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examples/Add gpt 4.5 web crawler
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Aparup Ganguly
parent
6508afc624
commit
06cdd988a4
261
examples/gpt-4.5-web-crawler/gpt-4.5-crawler.py
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261
examples/gpt-4.5-web-crawler/gpt-4.5-crawler.py
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import os
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from firecrawl import FirecrawlApp
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import json
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from dotenv import load_dotenv
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from openai import OpenAI
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# ANSI color codes
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class Colors:
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CYAN = '\033[96m'
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YELLOW = '\033[93m'
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GREEN = '\033[92m'
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RED = '\033[91m'
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MAGENTA = '\033[95m'
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BLUE = '\033[94m'
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RESET = '\033[0m'
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# Load environment variables
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load_dotenv()
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# Retrieve API keys from environment variables
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firecrawl_api_key = os.getenv("FIRECRAWL_API_KEY")
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openai_api_key = os.getenv("OPENAI_API_KEY")
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# Initialize the FirecrawlApp and OpenAI client
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app = FirecrawlApp(api_key=firecrawl_api_key)
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client = OpenAI(api_key=openai_api_key)
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# Find the page that most likely contains the objective
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def find_relevant_page_via_map(objective, url, app, client):
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try:
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print(f"{Colors.CYAN}Understood. The objective is: {objective}{Colors.RESET}")
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print(f"{Colors.CYAN}Initiating search on the website: {url}{Colors.RESET}")
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map_prompt = f"""
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The map function generates a list of URLs from a website and it accepts a search parameter. Based on the objective of: {objective}, come up with a 1-2 word search parameter that will help us find the information we need. Only respond with 1-2 words nothing else.
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"""
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print(f"{Colors.YELLOW}Analyzing objective to determine optimal search parameter...{Colors.RESET}")
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completion = client.chat.completions.create(
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model="gpt-4.5-preview",
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": map_prompt
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}
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]
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}
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]
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)
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map_search_parameter = completion.choices[0].message.content
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print(f"{Colors.GREEN}Optimal search parameter identified: {map_search_parameter}{Colors.RESET}")
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print(f"{Colors.YELLOW}Mapping website using the identified search parameter...{Colors.RESET}")
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map_website = app.map_url(url, params={"search": map_search_parameter})
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# Debug print to see the response structure
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print(f"{Colors.MAGENTA}Debug - Map response structure: {json.dumps(map_website, indent=2)}{Colors.RESET}")
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print(f"{Colors.GREEN}Website mapping completed successfully.{Colors.RESET}")
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# Handle the response based on its structure
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if isinstance(map_website, dict):
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# Assuming the links are in a 'urls' or similar key
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links = map_website.get('urls', []) or map_website.get('links', [])
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elif isinstance(map_website, str):
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try:
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parsed = json.loads(map_website)
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links = parsed.get('urls', []) or parsed.get('links', [])
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except json.JSONDecodeError:
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links = []
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else:
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links = map_website if isinstance(map_website, list) else []
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if not links:
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print(f"{Colors.RED}No links found in map response.{Colors.RESET}")
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return None
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rank_prompt = f"""
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Given this list of URLs and the objective: {objective}
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Analyze each URL and rank the top 3 most relevant ones that are most likely to contain the information we need.
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Return your response as a JSON array with exactly 3 objects, each containing:
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- "url": the full URL
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- "relevance_score": number between 0-100 indicating relevance to objective
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- "reason": brief explanation of why this URL is relevant
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Example output:
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[
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{{
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"url": "https://example.com/about",
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"relevance_score": 95,
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"reason": "Main about page containing company information"
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}},
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{{
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"url": "https://example.com/team",
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"relevance_score": 80,
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"reason": "Team page with leadership details"
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}},
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{{
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"url": "https://example.com/contact",
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"relevance_score": 70,
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"reason": "Contact page with location information"
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}}
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]
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URLs to analyze:
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{json.dumps(links, indent=2)}
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"""
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print(f"{Colors.YELLOW}Ranking URLs by relevance to objective...{Colors.RESET}")
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completion = client.chat.completions.create(
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model="gpt-4.5-preview",
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": rank_prompt
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}
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]
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}
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]
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)
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try:
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ranked_results = json.loads(completion.choices[0].message.content)
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links = [result["url"] for result in ranked_results]
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# Print detailed ranking info
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print(f"{Colors.CYAN}Top 3 ranked URLs:{Colors.RESET}")
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for result in ranked_results:
