# libraries import os from huggingface_hub import InferenceClient from dotenv import load_dotenv import json import re #import easyocr from PIL import Image, ImageEnhance, ImageDraw import cv2 import numpy as np from paddleocr import PaddleOCR import logging # Configure logging logging.basicConfig( level=logging.INFO, handlers=[ logging.StreamHandler() # Remove FileHandler and log only to the console ] ) # Set the PaddleOCR home directory to a writable location os.environ['PADDLEOCR_HOME'] = '/tmp/.paddleocr' RESULT_FOLDER = 'static/results/' JSON_FOLDER = 'static/json/' if not os.path.exists('/tmp/.paddleocr'): os.makedirs(RESULT_FOLDER, exist_ok=True) # Check if PaddleOCR home directory is writable if not os.path.exists('/tmp/.paddleocr'): os.makedirs('/tmp/.paddleocr', exist_ok=True) logging.info("Created PaddleOCR home directory.") else: logging.info("PaddleOCR home directory exists.") # Load environment variables from .env file load_dotenv() # Authenticate with Hugging Face HFT = os.getenv('HF_TOKEN') # Initialize the InferenceClient client = InferenceClient(model="mistralai/Mistral-7B-Instruct-v0.3", token=HFT) # Load image using OpenCV def load_image(image_path): ext = os.path.splitext(image_path)[1].lower() if ext in ['.png', '.jpg', '.jpeg', '.webp', '.tiff']: image = cv2.imread(image_path) return image else: raise ValueError(f"Could not load image from {image_path}. It may be corrupted or the path is incorrect. or in a not supported format") # Function for upscaling image using OpenCV's INTER_CUBIC def upscale_image(image, scale=2): height, width = image.shape[:2] upscaled_image = cv2.resize(image, (width * scale, height * scale), interpolation=cv2.INTER_CUBIC) return upscaled_image # Function to denoise the image (reduce noise) def reduce_noise(image): return cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21) # Function to sharpen the image def sharpen_image(image): kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]]) sharpened_image = cv2.filter2D(image, -1, kernel) return sharpened_image # Function to increase contrast and enhance details without changing color def enhance_image(image): pil_img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) enhancer = ImageEnhance.Contrast(pil_img) enhanced_image = enhancer.enhance(1.5) enhanced_image_bgr = cv2.cvtColor(np.array(enhanced_image), cv2.COLOR_RGB2BGR) return enhanced_image_bgr # Complete function to process image def process_image(image_path, scale=2): # Load the image image = load_image(image_path) # Upscale the image upscaled_image = upscale_image(image, scale) # Reduce noise denoised_image = reduce_noise(upscaled_image) # Sharpen the image sharpened_image = sharpen_image(denoised_image) # Enhance the image contrast and details without changing color final_image = enhance_image(sharpened_image) return final_image # Function for OCR with PaddleOCR, returning both text and bounding boxes def ocr_with_paddle(img): final_text = '' boxes = [] # Initialize PaddleOCR ocr = PaddleOCR( lang='en', use_angle_cls=True, det_model_dir=os.path.join(os.environ['PADDLEOCR_HOME'], 'whl/det'), rec_model_dir=os.path.join(os.environ['PADDLEOCR_HOME'], 'whl/rec/en/en_PP-OCRv4_rec_infer'), cls_model_dir=os.path.join(os.environ['PADDLEOCR_HOME'], 