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| import json | |
| import os | |
| import requests | |
| import streamlit as st | |
| from datetime import datetime | |
| from docx import Document | |
| from io import BytesIO | |
| from PyPDF2 import PdfReader, PdfWriter | |
| # Consider using a more robust environment variable loading mechanism for production | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| # ------------------------------------------------------------------------------ | |
| # 🌟 ULTIMATE AI-POWERED IMPORT OS - "ResumeForge Pro" 🌟 | |
| # Unleash the Power of AI for Career Advancement! | |
| # ------------------------------------------------------------------------------ | |
| # This is not just an import statement, it's a portal to career empowerment. | |
| # It combines cutting-edge AI, meticulous resume craftsmanship, and an intuitive UI. | |
| # ============================================================================== | |
| # 🚀 CONFIGURATION - API KEYS, ENDPOINTS, & MODEL SELECTION 🚀 | |
| # ============================================================================== | |
| # Ensure your GROQ_API key is securely stored. For development, use environment variables. | |
| # For production, consider secrets management solutions. | |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY") # Added correct env variable name | |
| # Define the API endpoint for the Groq service. This provides flexibility | |
| # to switch to different Groq deployments if needed. | |
| GROQ_ENDPOINT = "https://api.groq.com/openai/v1/chat/completions" | |
| # Choose the most appropriate Llama 3.3 model variant for your needs. | |
| # "llama-3.3-70b-versatile" offers a balance of capabilities, but other | |
| # variants might be better suited for specific tasks or resource constraints. | |
| GROQ_MODEL = "llama-3.3-70b-versatile" | |
| # ============================================================================== | |
| # 🔥 API HANDLER FOR GROQ AI - ROBUST AND RESILIENT 🛡️ | |
| # ============================================================================== | |
| def call_groq_api(messages, temperature=0.7, max_tokens=1024, top_p=0.95, frequency_penalty=0.0, presence_penalty=0.0): | |
| """ | |
| Handles API calls to Groq's Llama 3.3 model with comprehensive error handling, | |
| parameter tuning, and advanced logging. | |
| Args: | |
| messages (list): A list of message dictionaries, representing the conversation history. | |
| Each dictionary should have 'role' (e.g., 'system', 'user', 'assistant') | |
| and 'content' keys. | |
| temperature (float): Controls the randomness of the output (0.0 - deterministic, 1.0 - very random). | |
| Lower values are suitable for tasks requiring precision (e.g., resume formatting), | |
| while higher values can be used for creative tasks (e.g., cover letter generation). | |
| Defaults to 0.7. | |
| max_tokens (int): The maximum number of tokens to generate in the response. Adjust this | |
| based on the expected length of the output to balance completeness and cost. | |
| Defaults to 1024. | |
| top_p (float): Nucleus sampling parameter. Similar to temperature but controls the cumulative | |
| probability of the tokens to sample from. A higher value allows for more | |
| diverse outputs. Defaults to 0.95. | |
| frequency_penalty (float): Penalizes new tokens based on their existing frequency in the text so far. | |
| Encourages the model to use novel words and phrases. Defaults to 0.0. | |
| presence_penalty (float): Penalizes new tokens that already appear in the text so far, regardless of frequency. | |
| Discourages the model from repeating the same topics or ideas. Defaults to 0.0. | |
| Returns: | |
| str: The generated text content from the API response, or None if the API call fails. | |
| """ | |
| headers = { | |
| "Authorization": f"Bearer {GROQ_API_KEY}", | |
| "Content-Type": "application/json" | |
| } | |
| payload = { | |
| "model": GROQ_MODEL, | |
| "messages": messages, | |
| "temperature": temperature, | |
| "max_tokens": max_tokens, | |
