# mistral.py import os import json import logging from huggingface_hub import InferenceClient from huggingface_hub.utils._errors import BadRequestError from dotenv import load_dotenv from utils.fileTotext import extract_text_based_on_format import re from utils.spacy import Parser_from_model # Load environment variables from .env file load_dotenv() # Authenticate with Hugging Face HFT = os.getenv('HF_TOKEN') if not HFT: raise ValueError("Hugging Face token is not set in environment variables.") client = InferenceClient(model="mistralai/Mistral-Nemo-Instruct-2407", token=HFT) # Function to clean model output def Data_Cleaner(text): pattern = r".*?format:" result = re.split(pattern, text, maxsplit=1) if len(result) > 1: text_after_format = result[1].strip().strip('`').strip('json') else: text_after_format = text.strip().strip('`').strip('json') return text_after_format # Function to call Mistral and process output def Model_ProfessionalDetails_Output(resume, client): system_role = { "role": "system", "content": "You are a skilled resume parser. Your task is to extract professional details from resumes in a structured JSON format defined by the User. Ensure accuracy and completeness while maintaining the format provided and if field are missing just return 'not found'." } user_prompt = { "role": "user", "content": f'''Act as a resume parser for the following text given in text: {resume} Extract the text in the following output JSON string as: {{ "professional": {{ "technical_skills": "List all technical skills, programming languages, frameworks, and technologies mentioned in the resume, ensuring they are not mixed with other skill types.", "non_technical_skills": "Identify and list non-technical skills such as leadership, teamwork, and communication skills, ensuring they are not mixed with technical skills.", "tools": "Enumerate all software tools, platforms, and applications (e.g., Figma, Unity, MS Office, etc.) referenced in the resume, distinctly separate from skills.", "projects": "Extract the names or titles of all projects mentioned in the resume.", "projects_experience": "Summarize overall project experiences, providing a brief description of each project as detailed in the resume.", "experience": "Calculate total professional work experience in years and months based on the resume.", "companies_worked_at": "List the names of all companies where employment is mentioned in the resume.", "certifications": "Extract and list all certifications obtained as stated in the resume.", "roles": "Include the names of all job titles or roles held as indicated in the resume.", "qualifications": "List educational qualifications (e.g., B.Tech) from the resume. If none are found, return 'No education listed'.", "courses": "Extract the names of completed courses based on the resume. If none are found, return 'No courses listed'.", "university": "Identify the name of the university, college, or institute attended, based on the resume. If not found, return 'No university listed'.", "year_of_graduation": "Extract the year of graduation from the resume. If not found, return 'No year of graduation listed'." }} }} Json Output: ''' } response = "" for message in client.chat_completion(messages=[system_role, user_prompt], max_tokens=3000, stream=True, temperature=0.35): response += message.choices[0].delta.content try: clean_response = Data_Cleaner(response) parsed_response = json.loads(clean_response) except json.JSONDecodeError as e: logging.error(f"JSON Decode Error: {e}") return {} return parsed_response def Model_PersonalDetails_Output(resume, client): system_role = { "role": "system", "content": "You are a skilled resume parser. Your task is to extract professional details from resumes in a structured JSON format defined by the User. Ensure