main code
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from firecrawl import FirecrawlApp
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from pydantic import BaseModel, Field
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from typing import Optional
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from exa_py import Exa
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from phi.agent import Agent
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from phi.tools.firecrawl import FirecrawlTools
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from phi.model.openai import OpenAIChat
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# Define a simple schema for testing
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class SimpleSchema(BaseModel):
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title: Optional[str] = Field(description="Title of the webpage.")
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description: Optional[str] = Field(description="Meta description of the webpage.")
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exa = Exa(api_key="50a85")
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# Initialize the FirecrawlApp with your API key
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app = FirecrawlApp(api_key='fc-') # Replace with your API key
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firecrawl_tools = FirecrawlTools(
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api_key="f2",
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scrape=False,
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crawl=True,
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limit=5
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)
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firecrawl_agent = Agent(
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model=OpenAIChat(id="gpt-4o-mini", api_key="s"),
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tools=[firecrawl_tools, ],
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show_tool_calls=True,
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markdown=True
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)
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analysis_agent = Agent(
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model=OpenAIChat(id="gpt-4o-mini", api_key="s"),
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show_tool_calls=True,
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markdown=True
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)
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def get_competitor_urls(url=None, description=None):
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if url:
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result = exa.find_similar(
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url=url,
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excludeDomains=[url],
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num_results=3,
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exclude_source_domain=True,
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category="company"
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)
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elif description:
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result = exa.search(
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description,
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type="neural",
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category="company",
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use_autoprompt=True,
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num_results=3
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)
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else:
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raise ValueError("Please provide either a URL or a description.")
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competitor_urls = [item.url for item in result.results]
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return competitor_urls
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def extract_competitor_info(competitor_url: str):
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try:
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# Use Firecrawl to scrape and extract data
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data = app.scrape_url(competitor_url, {
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'formats': ['extract'],
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'extract': {
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'schema': SimpleSchema.model_json_schema(),
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}
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})
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return data.get("extract", {})
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except Exception as e:
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return {"error": str(e)}
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crawl_response = firecrawl_agent.run(f"Crawl and summarize {competitor_url}")
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crawled_data = crawl_response.content
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structured_info = firecrawl_agent.run(
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f"""Extract the following information from the crawled data:
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- Product pricing and features: Extract exact pricing numbers from their pricing page.
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- Technology stack information
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- Marketing messaging/positioning
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- Customer testimonials/case studies
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- Latest news and developements
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Crawled Data:
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{crawled_data}
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"""
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)
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return {
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"competitor": competitor_url,
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"data": structured_info.content
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}
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except Exception as e:
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return {
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"competitor": competitor_url,
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"error": str(e)
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}
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def generate_analysis_report(competitor_data: list):
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combined_data = "\n\n".join([str(data) for data in competitor_data])
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report = analysis_agent.run(
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f"""Analyze the following competitor data and generate a detailed report:
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{combined_data}
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Tasks:
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1. Compare pricing and identify opportunities for competitive pricing.
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2. Analyze features and highlight unique or missing features.
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3. Evaluate marketing messaging and suggest positioning strategies.
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4. Summarize customer testimonials and case studies.
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5. Provide actionable insights for market positioning.
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"""
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)
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return report.response
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def main():
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competitor_urls = get_competitor_urls(url="https://jenni.ai")
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print(f"Competitor URLs: {competitor_urls}")
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competitor_data = []
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for url in competitor_urls:
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print(f"\nAnalyzing Competitor: {url}")
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competitor_info = extract_competitor_info(url)
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competitor_data.append(competitor_info)
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analysis_report = generate_analysis_report(competitor_data)
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print("\nCompetitor Analysis Report:")
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print(analysis_report)
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# Example usage
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if __name__ == "__main__":
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# Competitor URL to analyze
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competitor_url = "https://www.equal.in" # Replace with your competitor URL
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# Extract competitor information
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competitor_info = extract_competitor_info(competitor_url)
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# Print the structured information
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print(f"Competitor: {competitor_url}")
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print(competitor_info)
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main()
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