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print(f"{Colors.GREEN}URL: {result['url']}{Colors.RESET}")
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print(f"{Colors.YELLOW}Relevance Score: {result['relevance_score']}{Colors.RESET}")
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print(f"{Colors.BLUE}Reason: {result['reason']}{Colors.RESET}")
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print("---")
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if not links:
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print(f"{Colors.RED}No relevant links identified.{Colors.RESET}")
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return None
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except (json.JSONDecodeError, KeyError) as e:
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print(f"{Colors.RED}Error parsing ranked results: {str(e)}{Colors.RESET}")
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return None
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print(f"{Colors.GREEN}Located {len(links)} relevant links.{Colors.RESET}")
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return links
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except Exception as e:
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print(f"{Colors.RED}Error encountered during relevant page identification: {str(e)}{Colors.RESET}")
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return None
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# Scrape the top 3 pages and see if the objective is met, if so return in json format else return None
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def find_objective_in_top_pages(map_website, objective, app, client):
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try:
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# Get top 3 links from the map result
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if not map_website:
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print(f"{Colors.RED}No links found to analyze.{Colors.RESET}")
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return None
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top_links = map_website[:3]
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print(f"{Colors.CYAN}Proceeding to analyze top {len(top_links)} links: {top_links}{Colors.RESET}")
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for link in top_links:
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print(f"{Colors.YELLOW}Initiating scrape of page: {link}{Colors.RESET}")
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# Scrape the page
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scrape_result = app.scrape_url(link, params={'formats': ['markdown']})
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print(f"{Colors.GREEN}Page scraping completed successfully.{Colors.RESET}")
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# Check if objective is met
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check_prompt = f"""
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Given the following scraped content and objective, determine if the objective is met.
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If it is, extract the relevant information in a simple and concise JSON format. Use only the necessary fields and avoid nested structures if possible.
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If the objective is not met with confidence, respond with 'Objective not met'.
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Objective: {objective}
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Scraped content: {scrape_result['markdown']}
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Remember:
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1. Only return JSON if you are confident the objective is fully met.
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2. Keep the JSON structure as simple and flat as possible.
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3. Do not include any explanations or markdown formatting in your response.
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"""
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completion = client.chat.completions.create(
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model="gpt-4.5-preview",
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": check_prompt
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}
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]
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}
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]
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)
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result = completion.choices[0].message.content
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if result != "Objective not met":
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print(f"{Colors.GREEN}Objective potentially fulfilled. Relevant information identified.{Colors.RESET}")
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try:
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# Clean up potential markdown formatting or extra text
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if "```json" in result:
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result = result.split("```json")[1].split("```")[0].strip()
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elif "```" in result:
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result = result.split("```")[1].split("```")[0].strip()
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# Try to find JSON content if there's explanatory text
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if "{" in result and "}" in result:
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start_idx = result.find("{")
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end_idx = result.rfind("}") + 1
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if start_idx >= 0 and end_idx > start_idx:
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result = result[start_idx:end_idx]
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return json.loads(result)
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except json.JSONDecodeError as e:
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print(f"{Colors.RED}Error in parsing response: {str(e)}. Proceeding to next page...{Colors.RESET}")
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# Optionally print the raw response for debugging
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# print(f"{Colors.MAGENTA}Raw response: {result}{Colors.RESET}")
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else:
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print(f"{Colors.YELLOW}Objective not met on this page. Proceeding to next link...{Colors.RESET}")
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print(f"{Colors.RED}All available pages analyzed. Objective not fulfilled in examined content.{Colors.RESET}")
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return None
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except Exception as e:
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print(f"{Colors.RED}Error encountered during page analysis: {str(e)}{Colors.RESET}")
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return None
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# Main function to execute the process
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def main():
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# Get user input
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url = input(f"{Colors.BLUE}Enter the website to crawl : {Colors.RESET}")
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objective = input(f"{Colors.BLUE}Enter your objective: {Colors.RESET}")
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print(f"{Colors.YELLOW}Initiating web crawling process...{Colors.RESET}")
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# Find the relevant page
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map_website = find_relevant_page_via_map(objective, url, app, client)
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if map_website:
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print(f"{Colors.GREEN}Relevant pages identified. Proceeding with detailed analysis using GPT-4.5...{Colors.RESET}")
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# Find objective in top pages
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result = find_objective_in_top_pages(map_website, objective, app, client)
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if result:
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print(f"{Colors.GREEN}Objective successfully fulfilled. Extracted information :{Colors.RESET}")
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print(f"{Colors.MAGENTA}{json.dumps(result, indent=2)}{Colors.RESET}")
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else:
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print(f"{Colors.RED}Unable to fulfill the objective with the available content.{Colors.RESET}")
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else:
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print(f"{Colors.RED}No relevant pages identified. Consider refining the search parameters or trying a different website.{Colors.RESET}")
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if __name__ == "__main__":
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main()
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