from crewai import Agent,Task, Crew

planner = Agent(
    role="Content Planner",
    goal="Plan engaging and factually accurate content on {topic}",
    backstory="You're working on planning a blog article "
              "about the topic: {topic}."
              "You collect information that helps the "
              "audience learn something "
              "and make informed decisions. "
              "Your work is the basis for "
              "the Content Writer to write an article on this topic.",
    allow_delegation=False,
	verbose=True
)

writer = Agent(
    role="Content Writer",
    goal="Write insightful and factually accurate "
         "opinion piece about the topic: {topic}",
    backstory="You're working on a writing "
              "a new opinion piece about the topic: {topic}. "
              "You base your writing on the work of "
              "the Content Planner, who provides an outline "
              "and relevant context about the topic. "
              "You follow the main objectives and "
              "direction of the outline, "
              "as provide by the Content Planner. "
              "You also provide objective and impartial insights "
              "and back them up with information "
              "provide by the Content Planner. "
              "You acknowledge in your opinion piece "
              "when your statements are opinions "
              "as opposed to objective statements.",
    allow_delegation=False,
    verbose=True
)

editor = Agent(
    role="Editor",
    goal="Edit a given blog post to align with "
         "the writing style of the organization. ",
    backstory="You are an editor who receives a blog post "
              "from the Content Writer. "
              "Your goal is to review the blog post "
              "to ensure that it follows journalistic best practices,"
              "provides balanced viewpoints "
              "when providing opinions or assertions, "
              "and also avoids major controversial topics "
              "or opinions when possible.",
    allow_delegation=False,
    verbose=True
)

plan = Task(
    description=(
        "1. Prioritize the latest trends, key players, "
            "and noteworthy news on {topic}.\n"
        "2. Identify the target audience, considering "
            "their interests and pain points.\n"
        "3. Develop a detailed content outline including "
            "an introduction, key points, and a call to action.\n"
        "4. Include SEO keywords and relevant data or sources."
    ),
    expected_output="A comprehensive content plan document "
        "with an outline, audience analysis, "
        "SEO keywords, and resources.",
    agent=planner,
)

write = Task(
    description=(
        "1. Use the content plan to craft a compelling "
            "blog post on {topic}.\n"
        "2. Incorporate SEO keywords naturally.\n"
		"3. Sections/Subtitles are properly named "
            "in an engaging manner.\n"
        "4. Ensure the post is structured with an "
            "engaging introduction, insightful body, "
            "and a summarizing conclusion.\n"
        "5. Proofread for grammatical errors and "
            "alignment with the brand's voice.\n"
    ),
    expected_output="A well-written blog post "
        "in markdown format, ready for publication, "
        "each section should have 2 or 3 paragraphs.",
    agent=writer,
)

edit = Task(
    description=("Proofread the given blog post for "
                 "grammatical errors and "
                 "alignment with the brand's voice."),
    expected_output="A well-written blog post in markdown format, "
                    "ready for publication, "
                    "each section should have 2 or 3 paragraphs.",
    agent=editor
)

crew = Crew(
    agents=[planner, writer, editor],
    tasks=[plan, write, edit],
    verbose=2
)

result = crew.kickoff(inputs={"topic": "Artificial Intelligence"})

from IPython.display import Markdown
Markdown(result)

# Warning control
import warnings
warnings.filterwarnings('ignore')

from crewai import Agent, Task, Crew

import os
from utils import get_openai_api_key

openai_api_key = get_openai_api_key()
os.environ["OPENAI_MODEL_NAME"] = 'gpt-3.5-turbo'

support_agent = Agent(
    role="Senior Support Representative",
	goal="Be the most friendly and helpful "
        "support representative in your team",
	backstory=(
		"You work at crewAI (https://crewai.com) and "
        " are now working on providing "
		"support to {customer}, a super important customer "
        " for your company."
		"You need to make sure that you provide the best support!"
		"Make sure to provide full complete answers, "
        " and make no assumptions."
	),
	allow_delegation=False,
	verbose=True
)

