# libraries import os from huggingface_hub import InferenceClient from dotenv import load_dotenv import json import re import easyocr import spacy from PIL import Image, ImageEnhance, ImageDraw import cv2 import numpy as np from paddleocr import PaddleOCR # 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) # Initialize EasyOCR reader for extracting text reader = easyocr.Reader(['en']) # Initialize spaCy's English model nlp = spacy.load("en_core_web_sm") def draw_boxes(image, bounds, color='red', width=2): draw = ImageDraw.Draw(image) for bound in bounds: p0, p1, p2, p3 = bound[0] draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width) return image #Image Quality upscaling # Load image using OpenCV def load_image(image_path): return cv2.imread(image_path) # Function for upscaling image using OpenCV's INTER_CUBIC or ESRGAN (if available) def upscale_image(image, scale=2): height, width = image.shape[:2] # Simple upscaling using cubic interpolation 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): # Convert from BGR to RGB for PIL processing, then back to BGR pil_img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) enhancer = ImageEnhance.Contrast(pil_img) enhanced_image = enhancer.enhance(1.5) # Convert back to BGR 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 def ocr_with_paddle(img): finaltext = '' ocr = PaddleOCR(lang='en', use_angle_cls=True) # img_path = 'exp.jpeg' result = ocr.ocr(img) for i in range(len(result[0])): text = result[0][i][1][0] finaltext += ' '+ text return finaltext def extract_text_from_images(image_paths, RESULT_FOLDER): all_extracted_texts = {} all_extracted_imgs={} for image_path in image_paths: # Enhance the image before OCR #enhanced_image = load_image(image_path) enhanced_image = process_image(image_path, scale=2) bounds = reader.readtext(enhanced_image) # Draw boxes on the processed image img_result = Image.fromarray(enhanced_image) draw_boxes(img_result, bounds) result_image_path = os.path.join(RESULT_FOLDER, f'result_{os.path.basename(image_path)}') img_result.save(result_image_path) # Save the processed image # Perform OCR on the enhanced image result=ocr_with_paddle(enhanced_image) # results = reader.readtext(enhanced_image) # extracted_text = " ".join([res[1] for res in results]) all_extracted_texts[image_path] =result all_extracted_imgs[image_path] = result_image_path # 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=600)#, 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) 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 0\d{2}-\d{7} | # India STD 0XX-XXXXXXX \+91\d{10} | # +91 XXXXXXXXXX \+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') # Profile links regex, updated to avoid conflicts with email domains #link_regex = re.compile(r'\b(?:https?://)?(?:www\.)?(?:linkedin\.com|github\.com|indeed\.com|[A-Za-z0-9-]+\.[A-Za-z]{2,})[\w./?-]*\b') #link_regex = re.compile(r'\b(?:https?://)?(?:www\.)?[a-zA-Z0-9-]+\.(?:[a-zA-Z]{2,})(?:\.[a-zA-Z]{2,})?(?:\.[a-zA-Z]{2,})?(?:[/\w.-]*)*[\w/?&=-]*\b') 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] print("phone_numbers--->",phone_numbers) 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 # Process the model output for parsed result def process_resume_data(LLMdata,cont_data,extracted_text): # Initialize the processed data dictionary processed_data = { "name": [LLMdata.get('Name', 'Not found')], "contact_number": [LLMdata.get('Contact', 'Not found')], "Designation":[LLMdata.get('Designation', 'Not found')], "email": [LLMdata.get("Email", 'Not found')], "Location": [LLMdata.get('Address', 'Not found')], "Link": [LLMdata.get('Link', 'Not found')], "Company":[LLMdata.get('Company', 'Not found')], "extracted_text": extracted_text } 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", [])) return processed_data