!pip install notte python-dotenv pydantic google-generativeai requests beautifulsoup4
!patchright install --with-deps chromium


import os
import json
import time
from typing import List, Optional
from pydantic import BaseModel
import google.generativeai as genai
from dotenv import load_dotenv


GEMINI_API_KEY = "USE YOUR OWN API KEY HERE"
os.environ['GEMINI_API_KEY'] = GEMINI_API_KEY
genai.configure(api_key=GEMINI_API_KEY)


import notte

class ProductInfo(BaseModel):
   name: str
   price: str
   rating: Optional[float]
   availability: str
   description: str


class NewsArticle(BaseModel):
   title: str
   summary: str
   url: str
   date: str
   source: str


class SocialMediaPost(BaseModel):
   content: str
   author: str
   likes: int
   timestamp: str
   platform: str


class SearchResult(BaseModel):
   query: str
   results: List[dict]
   total_found: int

class AdvancedNotteAgent:
   def __init__(self, headless=True, max_steps=20):
       self.headless = headless
       self.max_steps = max_steps
       self.session = None
       self.agent = None
      
   def __enter__(self):
       self.session = notte.Session(headless=self.headless)
       self.session.__enter__()
       self.agent = notte.Agent(
           session=self.session,
           reasoning_model='gemini/gemini-2.5-flash',
           max_steps=self.max_steps
       )
       return self
      
   def __exit__(self, exc_type, exc_val, exc_tb):
       if self.session:
           self.session.__exit__(exc_type, exc_val, exc_tb)


   def research_product(self, product_name: str, website: str = "amazon.com") -> ProductInfo:
       """Research a product and extract structured information"""
       task = f"Go to {website}, search for '{product_name}', click on the first relevant product, and extract detailed product information including name, price, rating, availability, and description."
      
       response = self.agent.run(
           task=task,
           response_format=ProductInfo,
           url=f"https://{website}"
       )
       return response.answer


   def news_aggregator(self, topic: str, num_articles: int = 3) -> List[NewsArticle]:
       """Aggregate news articles on a specific topic"""
       task = f"Search for recent news about '{topic}', find {num_articles} relevant articles, and extract title, summary, URL, date, and source for each."
      
       response = self.agent.run(
           task=task,
           url="https://news.google.com",
           response_format=List[NewsArticle]
       )
       return response.answer


   def social_media_monitor(self, hashtag: str, platform: str = "twitter") -> List[SocialMediaPost]:
       """Monitor social media for specific hashtags"""
       if platform.lower() == "twitter":
           url = "https://twitter.com"
       elif platform.lower() == "reddit":
           url = "https://reddit.com"
       else:
           url = f"https://{platform}.com"
          
       task = f"Go to {platform}, search for posts with hashtag '{hashtag}', and extract content, author, engagement metrics, and timestamps from the top 5 posts."
      
       response = self.agent.run(
           task=task,
           url=url,
           response_format=List[SocialMediaPost]
       )
       return response.answer


   def competitive_analysis(self, company: str, competitors: List[str]) -> dict:
       """Perform competitive analysis by gathering pricing and feature data"""
       results = {}
      
       for competitor in [company] + competitors:
           task = f"Go to {competitor}'s website, find their pricing page or main product page, and extract key features, pricing tiers, and unique selling points."
          
           try:
               response = self.agent.run(
                   task=task,
                   url=f"https://{competitor}.com"
               )
               results[competitor] = response.answer
               time.sleep(2) 
           except Exception as e:
               results[competitor] = f"Error: {str(e)}"
              
       return results


   def job_market_scanner(self, job_title: str, location: str = "remote") -> List[dict]:
       """Scan job market for opportunities"""
       task = f"Search for '{job_title}' jobs in '{location}', extract job titles, companies, salary ranges, and application URLs from the first 10 results."
      
       response = self.agent.run(
           task=task,
           url="https://indeed.com"
       )
       return response.answer


   def price_comparison(self, product: str, websites: List[str]) -> dict:
       """Compare prices across multiple websites"""
       price_data = {}
      
       for site in websites:
           task = f"Search for '{product}' on this website and find the best price, including any discounts or special offers."
          
           try:
               response = self.agent.run(
                   task=task,
                   url=f"https://{site}"
               )
               price_data[site] = response.answer
               time.sleep(1)
           except Exception as e:
               price_data[site] = f"Error: {str(e)}"
              
       return price_data


   def content_research(self, topic: str, content_type: str = "blog") -> dict:
       """Research content ideas and trending topics"""
       if content_type == "blog":
           url = "https://medium.com"
           task = f"Search for '{topic}' articles, analyze trending content, and identify popular themes, engagement patterns, and content gaps."
       elif content_type == "video":
           url = "https://youtube.com"
           task = f"Search for '{topic}' videos, analyze view counts, titles, and descriptions to identify trending formats and popular angles."
       else:
           url = "https://google.com"
           task = f"Search for '{topic}' content across the web and analyze trending discussions and popular formats."
          