support_quality_assurance_agent = Agent(
	role="Support Quality Assurance Specialist",
	goal="Get recognition for providing the "
    "best support quality assurance in your team",
	backstory=(
		"You work at crewAI (https://crewai.com) and "
        "are now working with your team "
		"on a request from {customer} ensuring that "
        "the support representative is "
		"providing the best support possible.\n"
		"You need to make sure that the support representative "
        "is providing full"
		"complete answers, and make no assumptions."
	),
	verbose=True
)

from crewai_tools import SerperDevTool, \
                         ScrapeWebsiteTool, \
                         WebsiteSearchTool

docs_scrape_tool = ScrapeWebsiteTool(
    website_url="https://docs.crewai.com/how-to/Creating-a-Crew-and-kick-it-off/"
)

inquiry_resolution = Task(
    description=(
        "{customer} just reached out with a super important ask:\n"
	    "{inquiry}\n\n"
        "{person} from {customer} is the one that reached out. "
		"Make sure to use everything you know "
        "to provide the best support possible."
		"You must strive to provide a complete "
        "and accurate response to the customer's inquiry."
    ),
    expected_output=(
	    "A detailed, informative response to the "
        "customer's inquiry that addresses "
        "all aspects of their question.\n"
        "The response should include references "
        "to everything you used to find the answer, "
        "including external data or solutions. "
        "Ensure the answer is complete, "
		"leaving no questions unanswered, and maintain a helpful and friendly "
		"tone throughout."
    ),
	tools=[docs_scrape_tool],
    agent=support_agent,
)

quality_assurance_review = Task(
    description=(
        "Review the response drafted by the Senior Support Representative for {customer}'s inquiry. "
        "Ensure that the answer is comprehensive, accurate, and adheres to the "
		"high-quality standards expected for customer support.\n"
        "Verify that all parts of the customer's inquiry "
        "have been addressed "
		"thoroughly, with a helpful and friendly tone.\n"
        "Check for references and sources used to "
        " find the information, "
		"ensuring the response is well-supported and "
        "leaves no questions unanswered."
    ),
    expected_output=(
        "A final, detailed, and informative response "
        "ready to be sent to the customer.\n"
        "This response should fully address the "
        "customer's inquiry, incorporating all "
		"relevant feedback and improvements.\n"
		"Don't be too formal, we are a chill and cool company "
	    "but maintain a professional and friendly tone throughout."
    ),
    agent=support_quality_assurance_agent,
)

crew = Crew(
  agents=[support_agent, support_quality_assurance_agent],
  tasks=[inquiry_resolution, quality_assurance_review],
  verbose=2,
  memory=True
)

inputs = {
    "customer": "DeepLearningAI",
    "person": "Andrew Ng",
    "inquiry": "I need help with setting up a Crew "
               "and kicking it off, specifically "
               "how can I add memory to my crew? "
               "Can you provide guidance?"
}
result = crew.kickoff(inputs=inputs)

from IPython.display import Markdown
Markdown(result)

# Warning control
import warnings
warnings.filterwarnings('ignore')

from crewai import Agent, Task, Crew

import os
from utils import get_openai_api_key, pretty_print_result
from utils import get_serper_api_key

openai_api_key = get_openai_api_key()
os.environ["OPENAI_MODEL_NAME"] = 'gpt-3.5-turbo'
os.environ["SERPER_API_KEY"] = get_serper_api_key()

sales_rep_agent = Agent(
    role="Sales Representative",
    goal="Identify high-value leads that match "
         "our ideal customer profile",
    backstory=(
        "As a part of the dynamic sales team at CrewAI, "
        "your mission is to scour "
        "the digital landscape for potential leads. "
        "Armed with cutting-edge tools "
        "and a strategic mindset, you analyze data, "
        "trends, and interactions to "
        "unearth opportunities that others might overlook. "
        "Your work is crucial in paving the way "
        "for meaningful engagements and driving the company's growth."
    ),
    allow_delegation=False,
    verbose=True
)

