import os import json import mimetypes import time import logging import pandas as pd import requests import google.generativeai as genai import pypdf from tabulate import tabulate gemini_api_key = "AIzaSyDC5D6SFk4SRlPzBGmXGwQZBtFd5jXr384" ###Use own API key # Configure logging logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") # Altered project path structure # Assuming the script is located in the project root: BASE_DIR = os.path.abspath(os.path.dirname(__file__)) DATA_DIR = os.path.join(BASE_DIR, "data") RESULTS_DIR = os.path.join(BASE_DIR, "results") CSV_PATH = os.path.join(DATA_DIR, "output_new.csv") def extract_text_from_proper_pdf(pdf_path): """ Extracts text from a proper (digitally generated) PDF using pypdf. """ logging.info(f"Extracting text from proper PDF: {pdf_path}") try: with open(pdf_path, "rb") as f: reader = pypdf.PdfReader(f) text = "" for page in reader.pages: page_text = page.extract_text() if page_text: text += page_text if not text: logging.warning(f"No text found in proper PDF: {pdf_path}") return text except Exception as e: logging.error(f"Error extracting text from proper PDF {pdf_path}: {e}") return "" def extract_text_with_gemini_ocr(pdf_path, gemini_api_key): """ Extracts text from a scanned image PDF using Gemini multimodal OCR capabilities. """ logging.info(f"Attempting OCR extraction for scanned PDF: {pdf_path}") try: genai.configure(api_key=gemini_api_key) mime_type, _ = mimetypes.guess_type(pdf_path) if mime_type is None: mime_type = "application/pdf" logging.warning(f"Could not guess MIME type for {pdf_path}, assuming {mime_type}") logging.info(f"Uploading file {pdf_path} with MIME type {mime_type}...") pdf_file = genai.upload_file(path=pdf_path, mime_type=mime_type) logging.info(f"File uploaded successfully: {pdf_file.name}") model = genai.GenerativeModel('gemini-1.5-flash-latest') max_attempts = 30 attempts = 0 while pdf_file.state.name == "PROCESSING" and attempts < max_attempts: print('.', end='', flush=True) time.sleep(10) pdf_file = genai.get_file(pdf_file.name) attempts += 1 if attempts == max_attempts: logging.error("Error: File processing timed out.") return "" if pdf_file.state.name == "FAILED": logging.error(f"Error: File processing failed for {pdf_path}") return "" logging.info("\nFile processed. Sending prompt to Gemini for OCR text extraction...") prompt = "Extract all the text content from the provided document. Preserve formatting like paragraphs and tables as best as possible." response = model.generate_content([prompt, pdf_file]) if response.parts: extracted_text = response.text logging.info(f"OCR text extraction successful (length: {len(extracted_text)} chars).") return extracted_text else: logging.warning("Gemini response contained no parts for text extraction.") logging.debug(f"Full Response: {response}") return "" except Exception as e: logging.error(f"An error occurred during Gemini OCR for {pdf_path}: {e}") return "" def extract_tender_info(extracted_text, gemini_api_key): """ Extracts structured tender information using Gemini API. """ genai.configure(api_key=gemini_api_key) model = genai.GenerativeModel('gemini-1.5-flash-latest') prompt = f""" Okay, here are prompt templates designed to extract the specified data points from State Transport Corporation (STC) tender documents. Each template includes a standardized name, a definition, and a prompt for extraction. ________________________________________Basic Tender Information 1. Name: TenderBasic_Id Prompt: Extract the unique Tender ID. 2. Name: TenderBasic_Title Prompt: Extract the complete Tender Title. 3. Name: TenderBasic_IssuingStu Prompt: Extract the issuing State Transport Undertaking (STU) name. 4. Name: TenderBasic_IssuingDepartment Prompt: Extract the Issuing Department name, if mentioned. 5. Name: TenderBasic_State Prompt: Extract the State of operation. 6. Name: TenderBasic_Type Prompt: Extract the Tender Type. 7. Name: TenderBasic_Category Prompt: Extract the Tender Category. 8. Name: TenderBasic_ProcurementMethod Prompt: Extract the Procurement Method and any bid conditions. ________________________________________Product/Service Details 9. Name: Product_ItemCategory Prompt: Extract the main Item Category. 10. Name: Product_ItemSubcategory Prompt: Extract the Item Subcategory, if specified. 