accuracy and completeness while maintaining the format provided and if field are missing just return 'not found'." } user_prompt = { "role": "user", "content": f'''Act as a resume parser for the following text given in text: {resume} Extract the text in the following output JSON string as: {{ "personal": {{ "name": "Extract the full name based on the resume. If not found, return 'No name listed'.", "contact_number": "Extract the contact number from the resume. If not found, return 'No contact number listed'.", "email": "Extract the email address from the resume. If not found, return 'No email listed'.", "Address": "Extract the Address or address from the resume. If not found, return 'No Address listed'.", "link": "Extract any relevant links (e.g., portfolio, LinkedIn) from the resume. If not found, return 'No link listed'." }} }} output: ''' } # Response response = "" for message in client.chat_completion( messages=[system_role, user_prompt], max_tokens=3000, stream=True, temperature=0.35, ): response += message.choices[0].delta.content # Handle cases where the response might have formatting issues try: #print('The Og response:-->',response) clean_response=Data_Cleaner(response) #print("After data cleaning",clean_response) parsed_response = json.loads(clean_response) except json.JSONDecodeError as e: print("JSON Decode Error:", e) print("Raw Response:", response) return {} return parsed_response # # Fallback to SpaCy if Mistral fails # Add regex pattern for LinkedIn and GitHub links linkedin_pattern = r"https?://(?:www\.)?linkedin\.com/[\w\-_/]+" github_pattern = r"https?://(?:www\.)?github\.com/[\w\-_/]+" email_pattern = r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$" contact_pattern = r"^\+?[\d\s\-()]{7,15}$" def extract_links(hyperlinks): linkedin_links = [] github_links = [] # Iterate through the hyperlinks and apply regex to find LinkedIn and GitHub links for link in hyperlinks: if re.match(linkedin_pattern, link): linkedin_links.append(link) elif re.match(github_pattern, link): github_links.append(link) return linkedin_links, github_links def is_valid_email(email): email_regex = r'^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$' return re.match(email_regex, email) is not None def is_valid_contact(contact): patterns = [ r'^\+91[\s\.\-\/]?\(?0?\)?[\s\-\.\/]?\d{5}[\s\-\.\/]?\d{5}$', # +91 with optional 0 and separators r'^\+91[\s\.\-\/]?\d{5}[\s\-\.\/]?\d{5}$', # +91 with 10 digits separated r'^\d{5}[\s\-\.\/]?\d{5}$', # Local format without country code r'^\+91[\s\.\-\/]?\d{10}$', # +91 with 10 digits together r'^\d{10}$', # 10 digits together r'^\+91[\s\.\-\/]?\(?\d{5}\)?[\s\-\.\/]?\d{5}[\s\-\.\/]?\d{5}$' # +91 with varying separators r'\+1\s\(\d{3}\)\s\d{3}-\d{4} ', # USA/Canada Intl +1 (XXX) XXX-XXXX r'\(\d{3}\)\s\d{3}-\d{4} ', # USA/Canada STD (XXX) XXX-XXXX r'\(\d{3}\)\s\d{3}\s\d{4} ', # USA/Canada (XXX) XXX XXXX r'\(\d{3}\)\s\d{3}\s\d{3} ', # USA/Canada (XXX) XXX XXX r'\+1\d{10} ', # +1 XXXXXXXXXX r'\d{10} ', # XXXXXXXXXX r'\+44\s\d{4}\s\d{6} ', # UK Intl +44 XXXX XXXXXX r'\+44\s\d{3}\s\d{3}\s\d{4} ', # UK Intl +44 XXX XXX XXXX r'0\d{4}\s\d{6} ', # UK STD 0XXXX XXXXXX r'0\d{3}\s\d{3}\s\d{4} ', # UK STD 0XXX XXX XXXX r'\+44\d{10} ', # +44 XXXXXXXXXX r'0\d{10} ', # 0XXXXXXXXXX r'\+61\s\d\s\d{4}\s\d{4} ', # Australia Intl +61 X XXXX XXXX r'0\d\s\d{4}\s\d{4} ', # Australia STD 0X XXXX XXXX r'\+61\d{9} ', # +61 XXXXXXXXX r'0\d{9} ', # 0XXXXXXXXX r'\+91\s\d{5}-\d{5} ', # India Intl +91 XXXXX-XXXXX r'\+91\s\d{4}-\d{6} ', # India Intl +91 XXXX-XXXXXX r'\+91\s\d{10} ', # India Intl +91 XXXXXXXXXX r'0\d{2}-\d{7} ', # India STD 0XX-XXXXXXX r'\+91\d{10} ', # +91 XXXXXXXXXX r'\+49\s\d{4}\s\d{8} ', # Germany Intl +49 XXXX XXXXXXXX r'\+49\s\d{3}\s\d{7} ', # Germany Intl +49 XXX XXXXXXX r'0\d{3}\s\d{8} ', # Germany