lead_sales_rep_agent = Agent(
    role="Lead Sales Representative",
    goal="Nurture leads with personalized, compelling communications",
    backstory=(
        "Within the vibrant ecosystem of CrewAI's sales department, "
        "you stand out as the bridge between potential clients "
        "and the solutions they need."
        "By creating engaging, personalized messages, "
        "you not only inform leads about our offerings "
        "but also make them feel seen and heard."
        "Your role is pivotal in converting interest "
        "into action, guiding leads through the journey "
        "from curiosity to commitment."
    ),
    allow_delegation=False,
    verbose=True
)

from crewai_tools import DirectoryReadTool, \
                         FileReadTool, \
                         SerperDevTool

directory_read_tool = DirectoryReadTool(directory='./instructions')
file_read_tool = FileReadTool()
search_tool = SerperDevTool()

from crewai_tools import BaseTool

class SentimentAnalysisTool(BaseTool):
    name: str ="Sentiment Analysis Tool"
    description: str = ("Analyzes the sentiment of text "
         "to ensure positive and engaging communication.")
    
    def _run(self, text: str) -> str:
        # Your custom code tool goes here
        return "positive"

sentiment_analysis_tool = SentimentAnalysisTool()

lead_profiling_task = Task(
    description=(
        "Conduct an in-depth analysis of {lead_name}, "
        "a company in the {industry} sector "
        "that recently showed interest in our solutions. "
        "Utilize all available data sources "
        "to compile a detailed profile, "
        "focusing on key decision-makers, recent business "
        "developments, and potential needs "
        "that align with our offerings. "
        "This task is crucial for tailoring "
        "our engagement strategy effectively.\n"
        "Don't make assumptions and "
        "only use information you absolutely sure about."
    ),
    expected_output=(
        "A comprehensive report on {lead_name}, "
        "including company background, "
        "key personnel, recent milestones, and identified needs. "
        "Highlight potential areas where "
        "our solutions can provide value, "
        "and suggest personalized engagement strategies."
    ),
    tools=[directory_read_tool, file_read_tool, search_tool],
    agent=sales_rep_agent,
)

personalized_outreach_task = Task(
    description=(
        "Using the insights gathered from "
        "the lead profiling report on {lead_name}, "
        "craft a personalized outreach campaign "
        "aimed at {key_decision_maker}, "
        "the {position} of {lead_name}. "
        "The campaign should address their recent {milestone} "
        "and how our solutions can support their goals. "
        "Your communication must resonate "
        "with {lead_name}'s company culture and values, "
        "demonstrating a deep understanding of "
        "their business and needs.\n"
        "Don't make assumptions and only "
        "use information you absolutely sure about."
    ),
    expected_output=(
        "A series of personalized email drafts "
        "tailored to {lead_name}, "
        "specifically targeting {key_decision_maker}."
        "Each draft should include "
        "a compelling narrative that connects our solutions "
        "with their recent achievements and future goals. "
        "Ensure the tone is engaging, professional, "
        "and aligned with {lead_name}'s corporate identity."
    ),
    tools=[sentiment_analysis_tool, search_tool],
    agent=lead_sales_rep_agent,
)

crew = Crew(
    agents=[sales_rep_agent, 
            lead_sales_rep_agent],
    
    tasks=[lead_profiling_task, 
           personalized_outreach_task],
	
    verbose=2,
	memory=True
)

inputs = {
    "lead_name": "DeepLearningAI",
    "industry": "Online Learning Platform",
    "key_decision_maker": "Andrew Ng",
    "position": "CEO",
    "milestone": "product launch"
}

result = crew.kickoff(inputs=inputs)

from IPython.display import Markdown
Markdown(result)

