import os from firecrawl import FirecrawlApp import json import requests from google.generativeai import types as genai_types from dotenv import load_dotenv import google.generativeai as genai # ANSI color codes class Colors: CYAN = '\033[96m' YELLOW = '\033[93m' GREEN = '\033[92m' RED = '\033[91m' MAGENTA = '\033[95m' BLUE = '\033[94m' RESET = '\033[0m' def is_pdf_url(u: str) -> bool: return u.lower().split('?')[0].endswith('.pdf') def is_image_url(u: str) -> bool: exts = ['.jpg', '.jpeg', '.png', '.gif', '.webp', '.heic', '.heif'] url_no_q = u.lower().split('?')[0] return any(url_no_q.endswith(ext) for ext in exts) def gemini_extract_pdf_content(pdf_url): """ Downloads a PDF from pdf_url, then calls Gemini to extract text. Returns a string with the extracted text only. """ try: pdf_data = requests.get(pdf_url, timeout=15).content model = genai.GenerativeModel('gemini-pro') response = model.generate_content([ genai_types.Part.from_bytes(pdf_data, mime_type='application/pdf'), "Extract all textual information from this PDF. Return only text." ]) return response.text.strip() except Exception as e: print(f"Error using Gemini to process PDF '{pdf_url}': {str(e)}") return "" def gemini_extract_image_data(image_url): """ Downloads an image from image_url, then calls Gemini to: 1) Summarize what's in the image 2) Return bounding boxes for the main objects Returns a string merging the summary and bounding box info. """ try: image_data = requests.get(image_url, timeout=15).content model = genai.GenerativeModel('gemini-pro') # 1) Summarize resp_summary = model.generate_content([ genai_types.Part.from_bytes(image_data, mime_type='image/jpeg'), "Describe the contents of this image in a short paragraph." ]) summary_text = resp_summary.text.strip() # 2) Get bounding boxes resp_bbox = model.generate_content([ genai_types.Part.from_bytes(image_data, mime_type='image/jpeg'), ("Return bounding boxes for the objects in this image in the " "format: [{'object':'cat','bbox':[y_min,x_min,y_max,x_max]}, ...]. " "Coordinates 0-1000. Output valid JSON only.") ]) bbox_text = resp_bbox.text.strip() return f"**Image Summary**:\n{summary_text}\n\n**Bounding Boxes**:\n{bbox_text}" except Exception as e: print(f"Error using Gemini to process Image '{image_url}': {str(e)}") return "" # Load environment variables load_dotenv() # Retrieve API keys from environment variables firecrawl_api_key = os.getenv("FIRECRAWL_API_KEY") gemini_api_key = os.getenv("GEMINI_API_KEY") # Initialize the FirecrawlApp and Gemini client app = FirecrawlApp(api_key=firecrawl_api_key) genai.configure(api_key=gemini_api_key) # Configure Gemini API def find_relevant_page_via_map(objective, url, app): try: print(f"{Colors.CYAN}Understood. The objective is: {objective}{Colors.RESET}") print(f"{Colors.CYAN}Initiating search on the website: {url}{Colors.RESET}") map_prompt = f""" Based on the objective of: {objective}, provide a 1-2 word search parameter that will help find the information. Respond with ONLY 1-2 words, no other text or formatting. """ print( f"{Colors.YELLOW}Analyzing objective to determine optimal search parameter...{Colors.RESET}") # Use gemini-pro instead of gemini-2.0-flash model = genai.GenerativeModel('gemini-pro') response = model.generate_content(map_prompt) map_search_parameter = response.text.strip() print( f"{Colors.GREEN}Optimal search parameter identified: {map_search_parameter}{Colors.RESET}") print( f"{Colors.YELLOW}Mapping website using the identified search parameter...{Colors.RESET}") map_website = app.map_url(url, params={"search": map_search_parameter}) print(f"{Colors.MAGENTA}Debug - Map response structure: {json.dumps(map_website, indent=2)}{Colors.RESET}") print(f"{Colors.GREEN}Website mapping completed successfully.{Colors.RESET}") if isinstance(map_website, dict): links = map_website.get('urls', []) or map_website.get('links', []) elif isinstance(map_website, str): try: parsed = json.loads(map_website) links = parsed.get('urls', []) or parsed.get('links', []) except json.JSONDecodeError: links = [] else: links = map_website if isinstance(map_website, list) else [] if not links: print(f"{Colors.RED}No links found in map response.{Colors.RESET}") return None rank_prompt = f"""RESPOND ONLY WITH JSON. Analyze these URLs and rank the top 3 most relevant ones for finding information about: {objective} Return ONLY a JSON array in this exact format - no other text or explanation: [ {{ "url": "http://example.com", "relevance_score": 95, "reason": "Main about page with company information" }}, {{ "url": "http://example2.com", "relevance_score": 85, "reason": "Team page with details" }}, {{ "url": "http://example3.com", "relevance_score": 75, "reason": "Blog post about company" }} ] URLs to analyze: {json.dumps(links, indent=2)}""" print(f"{Colors.YELLOW}Ranking URLs by relevance to objective...