11. Name: Product_ItemCode Prompt: Extract the Item Code(s) or Part Number(s), if available. 12. Name: Product_ItemDescription Prompt: Extract the detailed description for each required item. 13. Name: Product_QuantityRequired Prompt: Extract the Quantity Required for each item. 14. Name: Product_UnitOfMeasurement Prompt: Extract the Unit of Measurement. 15. Name: Product_QualityStandards Prompt: Extract the Quality Standards or Certifications. 16. Name: Product_WarrantyRequirements Prompt: Extract the details of the Warranty Period and Coverage. 17. Name: Product_DeliveryLocation Prompt: Extract the Delivery Location(s). 18. Name: Product_InstallationRequirements Prompt: Extract the installation requirements. ________________________________________Timeline Information 19. Name: Timeline_PublicationDate Prompt: Extract the Tender Publication Date. 20. Name: Timeline_BidSubmissionStartDate Prompt: Extract the Bid Submission Start Date and Time. 21. Name: Timeline_BidSubmissionEndDate Prompt: Extract the Bid Submission End Date and Time. 22. Name: Timeline_BidOpeningDate Prompt: Extract the Bid Opening Date and Time. 23. Name: Timeline_DocDownloadStartDate Prompt: Extract the Document Download Start Date and Time. 24. Name: Timeline_DocDownloadEndDate Prompt: Extract the Document Download End Date and Time. 25. Name: Timeline_PreBidMeetingDate Prompt: Extract the Pre-Bid Meeting Date, Time, and Venue. 26. Name: Timeline_ClarificationDeadline Prompt: Extract the Clarification Submission Deadline. 27. Name: Timeline_ContractAwardDate Prompt: Extract the Contract Award Date, if mentioned. 28. Name: Timeline_DeliveryPeriod Prompt: Extract the Delivery Timeline or Period. ________________________________________Financial Information 29. Name: Financial_EstimatedValue Prompt: Extract the Estimated Contract Value or Cost. 30. Name: Financial_EmdAmount Prompt: Extract the EMD Amount. 31. Name: Financial_EmdExemption Prompt: Extract details about EMD Exemption eligibility. 32. Name: Financial_TenderFee Prompt: Extract the Tender Fee amount and payment method. 33. Name: Financial_TenderFeeExemption Prompt: Extract details about Tender Fee Exemption. 34. Name: Financial_PerformanceSecurity Prompt: Extract the Performance Security details. 35. Name: Financial_PaymentTerms Prompt: Extract the Payment Terms. 36. Name: Financial_PriceRevisionTerms Prompt: Extract the Price Revision Terms. ________________________________________Documentation Requirements 37. Name: Docs_RequiredGeneral Prompt: Extract the list of general technical and financial documents. 38. Name: Docs_RequiredLegal Prompt: Extract the list of legal documents. 39. Name: Docs_RequiredCompliance Prompt: Extract the list of compliance documents. 40. Name: Docs_SubmissionFormat Prompt: Extract the required Format and Method of Submission. ________________________________________Contract Terms 41. Name: Contract_Duration Prompt: Extract the Contract Duration. 42. Name: Contract_ExtensionProvisions Prompt: Extract the Contract Extension provisions. 43. Name: Contract_PenaltyClauses Prompt: Extract the Penalty Clauses. 44. Name: Contract_DisputeResolution Prompt: Extract the Dispute Resolution mechanism. 45. Name: Contract_ForceMajeure Prompt: Extract the Force Majeure conditions. 46. Name: Contract_TerminationConditions Prompt: Extract the Termination Conditions. 47. Name: Contract_ContinuingObligations Prompt: Extract any Continuing Obligations. ________________________________________Contact Information 48. Name: Contact_PersonName Prompt: Extract the Contact Person's name. 49. Name: Contact_PersonDesignation Prompt: Extract the Contact Person's designation. 50. Name: Contact_PhoneNumber Prompt: Extract the Contact Phone Number(s). 51. Name: Contact_Email Prompt: Extract the Contact Email Address(es). 52. Name: Contact_OfficeAddress Prompt: Extract the Tender Office Address. ________________________________________Eligibility and Qualification Criteria 53. Name: Eligibility_BidderNationality Prompt: Extract the required Bidder Nationality. 54. Name: Eligibility_MinAnnualTurnover Prompt: Extract the Minimum Annual Turnover. 55. Name: Eligibility_MinYearsExperience Prompt: Extract the Minimum Years of Experience. 56. Name: Eligibility_SimilarWorkExperience Prompt: Extract the Similar Work Experience requirements. 