       response = self.agent.run(task=task, url=url)
       return {"topic": topic, "insights": response.answer, "platform": content_type}

def demo_ecommerce_research():
   """Demo: E-commerce product research and comparison"""
   print(" E-commerce Research Demo")
   print("=" * 50)
  
   with AdvancedNotteAgent(headless=True) as agent:
       product = agent.research_product("wireless earbuds", "amazon.com")
       print(f"Product Research Results:")
       print(f"Name: {product.name}")
       print(f"Price: {product.price}")
       print(f"Rating: {product.rating}")
       print(f"Availability: {product.availability}")
       print(f"Description: {product.description[:100]}...")
      
       print("\n Price Comparison:")
       websites = ["amazon.com", "ebay.com", "walmart.com"]
       prices = agent.price_comparison("wireless earbuds", websites)
       for site, data in prices.items():
           print(f"{site}: {data}")


def demo_news_intelligence():
   """Demo: News aggregation and analysis"""
   print(" News Intelligence Demo")
   print("=" * 50)
  
   with AdvancedNotteAgent() as agent:
       articles = agent.news_aggregator("artificial intelligence", 3)
      
       for i, article in enumerate(articles, 1):
           print(f"\nArticle {i}:")
           print(f"Title: {article.title}")
           print(f"Source: {article.source}")
           print(f"Summary: {article.summary}")
           print(f"URL: {article.url}")


def demo_social_listening():
   """Demo: Social media monitoring and sentiment analysis"""
   print(" Social Media Listening Demo")
   print("=" * 50)
  
   with AdvancedNotteAgent() as agent:
       posts = agent.social_media_monitor("#AI", "reddit")
      
       for i, post in enumerate(posts, 1):
           print(f"\nPost {i}:")
           print(f"Author: {post.author}")
           print(f"Content: {post.content[:100]}...")
           print(f"Engagement: {post.likes} likes")
           print(f"Platform: {post.platform}")


def demo_market_intelligence():
   """Demo: Competitive analysis and market research"""
   print(" Market Intelligence Demo")
   print("=" * 50)
  
   with AdvancedNotteAgent() as agent:
       company = "openai"
       competitors = ["anthropic", "google"]
       analysis = agent.competitive_analysis(company, competitors)
      
       for comp, data in analysis.items():
           print(f"\n{comp.upper()}:")
           print(f"Analysis: {str(data)[:200]}...")


def demo_job_market_analysis():
   """Demo: Job market scanning and analysis"""
   print(" Job Market Analysis Demo")
   print("=" * 50)
  
   with AdvancedNotteAgent() as agent:
       jobs = agent.job_market_scanner("python developer", "san francisco")
      
       print(f"Found {len(jobs)} job opportunities:")
       for job in jobs[:3]:  
           print(f"- {job}")


def demo_content_strategy():
   """Demo: Content research and trend analysis"""
   print(" Content Strategy Demo")
   print("=" * 50)
  
   with AdvancedNotteAgent() as agent:
       blog_research = agent.content_research("machine learning", "blog")
       video_research = agent.content_research("machine learning", "video")
      
       print("Blog Content Insights:")
       print(blog_research["insights"][:300] + "...")
      
       print("\nVideo Content Insights:")
       print(video_research["insights"][:300] + "...")

class WorkflowManager:
   def __init__(self):
       self.agents = []
       self.results = {}
      
   def add_agent_task(self, name: str, task_func, *args, **kwargs):
       """Add an agent task to the workflow"""
       self.agents.append({
           'name': name,
           'func': task_func,
           'args': args,
           'kwargs': kwargs
       })
      
   def execute_workflow(self, parallel=False):
       """Execute all agent tasks in the workflow"""
       print(" Executing Multi-Agent Workflow")
       print("=" * 50)
      
       for agent_task in self.agents:
           name = agent_task['name']
           func = agent_task['func']
           args = agent_task['args']
           kwargs = agent_task['kwargs']
          
           print(f"\n Executing {name}...")
           try:
               result = func(*args, **kwargs)
               self.results[name] = result
               print(f" {name} completed successfully")
           except Exception as e:
               self.results[name] = f"Error: {str(e)}"
               print(f" {name} failed: {str(e)}")
              
           if not parallel:
               time.sleep(2) 
       return self.results


def market_research_workflow(company_name: str, product_category: str):
   """Complete market research workflow"""
   workflow = WorkflowManager()
  
   workflow.add_agent_task(
       "Product Research",
       lambda: research_trending_products(product_category)
   )
  
   workflow.add_agent_task(
       "Competitive Analysis",
       lambda: analyze_competitors(company_name, product_category)
   )
  
   workflow.add_agent_task(
       "Social Sentiment",
       lambda: monitor_brand_sentiment(company_name)
   )
  
   return workflow.execute_workflow()


