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# 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/' | |
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) | |
# Specify a custom model storage directory (ensure this path is writable) | |
#model_storage_directory = '/app/models' | |
# Create the reader object and set the model storage directory | |
#reader = easyocr.Reader(['en'], model_storage_directory=model_storage_directory) | |
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 | |
# Load image using OpenCV | |
def load_image(image_path): | |
image = cv2.imread(image_path) | |
if image is None: | |
raise ValueError(f"Could not load image from {image_path}. It may be corrupted or the path is incorrect.") | |
return image | |
# 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 | |
def ocr_with_paddle(img): | |
finaltext = '' | |
#model_dir = os.getenv('PADDLEOCR_MODEL_DIR', '/tmp/.paddleocr') | |
#ocr = PaddleOCR(lang='en', use_angle_cls=True, det_model_dir=model_dir) | |
#ocr = PaddleOCR(lang='en', use_angle_cls=True, det_model_dir=os.environ['PADDLEOCR_HOME']) | |
logging.info(f"PADDLEOCR_HOME: {os.environ['PADDLEOCR_HOME']}") | |
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') | |
) | |
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): | |
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) | |
# Draw boxes on the processed image (optional, requires bounds) | |
img_result = Image.fromarray(enhanced_image) | |
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) | |
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=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 |