| "top_p": top_p, | |
| "frequency_penalty": frequency_penalty, | |
| "presence_penalty": presence_penalty | |
| } | |
| try: | |
| st.sidebar.write(f"**API Request Payload:**\n\n{json.dumps(payload, indent=2)}") # Log the API payload | |
| response = requests.post(GROQ_ENDPOINT, headers=headers, json=payload, timeout=25) # Increased timeout for potentially long requests | |
| response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) | |
| response_json = response.json() | |
| st.sidebar.write(f"**API Response:**\n\n{json.dumps(response_json, indent=2)}") # Log the entire response | |
| return response_json["choices"][0]["message"]["content"].strip() | |
| except requests.exceptions.RequestException as e: | |
| st.error(f"❌ API Request Failed: {e} - Please ensure your API key is valid and Groq's service is operational.") | |
| st.error(f"Detailed Error: {e}") # Provide a detailed error message | |
| return None | |
| except KeyError as e: | |
| st.error(f"❌ Error parsing API response: Missing key {e}. Check the API response format.") | |
| return None | |
| except json.JSONDecodeError as e: | |
| st.error(f"❌ Error decoding JSON response: {e}. The API might be returning invalid JSON.") | |
| return None | |
| except Exception as e: | |
| st.error(f"❌ An unexpected error occurred: {e}") | |
| return None | |
| finally: | |
| st.sidebar.write("--- API Request Complete ---") # Add a clear marker for request completion | |
| # ============================================================================== | |
| # 📄 FILE PROCESSING - PDF, DOCX, TXT - WITH EXTENSIVE VALIDATION 🛡️ | |
| # ============================================================================== | |
| def extract_resume_text(file_obj): | |
| """ | |
| Extracts text from uploaded resumes (PDF, DOCX, TXT) with robust error handling, | |
| file type validation, and size limitations. | |
| Args: | |
| file_obj (streamlit.UploadedFile): The uploaded file object. | |
| Returns: | |
| str: The extracted text from the resume, or None if extraction fails. | |
| """ | |
| MAX_FILE_SIZE_MB = 5 # Set a reasonable file size limit | |
| try: | |
| file_bytes = file_obj.read() | |
| file_size_mb = len(file_bytes) / (1024 * 1024) | |
| if file_size_mb > MAX_FILE_SIZE_MB: | |
| st.error(f"❌ File size exceeds the limit of {MAX_FILE_SIZE_MB} MB. Please upload a smaller file.") | |
| return None | |
| file_obj.seek(0) # Reset file pointer to the beginning | |
| ext = os.path.splitext(file_obj.name)[-1].lower() | |
| if ext == ".pdf": | |
| try: | |
| reader = PdfReader(BytesIO(file_bytes)) # Use BytesIO to handle in-memory file | |
| text = "\n".join(page.extract_text() for page in reader.pages if page.extract_text()) | |
| return text | |
| except Exception as e: | |
| st.error(f"❌ Error extracting text from PDF: {e}") | |
| return None | |
| elif ext in [".docx", ".doc"]: | |
| try: | |
| document = Document(BytesIO(file_bytes)) | |
| text = "\n".join([para.text for para in document.paragraphs]) | |
| return text | |
| except Exception as e: | |
| st.error(f"❌ Error extracting text from DOCX: {e}") | |
| return None | |
| elif ext == ".txt": | |
| try: | |
| return file_bytes.decode("utf-8", errors="ignore") # Handle encoding issues | |
| except Exception as e: | |
| st.error(f"❌ Error decoding text file: {e}") | |
| return None | |
| else: | |
| st.warning(f"⚠️ Unsupported file type: {ext}. Please upload a PDF, DOCX, or TXT file.") | |
| return None | |
| except Exception as e: | |
| st.error(f"❌ An unexpected error occurred during file processing: {e}") | |
| return None | |
| # ============================================================================== | |
| # 🎯 AI-POWERED RESUME GENERATION - ATS OPTIMIZED & STRUCTURED 🚀 | |
| # ============================================================================== | |
| def generate_resume(first_name, last_name, location, work_experience, school_experience, skills, contact_number, email_address, linkedin_profile): | |
| """ | |
| Generates a structured, ATS-friendly resume with enhanced formatting, | |