{Colors.RESET}") model = genai.GenerativeModel('gemini-pro') response = model.generate_content(rank_prompt) print(f"{Colors.MAGENTA}Debug - Raw Gemini response:{Colors.RESET}") print(response.text) try: response_text = response.text.strip() print(f"{Colors.MAGENTA}Debug - Cleaned response:{Colors.RESET}") print(response_text) if '[' in response_text and ']' in response_text: start_idx = response_text.find('[') end_idx = response_text.rfind(']') + 1 json_str = response_text[start_idx:end_idx] print( f"{Colors.MAGENTA}Debug - Extracted JSON string:{Colors.RESET}") print(json_str) ranked_results = json.loads(json_str) else: print(f"{Colors.RED}No JSON array found in response{Colors.RESET}") return None links = [result["url"] for result in ranked_results] print(f"{Colors.CYAN}Top 3 ranked URLs:{Colors.RESET}") for result in ranked_results: print(f"{Colors.GREEN}URL: {result['url']}{Colors.RESET}") print( f"{Colors.YELLOW}Relevance Score: {result['relevance_score']}{Colors.RESET}") print(f"{Colors.BLUE}Reason: {result['reason']}{Colors.RESET}") print("---") if not links: print(f"{Colors.RED}No relevant links identified.{Colors.RESET}") return None except json.JSONDecodeError as e: print(f"{Colors.RED}Error parsing ranked results: {str(e)}{Colors.RESET}") print(f"{Colors.RED}Failed JSON string: {response_text}{Colors.RESET}") return None except Exception as e: print(f"{Colors.RED}Unexpected error: {str(e)}{Colors.RESET}") return None print(f"{Colors.GREEN}Located {len(links)} relevant links.{Colors.RESET}") return links except Exception as e: print( f"{Colors.RED}Error encountered during relevant page identification: {str(e)}{Colors.RESET}") return None def find_objective_in_top_pages(map_website, objective, app): try: if not map_website: print(f"{Colors.RED}No links found to analyze.{Colors.RESET}") return None top_links = map_website[:3] print( f"{Colors.CYAN}Proceeding to analyze top {len(top_links)} links: {top_links}{Colors.RESET}") for link in top_links: print(f"{Colors.YELLOW}Initiating scrape of page: {link}{Colors.RESET}") # Include 'links' so we can parse sub-links for PDFs or images scrape_result = app.scrape_url( link, params={'formats': ['markdown', 'links']}) print( f"{Colors.GREEN}Page scraping completed successfully.{Colors.RESET}") # Check sub-links for PDFs or images pdf_image_append = "" sub_links = scrape_result.get('links', []) for sublink in sub_links: if is_pdf_url(sublink): print( f"{Colors.BLUE}Detected PDF in sub-link: {sublink}{Colors.RESET}") extracted_pdf_text = gemini_extract_pdf_content(sublink) if extracted_pdf_text: pdf_image_append += f"\n\n[Sub-link PDF] {sublink}\n{extracted_pdf_text}" elif is_image_url(sublink): print( f"{Colors.BLUE}Detected image in sub-link: {sublink}{Colors.RESET}") extracted_img_text = gemini_extract_image_data(sublink) if extracted_img_text: pdf_image_append += f"\n\n[Sub-link Image] {sublink}\n{extracted_img_text}" # Append extracted PDF/image text to the main markdown for the page if pdf_image_append: scrape_result[ 'markdown'] += f"\n\n---\n**Additional Gemini Extraction:**\n{pdf_image_append}\n" check_prompt = f""" Analyze this content to find: {objective} If found, return ONLY a JSON object with information related to the objective. If not found, respond EXACTLY with: Objective not met Content to analyze: {scrape_result['markdown']} Remember: - Return valid JSON if information is found - Return EXACTLY "Objective not met" if not found - No other text or explanations """ response = genai.GenerativeModel( 'gemini-pro').generate_content(check_prompt) result = response.text.strip() print(f"{Colors.MAGENTA}Debug - Check response:{Colors.RESET}") print(result) if result != "Objective not met": print( f"{Colors.GREEN}Objective potentially fulfilled. Relevant information identified.{Colors.RESET}") try: if '{' in result and '}' in result: start_idx = result.find('{') end_idx = result.rfind('}') + 1 json_str = result[start_idx:end_idx] return json.loads(json_str) else: print( f"{Colors.RED}No JSON object found in response{Colors.RESET}") except json.JSONDecodeError: print( f"{Colors.RED}Error in parsing response. Proceeding to next page...{Colors.RESET}") else: print( f"{Colors.YELLOW}Objective not met on this page. Proceeding to next link...{Colors.RESET}") print(f"{Colors.RED}All available pages analyzed. Objective not fulfilled in examined content.{Colors.RESET}") return None except Exception as e: print( f"{Colors.RED}Error encountered during page analysis: {str(e)}{Colors.RESET}") return None def main(): url = input(f"{Colors.BLUE}Enter the website to crawl : {Colors.RESET}") objective = input(f"{Colors.BLUE}Enter your objective: {Colors.RESET}") print(f"{Colors.YELLOW}Initiating web crawling process...{Colors.RESET}") map_website = find_relevant_page_via_map(objective, url, app) if map_website: print(f"{Colors.GREEN}Relevant pages identified. Proceeding with detailed analysis using gemini-pro...{Colors.RESET}") result = find_objective_in_top_pages(map_website, objective, app) if result: print( f"{Colors.GREEN}Objective successfully fulfilled. Extracted information:{Colors.RESET}") print(f"{Colors.MAGENTA}{json.dumps(result, indent=2)}{Colors.RESET}") else: print( f"{Colors.RED}Unable to fulfill the objective with the available content.{Colors.RESET}") else: print(f"{Colors.RED}No relevant pages identified. Consider refining the search parameters or trying a different website.{Colors.RESET}") if __name__ == "__main__": main()