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Focus on creating actionable, well-reasoned final recommendations that
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balance all relevant factors and stakeholder input.
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"""
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function_caller = OpenAIFunctionCaller(
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system_prompt=FINAL_AGENT_PROMPT,
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openai_api_key=os.getenv("OPENAI_API_KEY"),
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base_model=CollegesRecommendation,
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parallel_tool_calls=True,
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)
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# Student Profile Analyzer Agent
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profile_analyzer_agent = Agent(
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agent_name="Student-Profile-Analyzer",
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system_prompt="""You are an expert student profile analyzer. Your role is to:
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1. Analyze academic performance, test scores, and extracurricular activities
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2. Identify student's strengths, weaknesses, and unique qualities
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3. Evaluate personal statements and essays
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4. Assess leadership experiences and community involvement
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5. Determine student's preferences for college environment, location, and programs
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6. Create a comprehensive student profile summary
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Always consider both quantitative metrics (GPA, test scores) and qualitative aspects
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(personal growth, challenges overcome, unique perspectives).""",
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llm=model,
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max_loops=1,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="profile_analyzer_agent.json",
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user_name="student",
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context_length=200000,
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output_type="string",
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)
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# College Research Agent
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college_research_agent = Agent(
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agent_name="College-Research-Specialist",
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system_prompt="""You are a college research specialist. Your role is to:
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1. Maintain updated knowledge of college admission requirements
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2. Research academic programs, campus culture, and student life
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3. Analyze admission statistics and trends
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4. Evaluate college-specific opportunities and resources
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5. Consider financial aid availability and scholarship opportunities
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6. Track historical admission data and acceptance rates
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Focus on providing accurate, comprehensive information about each institution
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while considering both academic and cultural fit factors.""",
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llm=model,
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max_loops=1,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="college_research_agent.json",
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user_name="researcher",
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context_length=200000,
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output_type="string",
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)
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# College Match Agent
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college_match_agent = Agent(
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agent_name="College-Match-Maker",
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system_prompt="""You are a college matching specialist. Your role is to:
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1. Compare student profiles with college requirements
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2. Evaluate fit based on academic, social, and cultural factors
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3. Consider geographic preferences and constraints
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4. Assess financial fit and aid opportunities
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5. Create tiered lists of reach, target, and safety schools
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6. Explain the reasoning behind each match
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Always provide a balanced list with realistic expectations while
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considering both student preferences and admission probability.""",
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llm=model,
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max_loops=1,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="college_match_agent.json",
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user_name="matcher",
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context_length=200000,
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output_type="string",
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)
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# Debate Moderator Agent
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debate_moderator_agent = Agent(
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agent_name="Debate-Moderator",
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system_prompt="""You are a college selection debate moderator. Your role is to:
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1. Facilitate discussions between different perspectives
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2. Ensure all relevant factors are considered
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3. Challenge assumptions and biases
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4. Synthesize different viewpoints
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5. Guide the group toward consensus
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6. Document key points of agreement and disagreement
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Maintain objectivity while ensuring all important factors are thoroughly discussed
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and evaluated.""",
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llm=model,
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max_loops=1,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="debate_moderator_agent.json",
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user_name="moderator",
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context_length=200000,
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output_type="string",
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)
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# Critique Agent
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critique_agent = Agent(
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agent_name="College-Selection-Critic",
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system_prompt="""You are a college selection critic. Your role is to:
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1. Evaluate the strength of college matches
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2. Identify potential overlooked factors
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3. Challenge assumptions in the selection process
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4. Assess risks and potential drawbacks
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5. Provide constructive feedback on selections
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6. Suggest alternative options when appropriate
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Focus on constructive criticism that helps improve the final college list
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while maintaining realistic expectations.""",
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llm=model,
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max_loops=1,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="critique_agent.json",
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user_name="critic",
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context_length=200000,
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output_type="string",
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)
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# Final Decision Agent
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final_decision_agent = Agent(
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agent_name="Final-Decision-Maker",
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system_prompt="""
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You are a college selection final decision maker. Your role is to:
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1. Synthesize all previous analyses and discussions
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2. Weigh competing factors and trade-offs
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3. Create a final ranked list of recommended colleges
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4. Provide clear rationale for each recommendation
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5. Include specific action items for each selected school
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6. Outline next steps in the application process
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Focus on creating actionable, well-reasoned final recommendations that
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balance all relevant factors and stakeholder input.
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""",
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llm=model,
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max_loops=1,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="final_decision_agent.json",
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user_name="decision_maker",
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context_length=200000,
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output_type="string",
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)
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# Initialize the Sequential Workflow
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college_selection_workflow = AgentRearrange(
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name="college-selection-swarm",
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description="Comprehensive college selection and analysis system",
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max_loops=1,
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agents=[
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profile_analyzer_agent,
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college_research_agent,
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college_match_agent,
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debate_moderator_agent,
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critique_agent,
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final_decision_agent,
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],
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output_type="all",
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flow=f"{profile_analyzer_agent.name} -> {college_research_agent.name} -> {college_match_agent.name} -> {debate_moderator_agent.name} -> {critique_agent.name} -> {final_decision_agent.name}",
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)
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# Example usage
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if __name__ == "__main__":
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# Example student profile input
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student_profile = """
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Student Profile:
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- GPA: 3.8
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- SAT: 1450
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- Interests: Computer Science, Robotics
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- Location Preference: East Coast
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- Extracurriculars: Robotics Club President, Math Team
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- Budget: Need financial aid
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- Preferred Environment: Medium-sized urban campus
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"""
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# Run the comprehensive college selection analysis
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result = college_selection_workflow.run(
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student_profile,
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no_use_clusterops=True,
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)
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print(result)
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