# Warning control
import warnings
warnings.filterwarnings('ignore')

from crewai import Agent, Crew, Task

import os
from utils import get_openai_api_key,get_serper_api_key

openai_api_key = get_openai_api_key()
os.environ["OPENAI_MODEL_NAME"] = 'gpt-3.5-turbo'
os.environ["SERPER_API_KEY"] = get_serper_api_key()

from crewai_tools import ScrapeWebsiteTool, SerperDevTool

# Initialize the tools
search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()

# Agent 1: Venue Coordinator
venue_coordinator = Agent(
    role="Venue Coordinator",
    goal="Identify and book an appropriate venue "
    "based on event requirements",
    tools=[search_tool, scrape_tool],
    verbose=True,
    backstory=(
        "With a keen sense of space and "
        "understanding of event logistics, "
        "you excel at finding and securing "
        "the perfect venue that fits the event's theme, "
        "size, and budget constraints."
    )
)

 # Agent 2: Logistics Manager
logistics_manager = Agent(
    role='Logistics Manager',
    goal=(
        "Manage all logistics for the event "
        "including catering and equipmen"
    ),
    tools=[search_tool, scrape_tool],
    verbose=True,
    backstory=(
        "Organized and detail-oriented, "
        "you ensure that every logistical aspect of the event "
        "from catering to equipment setup "
        "is flawlessly executed to create a seamless experience."
    )
)

# Agent 3: Marketing and Communications Agent
marketing_communications_agent = Agent(
    role="Marketing and Communications Agent",
    goal="Effectively market the event and "
         "communicate with participants",
    tools=[search_tool, scrape_tool],
    verbose=True,
    backstory=(
        "Creative and communicative, "
        "you craft compelling messages and "
        "engage with potential attendees "
        "to maximize event exposure and participation."
    )
)

from pydantic import BaseModel
# Define a Pydantic model for venue details 
# (demonstrating Output as Pydantic)
class VenueDetails(BaseModel):
    name: str
    address: str
    capacity: int
    booking_status: str

venue_task = Task(
    description="Find a venue in {event_city} "
                "that meets criteria for {event_topic}.",
    expected_output="All the details of a specifically chosen"
                    "venue you found to accommodate the event.",
    human_input=True,
    output_json=VenueDetails,
    output_file="venue_details.json",  
      # Outputs the venue details as a JSON file
    agent=venue_coordinator
)

logistics_task = Task(
    description="Coordinate catering and "
                 "equipment for an event "
                 "with {expected_participants} participants "
                 "on {tentative_date}.",
    expected_output="Confirmation of all logistics arrangements "
                    "including catering and equipment setup.",
    human_input=True,
    async_execution=True,
    agent=logistics_manager
)

marketing_task = Task(
    description="Promote the {event_topic} "
                "aiming to engage at least"
                "{expected_participants} potential attendees.",
    expected_output="Report on marketing activities "
                    "and attendee engagement formatted as markdown.",
    async_execution=True,
    output_file="marketing_report.md",  # Outputs the report as a text file
    agent=marketing_communications_agent
)

# Define the crew with agents and tasks
event_management_crew = Crew(
    agents=[venue_coordinator, 
            logistics_manager, 
            marketing_communications_agent],
    
    tasks=[venue_task, 
           logistics_task, 
           marketing_task],
    
    verbose=True
)

event_details = {
    'event_topic': "Tech Innovation Conference",
    'event_description': "A gathering of tech innovators "
                         "and industry leaders "
                         "to explore future technologies.",
    'event_city': "San Francisco",
    'tentative_date': "2024-09-15",
    'expected_participants': 500,
    'budget': 20000,
    'venue_type': "Conference Hall"
}

result = event_management_crew.kickoff(inputs=event_details)

import json
from pprint import pprint

with open('venue_details.json') as f:
   data = json.load(f)

pprint(data)

from IPython.display import Markdown
Markdown("marketing_report.md")