'whl/cls/ch_ppocr_mobile_v2.0_cls_infer') ) # Check if img is a file path or an image array if isinstance(img, str): img = cv2.imread(img) # Perform OCR result = ocr.ocr(img) # Iterate through the OCR result for line in result[0]: # Check how many values are returned (2 or 3) and unpack accordingly if len(line) == 3: box, (text, confidence), _ = line # When 3 values are returned elif len(line) == 2: box, (text, confidence) = line # When only 2 values are returned # Store the recognized text and bounding boxes final_text += ' ' + text # Extract the text from the tuple boxes.append(box) # Draw the bounding box points = [(int(point[0]), int(point[1])) for point in box] cv2.polylines(img, [np.array(points)], isClosed=True, color=(0, 255, 0), thickness=2) # Store the image with bounding boxes in a variable img_with_boxes = img return final_text, img_with_boxes def extract_text_from_images(image_paths): all_extracted_texts = {} all_extracted_imgs = {} for image_path in image_paths: try: # Enhance the image before OCR enhanced_image = process_image(image_path, scale=2) # Perform OCR on the enhanced image and get boxes result, img_with_boxes = ocr_with_paddle(enhanced_image) # Draw bounding boxes on the processed image img_result = Image.fromarray(enhanced_image) #img_with_boxes = draw_boxes(img_result, boxes) # Save the image with boxes result_image_path = os.path.join(RESULT_FOLDER, f'result_{os.path.basename(image_path)}') #img_with_boxes.save(result_image_path) cv2.imwrite(result_image_path, img_with_boxes) # Store the text and image result paths all_extracted_texts[image_path] = result all_extracted_imgs[image_path] = result_image_path except ValueError as ve: print(f"Error processing image {image_path}: {ve}") continue # Continue to the next image if there's an error # Convert to JSON-compatible structure all_extracted_imgs_json = {str(k): str(v) for k, v in all_extracted_imgs.items()} return all_extracted_texts, all_extracted_imgs_json # Function to call the Gemma model and process the output as Json def Data_Extractor(data, client=client): text = f'''Act as a Text extractor for the following text given in text: {data} Extract text in the following output JSON string: {{ "Name": ["Identify and Extract All the person's name from the text."], "Designation": ["Extract All the designation or job title mentioned in the text."], "Company": ["Extract All the company or organization name if mentioned."], "Contact": ["Extract All phone number, including country codes if present."], "Address": ["Extract All the full postal address or location mentioned in the text."], "Email": ["Identify and Extract All valid email addresses mentioned in the text else 'Not found'."], "Link": ["Identify and Extract any website URLs or social media links present in the text."] }} Output: ''' # Call the API for inference response = client.text_generation(text, max_new_tokens=1000, temperature=0.4, top_k=50, top_p=0.9, repetition_penalty=1.2) print("parse in text ---:",response) # Convert the response text to JSON try: json_data = json.loads(response) print("Json_data-------------->",json_data) return json_data except json.JSONDecodeError as e: return {"error": f"Error decoding JSON: {e}"} # For have text compatible to the llm def json_to_llm_str(textJson): str='' for file,item in textJson.items(): str+=item + ' ' return str # Define the RE for extracting the contact details like number, mail , portfolio, website etc def extract_contact_details(text): # Regex patterns # Phone numbers with at least 5 digits in any segment combined_phone_regex = re.compile(r''' (?: #(?:(?:\+91[-.