import os
import gradio as gr
from textwrap import dedent
import google.generativeai as genai

# Tool import
from crewai.tools.gemini_tools import GeminiSearchTools
from langchain.tools.yahoo_finance_news import YahooFinanceNewsTool
from crewai.tools.browser_tools import BrowserTools
from crewai.tools.sec_tools import SECTools

# Google Langchain
from langchain_google_genai import GoogleGenerativeAI

#Crew imports
from crewai import Agent, Task, Crew, Process

# Retrieve API Key from Environment Variable
GOOGLE_AI_STUDIO = os.environ.get('GOOGLE_API_KEY')

# Ensure the API key is available
if not GOOGLE_AI_STUDIO:
    raise ValueError("API key not found. Please set the GOOGLE_AI_STUDIO2 environment variable.")

# Set gemini_llm
gemini_llm = GoogleGenerativeAI(model="gemini-pro", google_api_key=GOOGLE_AI_STUDIO)

# Base Example with Gemini Search

def crewai_process(research_topic):
    # Define your agents with roles and goals
    researcher = Agent(
        role='Senior Research Analyst',
        goal=f'Uncover cutting-edge developments in {research_topic}',
        backstory="""You are a Senior Research Analyst at a leading think tank.
        Your expertise lies in identifying emerging trends. You have a knack for dissecting complex data and presenting
        actionable insights.""",
        verbose=True,
        allow_delegation=False,
        llm = gemini_llm,
        tools=[
                GeminiSearchTools.gemini_search
      ]

    )
    writer = Agent(
        role='Tech Content Strategist',
        goal='Craft compelling content on tech advancements',
        backstory="""You are a renowned Tech Social Media Content Writer and Strategist, known for your insightful
        and engaging articles on technology and innovation. With a deep understanding of
        the tech industry and how people are impacted by it, you transform complex concepts into compelling narratives.""",
        verbose=True,
        allow_delegation=True,
        llm = gemini_llm
     
        # Add tools and other optional parameters as needed
    )

    # Create tasks for your agents
    task1 = Task(
        description=f"""Conduct a comprehensive analysis of the latest advancements in {research_topic}.
        Compile your findings in a detailed report. Your final answer MUST be a full analysis report""",
        agent=researcher
    )

    task2 = Task(
        description="""Using the insights from the researcher's report, develop an engaging blog
        post that highlights the most significant advancements.
        Your post should be informative yet accessible, catering to a tech-savvy audience.
        Aim for a narrative that captures the essence of these breakthroughs and their
        implications for the future. Your final answer MUST be the full blog post of at least 3 paragraphs.""",
        agent=writer
    )

    # Instantiate your crew with a sequential process
    crew = Crew(
        agents=[researcher, writer],
        tasks=[task1, task2],
        verbose=2,
        process=Process.sequential
    )

    # Get your crew to work!
    result = crew.kickoff()
    
    return result

# Create a Gradio interface
iface = gr.Interface(
    fn=crewai_process, 
    inputs=gr.Textbox(lines=2, placeholder="Enter Research Topic Here..."), 
    outputs="text",
    title="CrewAI on Gemini (Blog Post Writer)",
    description="Input a research topic to get a comprehensive analysis (in logs) and a blog post draft (in output). To learn more connect with Mike Lively on LinkedIn at https://www.linkedin.com/in/awsmulticloud/ or join his cloud Meetup at https://www.meetup.com/florence-aws-user-group-meetup/"
)

# Launch the interface
iface.launch()