57. Name: Eligibility_IsoCertification Prompt: Extract any required ISO Certifications. 58. Name: Eligibility_ManufacturingCapacity Prompt: Extract the Manufacturing Capacity requirements. 59. Name: Eligibility_TechnicalCapability Prompt: Extract the required Technical Capabilities. 60. Name: Eligibility_FinancialRatios Prompt: Extract any Financial Ratios requirements. 61. Name: Eligibility_RegistrationRequirements Prompt: Extract the required Registration Requirements. ________________________________________ Tender Document Text: {extracted_text} Provide ONLY the extracted information in valid JSON format, without any introductory text, explanations, or markdown formatting. """ try: response = model.generate_content(prompt) cleaned_text = response.text.strip() if cleaned_text.startswith("```json"): cleaned_text = cleaned_text[7:] if cleaned_text.endswith("```"): cleaned_text = cleaned_text[:-3] cleaned_text = cleaned_text.strip() extracted_data = json.loads(cleaned_text) return extracted_data except json.JSONDecodeError as e: logging.error(f"Error decoding JSON from Gemini response: {e}") logging.debug(f"--- Raw Response Text ---:\n{response.text}\n-------------------------") return {} except Exception as e: logging.error(f"An unexpected error occurred during Gemini processing: {e}") if 'response' in locals() and hasattr(response, 'text'): logging.debug(f"--- Raw Response Text ---:\n{response.text}\n-------------------------") return {} def process_tender_documents(): """ Main function to process tender documents. It reads a CSV file that contains the file path and PDF type (e.g., 'Proper' for digital PDFs or 'Scanned' for image PDFs), extracts text using the appropriate method, and then extracts structured tender information. """ gemini_api_key = input("Enter your Gemini API key: ").strip() os.makedirs(RESULTS_DIR, exist_ok=True) try: df_paths = pd.read_csv(CSV_PATH) # Ensure that a "PDF Type" column exists; if not, default all to "Scanned" if "PDF Type" not in df_paths.columns: df_paths["PDF Type"] = "Scanned Image PDF" file_info = df_paths[["Local PDF File", "PDF Type"]].dropna() file_info = file_info.iloc[:] # Process only the first 10 files except Exception as e: logging.error(f"Error reading CSV file: {e}") return results = [] for index, row in file_info.iterrows(): # Assuming PDF paths in the CSV are relative to the data directory pdf_path = os.path.join(DATA_DIR, os.path.normpath(row["Local PDF File"])) pdf_type = str(row["PDF Type"]).strip().lower() if not os.path.exists(pdf_path): logging.warning(f"File not found: {pdf_path}. Skipping...") continue filename = os.path.basename(pdf_path) logging.info(f"Processing {filename} with PDF type: {pdf_type}...") # Choose extraction method based on PDF type if pdf_type in ["Proper PDF", ""]: extracted_text = extract_text_from_proper_pdf(pdf_path) else: extracted_text = extract_text_with_gemini_ocr(pdf_path, gemini_api_key) # Save extracted text to a .txt file text_filename = os.path.join(RESULTS_DIR, f"{os.path.splitext(filename)[0]}.txt") try: with open(text_filename, 'w', encoding='utf-8') as f: f.write(extracted_text) logging.info(f"Text extracted and saved to {text_filename}") except Exception as e: logging.error(f"Error saving extracted text for {filename}: {e}") # Extract structured tender information logging.info("Extracting tender information using Gemini...") tender_info_dict = extract_tender_info(extracted_text, gemini_api_key) if isinstance(tender_info_dict, dict) and tender_info_dict: tender_info_dict['filename'] = filename results.append(tender_info_dict) logging.info(f"Successfully extracted data for {filename}.") else: logging.warning(f"Could not extract structured data or received empty data for {filename}.") if results: df = pd.DataFrame(results) cols = df.columns.tolist() if 'filename' in cols: cols.remove('filename') cols = ['filename'] + cols df = df[cols] csv_output_path = os.path.join(RESULTS_DIR, "tender_results.csv") try: df.to_csv(csv_output_path, index=False) logging.info(f"Results saved to {csv_output_path}") except Exception as e: logging.error(f"Error saving results to CSV: {e}") print("\nExtracted Tender Information:") print(tabulate(df, headers='keys', tablefmt='grid')) else: logging.info("No results to display.") def main(): process_tender_documents() if __name__ == "__main__": main()