| detailed instructions to the AI, and incorporates contact information. | |
| Args: | |
| first_name (str): The candidate's first name. | |
| last_name (str): The candidate's last name. | |
| location (str): The candidate's location. | |
| work_experience (str): A detailed description of work experience. | |
| school_experience (str): Details of educational background. | |
| skills (str): A comma-separated list of skills. | |
| contact_number (str): Candidate's phone number. | |
| email_address (str): Candidate's email address. | |
| linkedin_profile (str): Candidate's LinkedIn profile URL. | |
| Returns: | |
| str: The generated resume text. | |
| """ | |
| candidate_data = json.dumps({ | |
| "full_name": f"{first_name} {last_name}", # Added full_name | |
| "location": location, | |
| "contact_number": contact_number, # Include contact_number | |
| "email_address": email_address, # Include email_address | |
| "linkedin_profile": linkedin_profile, # Include linkedin_profile | |
| "work_experience": work_experience, | |
| "school_experience": school_experience, | |
| "skills": skills, | |
| }, indent=2) | |
| messages = [ | |
| {"role": "system", "content": """ | |
| You are an expert resume writer with extensive experience in Applicant Tracking Systems (ATS) optimization and modern resume design principles. | |
| Your goal is to create a compelling and highly effective resume that showcases the candidate's strengths and experiences in a clear, concise, and ATS-friendly format. | |
| You are an expert in using bullet points to quantify experience with metrics and achievements. | |
| You will focus on extracting information given in work experience, education, and skills to accurately tailor the resume | |
| """}, | |
| {"role": "user", "content": f""" | |
| Generate a **highly professional**, **ATS-optimized resume** using the following structured information. Focus on: | |
| * **Quantifiable Achievements:** Emphasize results and use numbers/metrics wherever possible. | |
| * **Keywords:** Incorporate industry-specific keywords naturally. | |
| * **Action Verbs:** Start each bullet point with strong action verbs. | |
| * **Conciseness:** Keep sentences short and to the point. | |
| * **Use the info provided and do not make things up.** | |
| {candidate_data} | |
| **Resume Structure Requirements:** | |
| 1. **Contact Information:** | |
| * Full Name (prominently displayed) | |
| * Phone Number | |
| * Email Address | |
| * LinkedIn Profile URL (if provided, ensure it's valid) | |
| * Location (City, State - no full address) | |
| 2. **Summary/Profile:** | |
| * A brief (3-4 sentence) summary highlighting key skills, experience, and career goals. Tailor this to be generic enough to apply to several positions. | |
| 3. **Work Experience:** | |
| * Job Title | |
| * Company Name | |
| * Dates of Employment (Month Year - Month Year) | |
| * **Responsibilities and Achievements:** Use bullet points to describe responsibilities and, most importantly, **quantifiable achievements**. | |
| * Example: "Increased sales by 25% in Q3 by implementing a new marketing strategy." | |
| * Example: "Reduced customer support tickets by 15% by improving the onboarding process." | |
| * Order experiences from most recent to oldest. | |
| 4. **Education:** | |
| * Degree Name | |
| * Major (if applicable) | |
| * University Name | |
| * Graduation Date (Month Year) or Expected Graduation Date | |
| * Include relevant coursework or academic achievements (optional). | |
| 5. **Skills:** | |
| * List both technical and soft skills relevant to the candidate's field. | |
| * Categorize skills (e.g., "Technical Skills," "Soft Skills," "Programming Languages"). | |
| 6. **Certifications/Awards (Optional):** | |
| * List any relevant certifications or awards. | |
| **Formatting Guidelines:** | |
| * Use a clean and professional font (e.g., Arial, Calibri, Times New Roman) – Size 10-12. | |
| * Use clear headings and subheadings. | |
| * Use white space effectively to improve readability. | |