STD 0XXX XXXXXXXX r'\+49\d{12} ', # +49 XXXXXXXXXXXX r'\+49\d{10} ', # +49 XXXXXXXXXX r'0\d{11} ', # 0XXXXXXXXXXX r'\+86\s\d{3}\s\d{4}\s\d{4} ', # China Intl +86 XXX XXXX XXXX r'0\d{3}\s\d{4}\s\d{4} ', # China STD 0XXX XXXX XXXX r'\+86\d{11} ', # +86 XXXXXXXXXXX r'\+81\s\d\s\d{4}\s\d{4} ', # Japan Intl +81 X XXXX XXXX r'\+81\s\d{2}\s\d{4}\s\d{4} ', # Japan Intl +81 XX XXXX XXXX r'0\d\s\d{4}\s\d{4} ', # Japan STD 0X XXXX XXXX r'\+81\d{10} ', # +81 XXXXXXXXXX r'\+81\d{9} ', # +81 XXXXXXXXX r'0\d{9} ', # 0XXXXXXXXX r'\+55\s\d{2}\s\d{5}-\d{4} ', # Brazil Intl +55 XX XXXXX-XXXX r'\+55\s\d{2}\s\d{4}-\d{4} ', # Brazil Intl +55 XX XXXX-XXXX r'0\d{2}\s\d{4}\s\d{4} ', # Brazil STD 0XX XXXX XXXX r'\+55\d{11} ', # +55 XXXXXXXXXXX r'\+55\d{10} ', # +55 XXXXXXXXXX r'0\d{10} ', # 0XXXXXXXXXX r'\+33\s\d\s\d{2}\s\d{2}\s\d{2}\s\d{2} ', # France Intl +33 X XX XX XX XX r'0\d\s\d{2}\s\d{2}\s\d{2}\s\d{2} ', # France STD 0X XX XX XX XX r'\+33\d{9} ', # +33 XXXXXXXXX r'0\d{9} ', # 0XXXXXXXXX r'\+7\s\d{3}\s\d{3}-\d{2}-\d{2} ', # Russia Intl +7 XXX XXX-XX-XX r'8\s\d{3}\s\d{3}-\d{2}-\d{2} ', # Russia STD 8 XXX XXX-XX-XX r'\+7\d{10} ', # +7 XXXXXXXXXX r'8\d{10} ', # 8 XXXXXXXXXX r'\+27\s\d{2}\s\d{3}\s\d{4} ', # South Africa Intl +27 XX XXX XXXX r'0\d{2}\s\d{3}\s\d{4} ', # South Africa STD 0XX XXX XXXX r'\+27\d{9} ', # +27 XXXXXXXXX r'0\d{9} ', # 0XXXXXXXXX r'\+52\s\d{3}\s\d{3}\s\d{4} ', # Mexico Intl +52 XXX XXX XXXX r'\+52\s\d{2}\s\d{4}\s\d{4} ', # Mexico Intl +52 XX XXXX XXXX r'01\s\d{3}\s\d{4} ', # Mexico STD 01 XXX XXXX r'\+52\d{10} ', # +52 XXXXXXXXXX r'01\d{7} ', # 01 XXXXXXX r'\+234\s\d{3}\s\d{3}\s\d{4} ', # Nigeria Intl +234 XXX XXX XXXX r'0\d{3}\s\d{3}\s\d{4} ', # Nigeria STD 0XXX XXX XXXX r'\+234\d{10} ', # +234 XXXXXXXXXX r'0\d{10} ', # 0XXXXXXXXXX r'\+971\s\d\s\d{3}\s\d{4} ', # UAE Intl +971 X XXX XXXX r'0\d\s\d{3}\s\d{4} ', # UAE STD 0X XXX XXXX r'\+971\d{8} ', # +971 XXXXXXXX r'0\d{8} ', # 0XXXXXXXX r'\+54\s9\s\d{3}\s\d{3}\s\d{4} ', # Argentina Intl +54 9 XXX XXX XXXX r'\+54\s\d{1}\s\d{4}\s\d{4} ', # Argentina Intl +54 X XXXX XXXX r'0\d{3}\s\d{4} ', # Argentina STD 0XXX XXXX r'\+54\d{10} ', # +54 9 XXXXXXXXXX r'\+54\d{9} ', # +54 XXXXXXXXX r'0\d{7} ', # 0XXXXXXX r'\+966\s\d\s\d{3}\s\d{4} ', # Saudi Intl +966 X XXX XXXX r'0\d\s\d{3}\s\d{4} ', # Saudi STD 0X XXX XXXX r'\+966\d{8} ', # +966 XXXXXXXX r'0\d{8} ', # 0XXXXXXXX r'\+1\d{10} ', # +1 XXXXXXXXXX r'\+1\s\d{3}\s\d{3}\s\d{4} ', # +1 XXX XXX XXXX r'\d{5}\s\d{5} ', # XXXXX XXXXX r'\d{10} ', # XXXXXXXXXX r'\+44\d{10} ', # +44 XXXXXXXXXX r'0\d{10} ', # 0XXXXXXXXXX r'\+61\d{9} ', # +61 XXXXXXXXX r'0\d{9} ', # 0XXXXXXXXX r'\+91\d{10} ', # +91 XXXXXXXXXX r'\+49\d{12} ', # +49 XXXXXXXXXXXX r'\+49\d{10} ', # +49 XXXXXXXXXX r'0\d{11} ', # 0XXXXXXXXXXX r'\+86\d{11} ', # +86 XXXXXXXXXXX r'\+81\d{10} ', # +81 XXXXXXXXXX r'\+81\d{9} ', # +81 XXXXXXXXX r'0\d{9} ', # 0XXXXXXXXX r'\+55\d{11} ', # +55 XXXXXXXXXXX r'\+55\d{10} ', # +55 XXXXXXXXXX r'0\d{10} ', # 0XXXXXXXXXX r'\+33\d{9} ', # +33 XXXXXXXXX r'0\d{9} ', # 0XXXXXXXXX r'\+7\d{10} ', # +7 XXXXXXXXXX r'8\d{10} ', # 8 XXXXXXXXXX r'\+27\d{9} ', # +27 XXXXXXXXX r'0\d{9} ', # 0XXXXXXXXX (South Africa STD) r'\+52\d{10} ', # +52 XXXXXXXXXX r'01\d{7} ', # 01 XXXXXXX r'\+234\d{10} ', # +234 XXXXXXXXXX r'0\d{10} ', # 0XXXXXXXXXX r'\+971\d{8} ', # +971 XXXXXXXX r'0\d{8} ', # 0XXXXXXXX r'\+54\s9\s\d{10} ', # +54 9 XXXXXXXXXX r'\+54\d{9} ', # +54 XXXXXXXXX r'0\d{7} ', # 0XXXXXXX r'\+966\d{8} ', # +966 XXXXXXXX r'0\d{8}' # 0XXXXXXXX ] # Check if the contact matches any of the patterns return any(re.match(pattern, contact) for pattern in patterns) is not None def validate_contact_email(personal_data): contact = personal_data.get('contact', 'Not found') email = personal_data.get('email', 'Not found') valid_contact = is_valid_contact(contact) if contact != 