\s]?)?\d{5}[-.\s]?\d{5})|(?:\+?\d{1,3})?[-.\s()]?\d{5,}[-.\s()]?\d{5,}[-.\s()]?\d{1,9} | /^[\.-)( ]*([0-9]{3})[\.-)( ]*([0-9]{3})[\.-)( ]*([0-9]{4})$/ | \+1\s\(\d{3}\)\s\d{3}-\d{4} | # USA/Canada Intl +1 (XXX) XXX-XXXX \(\d{3}\)\s\d{3}-\d{4} | # USA/Canada STD (XXX) XXX-XXXX \(\d{3}\)\s\d{3}\s\d{4} | # USA/Canada (XXX) XXX XXXX \(\d{3}\)\s\d{3}\s\d{3} | # USA/Canada (XXX) XXX XXX \+1\d{10} | # +1 XXXXXXXXXX \d{10} | # XXXXXXXXXX \+44\s\d{4}\s\d{6} | # UK Intl +44 XXXX XXXXXX \+44\s\d{3}\s\d{3}\s\d{4} | # UK Intl +44 XXX XXX XXXX 0\d{4}\s\d{6} | # UK STD 0XXXX XXXXXX 0\d{3}\s\d{3}\s\d{4} | # UK STD 0XXX XXX XXXX \+44\d{10} | # +44 XXXXXXXXXX 0\d{10} | # 0XXXXXXXXXX \+61\s\d\s\d{4}\s\d{4} | # Australia Intl +61 X XXXX XXXX 0\d\s\d{4}\s\d{4} | # Australia STD 0X XXXX XXXX \+61\d{9} | # +61 XXXXXXXXX 0\d{9} | # 0XXXXXXXXX \+91\s\d{5}-\d{5} | # India Intl +91 XXXXX-XXXXX \+91\s\d{4}-\d{6} | # India Intl +91 XXXX-XXXXXX \+91\s\d{10} | # India Intl +91 XXXXXXXXXX \+91\s\d{3}\s\d{3}\s\d{4} | # India Intl +91 XXX XXX XXXX \+91\s\d{3}-\d{3}-\d{4} | # India Intl +91 XXX-XXX-XXXX \+91\s\d{2}\s\d{4}\s\d{4} | # India Intl +91 XX XXXX XXXX \+91\s\d{2}-\d{4}-\d{4} | # India Intl +91 XX-XXXX-XXXX \+91\s\d{5}\s\d{5} | # India Intl +91 XXXXX XXXXX \d{5}\s\d{5} | # India XXXXX XXXXX \d{5}-\d{5} | # India XXXXX-XXXXX 0\d{2}-\d{7} | # India STD 0XX-XXXXXXX \+91\d{10} | # +91 XXXXXXXXXX \d{10} | # XXXXXXXXXX # Here is the regex to handle all possible combination of the contact \d{6}-\d{4} | # XXXXXX-XXXX \d{4}-\d{6} | # XXXX-XXXXXX \d{3}\s\d{3}\s\d{4} | # XXX XXX XXXX \d{3}-\d{3}-\d{4} | # XXX-XXX-XXXX \d{4}\s\d{3}\s\d{3} | # XXXX XXX XXX \d{4}-\d{3}-\d{3} | # XXXX-XXX-XXX #----- \+49\s\d{4}\s\d{8} | # Germany Intl +49 XXXX XXXXXXXX \+49\s\d{3}\s\d{7} | # Germany Intl +49 XXX XXXXXXX 0\d{3}\s\d{8} | # Germany STD 0XXX XXXXXXXX \+49\d{12} | # +49 XXXXXXXXXXXX \+49\d{10} | # +49 XXXXXXXXXX 0\d{11} | # 0XXXXXXXXXXX \+86\s\d{3}\s\d{4}\s\d{4} | # China Intl +86 XXX XXXX XXXX 0\d{3}\s\d{4}\s\d{4} | # China STD 0XXX XXXX XXXX \+86\d{11} | # +86 XXXXXXXXXXX \+81\s\d\s\d{4}\s\d{4} | # Japan Intl +81 X XXXX XXXX \+81\s\d{2}\s\d{4}\s\d{4} | # Japan Intl +81 XX XXXX XXXX 0\d\s\d{4}\s\d{4} | # Japan STD 0X XXXX XXXX \+81\d{10} | # +81 XXXXXXXXXX \+81\d{9} | # +81 XXXXXXXXX 0\d{9} | # 0XXXXXXXXX \+55\s\d{2}\s\d{5}-\d{4} | # Brazil Intl +55 XX XXXXX-XXXX \+55\s\d{2}\s\d{4}-\d{4} | # Brazil Intl +55 XX XXXX-XXXX 0\d{2}\s\d{4}\s\d{4} | # Brazil STD 0XX XXXX XXXX \+55\d{11} | # +55 XXXXXXXXXXX \+55\d{10} | # +55 XXXXXXXXXX 0\d{10} | # 0XXXXXXXXXX \+33\s\d\s\d{2}\s\d{2}\s\d{2}\s\d{2} | # France Intl +33 X XX XX XX XX 0\d\s\d{2}\s\d{2}\s\d{2}\s\d{2} | # France STD 0X XX XX XX XX \+33\d{9} | # +33 XXXXXXXXX 0\d{9} | # 0XXXXXXXXX \+7\s\d{3}\s\d{3}-\d{2}-\d{2} | # Russia Intl +7 XXX XXX-XX-XX 