| * Maintain consistent formatting throughout the document. | |
| * Adhere to a one-page or two-page limit, depending on the candidate's experience level. | |
| * Make sure to include enough information so the bot can create a great resume. | |
| **IMPORTANT:** The resume MUST be ATS-compatible. Avoid using tables, columns, or graphics that can confuse ATS systems. | |
| **Tone:** Professional, confident, and results-oriented. | |
| """} | |
| ] | |
| return call_groq_api(messages, temperature=0.4, max_tokens=2048) # Lower temperature for more precision | |
| # ============================================================================== | |
| # ✍️ AI-POWERED COVER LETTER GENERATION - PERSUASIVE & TAILORED 🚀 | |
| # ============================================================================== | |
| def generate_cover_letter(candidate_json, job_description, hiring_manager_name="Hiring Manager"): | |
| """ | |
| Generates a compelling cover letter using structured candidate details and | |
| a provided job description. | |
| Args: | |
| candidate_json (str): A JSON string containing candidate information (name, skills, experience). | |
| job_description (str): The text of the job description. | |
| hiring_manager_name (str, optional): The name of the hiring manager. Defaults to "Hiring Manager". | |
| Returns: | |
| str: The generated cover letter text. | |
| """ | |
| date_str = datetime.today().strftime("%d %B %Y") | |
| messages = [ | |
| {"role": "system", "content": """ | |
| You are a world-class career advisor, adept at crafting highly persuasive and tailored cover letters. | |
| Your cover letters are known for their ability to capture the attention of hiring managers and showcase the candidate's | |
| unique qualifications and enthusiasm for the role. You are familiar with best practices in cover letter writing, | |
| including the importance of tailoring the content to the specific job description and highlighting measurable achievements. | |
| """}, | |
| {"role": "user", "content": f""" | |
| Generate a **compelling and professional cover letter** using the following information: | |
| **Candidate Profile:** {candidate_json} | |
| **Job Description:** {job_description} | |
| **Date:** {date_str} | |
| **Hiring Manager Name (Salutation):** {hiring_manager_name} | |
| **Cover Letter Requirements:** | |
| 1. **Opening Paragraph (Introduction):** | |
| * Start with a strong opening that grabs the reader's attention. | |
| * Clearly state the position you are applying for and where you saw the job posting. | |
| * Express your enthusiasm for the opportunity. | |
| 2. **Body Paragraphs (Skills and Experience Alignment):** | |
| * Highlight 2-3 key skills or experiences that directly align with the requirements outlined in the job description. | |
| * Provide specific examples of how you have demonstrated these skills and achieved measurable results in previous roles. | |
| * Quantify your achievements whenever possible (e.g., "Increased sales by 20%," "Reduced costs by 15%"). | |
| * Showcase your understanding of the company and the role. | |
| 3. **Closing Paragraph (Call to Action):** | |
| * Reiterate your interest in the position and your enthusiasm for the opportunity. | |
| * Express your confidence in your ability to contribute to the company's success. | |
| * Thank the hiring manager for their time and consideration. | |
| * Include a call to action, such as requesting an interview. | |
| **Formatting Guidelines:** | |
| * Use a professional and easy-to-read font (e.g., Arial, Calibri, Times New Roman). | |
| * Keep the cover letter concise and focused (ideally, no more than one page). | |
| * Use proper grammar and spelling. | |
| * Address the cover letter to the hiring manager by name, if possible. | |
| * If the hiring manager's name is not available, use "Dear Hiring Manager." | |
| **Tone:** Enthusiastic, confident, professional, and tailored. | |
| """} | |
| ] | |
| return call_groq_api(messages, temperature=0.5, max_tokens=1536) # Adjusted temperature for persuasion | |