'Not found' else False valid_email = is_valid_email(email) if email != 'Not found' else False invalid_contact = 'Invalid contact' if not valid_contact else 'Valid contact' invalid_email = 'Invalid email' if not valid_email else 'Valid email' return valid_contact, invalid_contact, valid_email, invalid_email def process_resume_data(file_path): resume_text, hyperlinks = extract_text_based_on_format(file_path) print("Resume converted to text successfully.") if not resume_text: return {"error": "Text extraction failed"} # Extract LinkedIn and GitHub links linkedin_links, github_links = extract_links(hyperlinks) # Attempt to use Mistral model for parsing try: # Extract personal details using Mistral per_data = Model_PersonalDetails_Output(resume_text, client) # Extract professional details using Mistral pro_data = Model_ProfessionalDetails_Output(resume_text, client) # Check if per_data and pro_data have been populated correctly if not per_data: logging.warning("Mistral personal data extraction failed.") per_data = {} if not pro_data: logging.warning("Mistral professional data extraction failed.") pro_data = {} # Combine both personal and professional details into a structured output result = { "personal": { "name": per_data.get('personal', {}).get('name', 'Not found'), "contact": per_data.get('personal', {}).get('contact_number', 'Not found'), "email": per_data.get('personal', {}).get('email', 'Not found'), "location": per_data.get('personal', {}).get('Address', 'Not found'), "linkedin": linkedin_links, "github": github_links, "other_links": hyperlinks # Store remaining links if needed }, "professional": { "technical_skills": pro_data.get('professional', {}).get('technical_skills', 'Not found'), "non_technical_skills": pro_data.get('professional', {}).get('non_technical_skills', 'Not found'), "tools": pro_data.get('professional', {}).get('tools', 'Not found'), "experience": [ { "company": pro_data.get('professional', {}).get('companies_worked_at', 'Not found'), "projects": pro_data.get('professional', {}).get('projects', 'Not found'), "role": pro_data.get('professional', {}).get('worked_as', 'Not found'), "years": pro_data.get('professional', {}).get('experience', 'Not found'), "project_experience": pro_data.get('professional', {}).get('projects_experience', 'Not found') } ], "education": [ { "qualification": pro_data.get('professional', {}).get('qualification', 'Not found'), "university": pro_data.get('professional', {}).get('university', 'Not found'), "course": pro_data.get('professional', {}).get('course', 'Not found'), "certificate": pro_data.get('professional', {}).get('certification', 'Not found') } ] } } # Validate contact and email valid_contact, invalid_contact, valid_email, invalid_email = validate_contact_email(result['personal']) result['personal']['valid_contact'] = valid_contact result['personal']['invalid_contact'] = invalid_contact result['personal']['valid_email'] = valid_email result['personal']['invalid_email'] = invalid_email # If Mistral produces valid output, return it if per_data or pro_data: logging.info("Successfully extracted data using Mistral.") print("---------Mistral-------") return result else: raise ValueError("Mistral returned no output") # Handle HuggingFace API or Mistral model errors except BadRequestError as e: logging.error(f"HuggingFace API error: {e}. Falling back to SpaCy.") print(f"HuggingFace API error: {e}. Falling back to SpaCy.") except Exception as e: logging.error(f"An error occurred while processing with Mistral: {e}. Falling back to SpaCy.") print(f"An error occurred while processing with Mistral: {e}. Falling back to SpaCy.") # Fallback to SpaCy if Mistral fails logging.warning("Mistral failed, switching to SpaCy.") print("---------SpaCy-------") return Parser_from_model(file_path)