8\s\d{3}\s\d{3}-\d{2}-\d{2} | # Russia STD 8 XXX XXX-XX-XX \+7\d{10} | # +7 XXXXXXXXXX 8\d{10} | # 8 XXXXXXXXXX \+27\s\d{2}\s\d{3}\s\d{4} | # South Africa Intl +27 XX XXX XXXX 0\d{2}\s\d{3}\s\d{4} | # South Africa STD 0XX XXX XXXX \+27\d{9} | # +27 XXXXXXXXX 0\d{9} | # 0XXXXXXXXX \+52\s\d{3}\s\d{3}\s\d{4} | # Mexico Intl +52 XXX XXX XXXX \+52\s\d{2}\s\d{4}\s\d{4} | # Mexico Intl +52 XX XXXX XXXX 01\s\d{3}\s\d{4} | # Mexico STD 01 XXX XXXX \+52\d{10} | # +52 XXXXXXXXXX 01\d{7} | # 01 XXXXXXX \+234\s\d{3}\s\d{3}\s\d{4} | # Nigeria Intl +234 XXX XXX XXXX 0\d{3}\s\d{3}\s\d{4} | # Nigeria STD 0XXX XXX XXXX \+234\d{10} | # +234 XXXXXXXXXX 0\d{10} | # 0XXXXXXXXXX \+971\s\d\s\d{3}\s\d{4} | # UAE Intl +971 X XXX XXXX 0\d\s\d{3}\s\d{4} | # UAE STD 0X XXX XXXX \+971\d{8} | # +971 XXXXXXXX 0\d{8} | # 0XXXXXXXX \+54\s9\s\d{3}\s\d{3}\s\d{4} | # Argentina Intl +54 9 XXX XXX XXXX \+54\s\d{1}\s\d{4}\s\d{4} | # Argentina Intl +54 X XXXX XXXX 0\d{3}\s\d{4} | # Argentina STD 0XXX XXXX \+54\d{10} | # +54 9 XXXXXXXXXX \+54\d{9} | # +54 XXXXXXXXX 0\d{7} | # 0XXXXXXX \+966\s\d\s\d{3}\s\d{4} | # Saudi Intl +966 X XXX XXXX 0\d\s\d{3}\s\d{4} | # Saudi STD 0X XXX XXXX \+966\d{8} | # +966 XXXXXXXX 0\d{8} | # 0XXXXXXXX \+1\d{10} | # +1 XXXXXXXXXX \+1\s\d{3}\s\d{3}\s\d{4} | # +1 XXX XXX XXXX \d{5}\s\d{5} | # XXXXX XXXXX \d{10} | # XXXXXXXXXX \+44\d{10} | # +44 XXXXXXXXXX 0\d{10} | # 0XXXXXXXXXX \+61\d{9} | # +61 XXXXXXXXX 0\d{9} | # 0XXXXXXXXX \+91\d{10} | # +91 XXXXXXXXXX \+49\d{12} | # +49 XXXXXXXXXXXX \+49\d{10} | # +49 XXXXXXXXXX 0\d{11} | # 0XXXXXXXXXXX \+86\d{11} | # +86 XXXXXXXXXXX \+81\d{10} | # +81 XXXXXXXXXX \+81\d{9} | # +81 XXXXXXXXX 0\d{9} | # 0XXXXXXXXX \+55\d{11} | # +55 XXXXXXXXXXX \+55\d{10} | # +55 XXXXXXXXXX 0\d{10} | # 0XXXXXXXXXX \+33\d{9} | # +33 XXXXXXXXX 0\d{9} | # 0XXXXXXXXX \+7\d{10} | # +7 XXXXXXXXXX 8\d{10} | # 8 XXXXXXXXXX \+27\d{9} | # +27 XXXXXXXXX 0\d{9} | # 0XXXXXXXXX (South Africa STD) \+52\d{10} | # +52 XXXXXXXXXX 01\d{7} | # 01 XXXXXXX \+234\d{10} | # +234 XXXXXXXXXX 0\d{10} | # 0XXXXXXXXXX \+971\d{8} | # +971 XXXXXXXX 0\d{8} | # 0XXXXXXXX \+54\s9\s\d{10} | # +54 9 XXXXXXXXXX \+54\d{9} | # +54 XXXXXXXXX 0\d{7} | # 0XXXXXXX \+966\d{8} | # +966 XXXXXXXX 0\d{8} # 0XXXXXXXX \+\d{3}-\d{3}-\d{4} ) ''',re.VERBOSE) # Email regex email_regex = re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b') # URL and links regex, updated to avoid conflicts with email domains link_regex = re.compile(r'\b(?:https?:\/\/)?(?:www\.)[a-zA-Z0-9-]+\.(?:com|co\.in|co|io|org|net|edu|gov|mil|int|uk|us|in|de|au|app|tech|xyz|info|biz|fr|dev)\b') # Find all matches in the text phone_numbers = [num for num in combined_phone_regex.findall(text) if len(num) >= 5] emails = email_regex.findall(text) links_RE = [link for link in link_regex.findall(text) if len(link)>=11] # Remove profile links that might conflict with emails links_RE = [link for link in links_RE if not any(email in link for email in emails)] return { "phone_numbers": phone_numbers, "emails": emails, "links_RE": links_RE } # preprocessing the data def process_extracted_text(extracted_text): # Load JSON data data = json.dumps(extracted_text, indent=4) data = json.loads(data) # Create a single dictionary to hold combined results