| # ============================================================================== | |
| # 💾 SAVE FUNCTION | |
| # ============================================================================== | |
| def save_text_as_file(text, filename, file_format): | |
| """Saves the generated text as a downloadable file in specified format.""" | |
| try: | |
| if file_format == "txt": | |
| # UTF-8 encoding to support special characters. | |
| data = text.encode('utf-8') | |
| st.download_button( | |
| label=f"Download {filename}.txt", | |
| data=data, | |
| file_name=f"{filename}.txt", | |
| mime="text/plain", | |
| help="Click to download the generated text as a plain text file.", | |
| ) | |
| elif file_format == "docx": | |
| document = Document() | |
| document.add_paragraph(text) | |
| # Write docx to a BytesIO object for downloading. | |
| buffer = BytesIO() | |
| document.save(buffer) | |
| buffer.seek(0) # Go to the beginning of the buffer so it can be read. | |
| st.download_button( | |
| label=f"Download {filename}.docx", | |
| data=buffer, | |
| file_name=f"{filename}.docx", | |
| mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document", | |
| help="Click to download the generated text as a Word document (.docx).", | |
| ) | |
| elif file_format == "pdf": | |
| # Creating PDF from the given text. | |
| from reportlab.lib.pagesizes import letter | |
| from reportlab.pdfgen import canvas | |
| # Buffer to hold the PDF in memory | |
| buffer = BytesIO() | |
| # Create the PDF object, using the buffer as its "file." | |
| c = canvas.Canvas(buffer, pagesize=letter) | |
| # Set font and size | |
| c.setFont('Helvetica', 12) | |
| textobject = c.beginText() | |
| textobject.setTextOrigin(50, 750) | |
| # Split the text into lines | |
| lines = text.splitlines() | |
| # Setting line height | |
| line_height = 14 | |
| # Write each line of text | |
| for line in lines: | |
| textobject.textLine(line.strip()) | |
| textobject.moveCursor(0, -line_height) # Adjust line height to move down a bit | |
| # Finish up | |
| c.drawText(textobject) | |
| c.showPage() | |
| c.save() | |
| # Move the buffer position to the beginning | |
| buffer.seek(0) | |
| st.download_button( | |
| label=f"Download {filename}.pdf", | |
| data=buffer, | |
| file_name=f"{filename}.pdf", | |
| mime="application/pdf", | |
| help="Click to download the generated text as a PDF document.", | |
| ) | |
| else: | |
| st.error("Unsupported file format. Please choose txt, docx, or pdf.") | |
| except Exception as e: | |
| st.error(f"Error generating download: {e}") | |
| # ============================================================================== | |
| # 🎨 STREAMLIT UI DESIGN - USER-FRIENDLY & RESPONSIVE 📱 | |
| # ============================================================================== | |
| def main(): | |
| st.set_page_config(page_title="AI Resume & Cover Letter Generator", layout="wide") | |
| st.title("🚀 AI-Powered Resume & Cover Letter Generator") | |
| st.markdown("### Create a **perfect ATS-friendly Resume** and **tailored Cover Letter** using **Groq AI (Llama 3.3-70B)**") | |
| # Sidebar for API key and advanced settings | |
| with st.sidebar: | |
| st.header("⚙️ Advanced Settings") | |
| api_key_input = st.text_input("Groq API Key", type="password", value=GROQ_API_KEY if GROQ_API_KEY else "", help="Enter your Groq API key. Keep it safe!", key="api_key_input") | |
| if api_key_input: | |
| os.environ["GROQ_API_KEY"] = api_key_input # Correct way to set/update env var | |
| model_temperature = st.slider("Model Temperature", min_value=0.0, max_value=1.0, value=0.5, step=0.05, help="Adjust the randomness of the AI's output. Lower values for more precise output.") | |
| model_max_tokens = st.slider("Max Tokens", min_value=256, max_value=4096, value=1024, step=128, help="The maximum number of tokens in the generated output.") | |
| top_p = st.slider("Top P", min_value=0.0, max_value=1.0, value=0.95, step=0.05, help="Nucleus sampling parameter. Controls the cumulative probability of the tokens to sample from.") | |
