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from crewai import Agent, Task, Crew, Process
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from crewai_tools import SerperDevTool
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import os
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llm_models = [
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"gemini/gemini-1.5-flash",
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"gemini/gemini-1.5-pro",
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"gemini/gemini-pro"
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]
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selected_model = llm_models[0]
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def set_model(selected_model_name):
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global selected_model
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selected_model = selected_model_name
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def configure_api_keys(gemini_api_key, search_choice, serper_api_key):
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if not gemini_api_key:
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raise ValueError("Gemini API key is required")
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os.environ['GEMINI_API_KEY'] = gemini_api_key
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search_tool = None
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if search_choice == "Yes":
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if not serper_api_key:
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raise ValueError("Serper API key is required for online search")
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os.environ['SERPER_API_KEY'] = serper_api_key
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search_tool = SerperDevTool()
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return search_tool
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def run_crew_cga(gemini_api_key, search_choice, serper_api_key, topic):
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try:
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search_tool = configure_api_keys(gemini_api_key, search_choice, serper_api_key)
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researcher = Agent(
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role="Online Research Specialist",
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goal=f"Aggregate comprehensive information on {topic}",
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verbose=True,
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backstory="Expert research analyst with data sourcing expertise",
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tools=[search_tool] if search_tool else [],
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llm=selected_model,
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allow_delegation=True
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)
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content_writer = Agent(
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role="Expert Content Writer",
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goal=f"Create SEO-optimized content on {topic}",
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verbose=True,
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backstory="Professional writer with digital journalism background",
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tools=[],
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llm=selected_model,
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allow_delegation=False
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)
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research_task = Task(
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description=f"Conduct SEO research on '{topic}'",
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expected_output="Detailed research report with SEO recommendations",
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tools=[search_tool] if search_tool else [],
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agent=researcher
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)
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writer_task = Task(
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description=f"Write SEO-optimized article on '{topic}'",
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expected_output="Polished article draft ready for publication",
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agent=content_writer,
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output_file="content.md"
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)
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crew = Crew(
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agents=[researcher, content_writer],
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tasks=[research_task, writer_task],
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process=Process.sequential,
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verbose=True,
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max_rpm=100,
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share_crew=True,
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output_log_file=True
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)
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crew.kickoff(inputs={'topic': topic})
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with open("content.md", "r") as f:
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content = f.read()
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with open("logs.txt", 'r') as f:
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logs = f.read()
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with open("logs.txt", 'w') as f:
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f.truncate(0)
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return content, logs
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except Exception as e:
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return f"Error: {str(e)}", str(e)
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