combined_results = { "phone_numbers": [], "emails": [], "links_RE": [] } # Process each text entry for filename, text in data.items(): contact_details = extract_contact_details(text) # Extend combined results with the details from this file combined_results["phone_numbers"].extend(contact_details["phone_numbers"]) combined_results["emails"].extend(contact_details["emails"]) combined_results["links_RE"].extend(contact_details["links_RE"]) # Convert the combined results to JSON #combined_results_json = json.dumps(combined_results, indent=4) combined_results_json = combined_results # Print the final JSON results print("Combined contact details in JSON format:") print(combined_results_json) return combined_results_json # Function to remove duplicates (case-insensitive) from each list in the dictionary def remove_duplicates_case_insensitive(data_dict): for key, value_list in data_dict.items(): seen = set() unique_list = [] for item in value_list: if item.lower() not in seen: unique_list.append(item) # Add original item (preserving its case) seen.add(item.lower()) # Track lowercase version # Update the dictionary with unique values data_dict[key] = unique_list return data_dict # Process the model output for parsed result def process_resume_data(LLMdata,cont_data,extracted_text): # Removing duplicate emails unique_emails = [] for email in cont_data['emails']: if not any(email.lower() == existing_email.lower() for existing_email in LLMdata['Email']): unique_emails.append(email) # Removing duplicate links (case insensitive) unique_links = [] for link in cont_data['links_RE']: if not any(link.lower() == existing_link.lower() for existing_link in LLMdata['Link']): unique_links.append(link) # Removing duplicate phone numbers normalized_contact = [num[-10:] for num in LLMdata['Contact']] unique_numbers = [] for num in cont_data['phone_numbers']: if num[-10:] not in normalized_contact: unique_numbers.append(num) # Add unique emails, links, and phone numbers to the original LLMdata LLMdata['Email'] += unique_emails LLMdata['Link'] += unique_links LLMdata['Contact'] += unique_numbers # Apply the function to the data LLMdata=remove_duplicates_case_insensitive(LLMdata) # Initialize the processed data dictionary processed_data = { "name": [], "contact_number": [], "Designation":[], "email": [], "Location": [], "Link": [], "Company":[], "extracted_text": extracted_text } #LLM processed_data['name'].extend(LLMdata.get('Name', [])) #processed_data['contact_number'].extend(LLMdata.get('Contact', [])) processed_data['Designation'].extend(LLMdata.get('Designation', [])) #processed_data['email'].extend(LLMdata.get("Email", [])) processed_data['Location'].extend(LLMdata.get('Address', [])) #processed_data['Link'].extend(LLMdata.get('Link', [])) processed_data['Company'].extend(LLMdata.get('Company', [])) #Contact #processed_data['email'].extend(cont_data.get("emails", [])) #processed_data['contact_number'].extend(cont_data.get("phone_numbers", [])) #processed_data['Link'].extend(cont_data.get("links_RE", [])) #New_merge_data processed_data['email'].extend(LLMdata['Email']) processed_data['contact_number'].extend(LLMdata['Contact']) processed_data['Link'].extend(LLMdata['Link']) return processed_data