def research_trending_products(category: str):
   """Research trending products in a category"""
   with AdvancedNotteAgent(headless=True) as agent:
       task = f"Research trending {category} products, find top 5 products with prices, ratings, and key features."
       response = agent.agent.run(
           task=task,
           url="https://amazon.com"
       )
       return response.answer


def analyze_competitors(company: str, category: str):
   """Analyze competitors in the market"""
   with AdvancedNotteAgent(headless=True) as agent:
       task = f"Research {company} competitors in {category}, compare pricing strategies, features, and market positioning."
       response = agent.agent.run(
           task=task,
           url="https://google.com"
       )
       return response.answer


def monitor_brand_sentiment(brand: str):
   """Monitor brand sentiment across platforms"""
   with AdvancedNotteAgent(headless=True) as agent:
       task = f"Search for recent mentions of {brand} on social media and news, analyze sentiment and key themes."
       response = agent.agent.run(
           task=task,
           url="https://reddit.com"
       )
       return response.answer

def main():
   """Main function to run all demos"""
   print(" Advanced Notte AI Agent Tutorial")
   print("=" * 60)
   print("Note: Make sure to set your GEMINI_API_KEY above!")
   print("Get your free API key at: https://makersuite.google.com/app/apikey")
   print("=" * 60)
  
   if GEMINI_API_KEY == "YOUR_GEMINI_API_KEY":
       print(" Please set your GEMINI_API_KEY in the code above!")
       return
  
   try:
       print("\n1. E-commerce Research Demo")
       demo_ecommerce_research()
      
       print("\n2. News Intelligence Demo")
       demo_news_intelligence()
      
       print("\n3. Social Media Listening Demo")
       demo_social_listening()
      
       print("\n4. Market Intelligence Demo")
       demo_market_intelligence()
      
       print("\n5. Job Market Analysis Demo")
       demo_job_market_analysis()
      
       print("\n6. Content Strategy Demo")
       demo_content_strategy()
      
       print("\n7. Multi-Agent Workflow Demo")
       results = market_research_workflow("Tesla", "electric vehicles")
       print("Workflow Results:")
       for task, result in results.items():
           print(f"{task}: {str(result)[:150]}...")
          
   except Exception as e:
       print(f" Error during execution: {str(e)}")
       print(" Tip: Make sure your Gemini API key is valid and you have internet connection")


def quick_scrape(url: str, instructions: str = "Extract main content"):
   """Quick scraping function for simple data extraction"""
   with AdvancedNotteAgent(headless=True, max_steps=5) as agent:
       response = agent.agent.run(
           task=f"{instructions} from this webpage",
           url=url
       )
       return response.answer


def quick_search(query: str, num_results: int = 5):
   """Quick search function with structured results"""
   with AdvancedNotteAgent(headless=True, max_steps=10) as agent:
       task = f"Search for '{query}' and return the top {num_results} results with titles, URLs, and brief descriptions."
       response = agent.agent.run(
           task=task,
           url="https://google.com",
           response_format=SearchResult
       )
       return response.answer


def quick_form_fill(form_url: str, form_data: dict):
   """Quick form filling function"""
   with AdvancedNotteAgent(headless=False, max_steps=15) as agent:
       data_str = ", ".join([f"{k}: {v}" for k, v in form_data.items()])
       task = f"Fill out the form with this information: {data_str}, then submit it."
      
       response = agent.agent.run(
           task=task,
           url=form_url
       )
       return response.answer


if __name__ == "__main__":
   print(" Quick Test Examples:")
   print("=" * 30)
  
   print("1. Quick Scrape Example:")
   try:
       result = quick_scrape("https://news.ycombinator.com", "Extract the top 3 post titles")
       print(f"Scraped: {result}")
   except Exception as e:
       print(f"Error: {e}")
  
   print("\n2. Quick Search Example:")
   try:
       search_results = quick_search("latest AI news", 3)
       print(f"Search Results: {search_results}")
   except Exception as e:
       print(f"Error: {e}")
  
   print("\n3. Custom Agent Task:")
   try:
       with AdvancedNotteAgent(headless=True) as agent:
           response = agent.agent.run(
               task="Go to Wikipedia, search for 'artificial intelligence', and summarize the main article in 2 sentences.",
               url="https://wikipedia.org"
           )
           print(f"Wikipedia Summary: {response.answer}")
   except Exception as e:
       print(f"Error: {e}")
  
   main()
  
   print("\n Tutorial Complete!")
   print(" Tips for success:")
   print("- Start with simple tasks and gradually increase complexity")
   print("- Use structured outputs (Pydantic models) for reliable data extraction")
   print("- Implement rate limiting to respect API quotas")
   print("- Handle errors gracefully in production workflows")
   print("- Combine scripting with AI for cost-effective automation")
  
   print("\n Next Steps:")
   print("- Customize the agents for your specific use cases")
   print("- Add error handling and retry logic for production")
   print("- Implement logging and monitoring for agent activities")
   print("- Scale up with Notte's hosted API service for enterprise features")