| frequency_penalty = st.slider("Frequency Penalty", min_value=-2.0, max_value=2.0, value=0.0, step=0.1, help="Penalizes new tokens based on their existing frequency.") | |
| presence_penalty = st.slider("Presence Penalty", min_value=-2.0, max_value=2.0, value=0.0, step=0.1, help="Penalizes new tokens that already appear in the text.") | |
| st.markdown("---") | |
| st.markdown("💡 **Tip:** Adjust these settings to fine-tune the AI's performance. Lower temperature for resumes, higher for cover letters.") | |
| tabs = st.tabs(["📄 Cover Letter Generator", "📑 Resume Creator"]) | |
| # ----- COVER LETTER GENERATOR ----- | |
| with tabs[0]: | |
| st.header("📄 Cover Letter Generator") | |
| resume_file = st.file_uploader("Upload Your Resume", type=["pdf", "docx", "txt"], help="Upload your resume in PDF, DOCX, or TXT format.") | |
| job_description = st.text_area("Paste Job Description", height=200, help="Paste the job description from the job posting.") | |
| hiring_manager_name = st.text_input("Hiring Manager Name (Optional)", value="Hiring Manager", help="Enter the name of the hiring manager if known.") | |
| if st.button("🔹 Generate Cover Letter"): | |
| if not os.getenv("GROQ_API_KEY"): | |
| st.error("Please enter your Groq API key in the sidebar.") | |
| return | |
| if resume_file and job_description.strip(): | |
| with st.spinner("✨ Processing resume..."): | |
| resume_text = extract_resume_text(resume_file) | |
| if resume_text: | |
| # Dummy data, consider extracting dynamically from resume. | |
| candidate_json = json.dumps({ | |
| "first_name": "Jane", | |
| "last_name": "Doe", | |
| "location": "San Francisco, CA", | |
| "work_experience": "Experienced Software Engineer with a proven track record of success.", | |
| "school_experience": "Master's Degree in Computer Science from Stanford University", | |
| "skills": "Python, Java, Machine Learning, Cloud Computing" | |
| }) | |
| cover_letter = generate_cover_letter(candidate_json, job_description, hiring_manager_name) | |
| if cover_letter: | |
| st.success("✅ Cover Letter Generated!") | |
| st.text_area("📜 Your Cover Letter:", cover_letter, height=300) | |
| filename = "cover_letter" | |
| file_format = st.selectbox("Select download format:", ["txt", "docx", "pdf"]) | |
| save_text_as_file(cover_letter, filename, file_format) | |
| else: | |
| st.warning("⚠️ Please upload a resume and paste the job description.") | |
| # ----- RESUME CREATOR ----- | |
| with tabs[1]: | |
| st.header("📑 Resume Creator") | |
| with st.form("resume_form"): | |
| col1, col2 = st.columns(2) | |
| first_name = col1.text_input("First Name", help="Enter your first name.", value="Oluwafemi") # Pre-filled value | |
| last_name = col2.text_input("Last Name", help="Enter your last name.", value="Idiakhoa") # Pre-filled value | |
| location = st.text_input("Location", help="Enter your city and state.", value="Houston, Texas") # Pre-filled value | |
| contact_number = st.text_input("Contact Number", help="Enter your phone number.", value="404-922-0516") # Pre-filled value | |
| email_address = st.text_input("Email Address", help="Enter your email address.", value="[email protected]") # Pre-filled value | |
| linkedin_profile = st.text_input("LinkedIn Profile URL", help="Enter your LinkedIn profile URL (optional).") | |
| work_experience = st.text_area("Work Experience", height=150, help="Describe your work experience, including job titles, company names, dates of employment, and responsibilities/achievements.", value="Verizon feb-2023") # Pre-filled value | |
| school_experience = st.text_area("Education", height=150, help="Describe your educational background, including degree names, university names, and graduation dates.", value="Master in Engineering") # Pre-filled value | |
| skills = st.text_area("Skills", height=100, help="List your skills, separated by commas.") | |
| submit = st.form_submit_button("📑 Generate Resume") | |
| if submit: | |
| if not os.getenv("GROQ_API_KEY"): | |
| st.error("Please enter your Groq API key in the sidebar.") | |
| return | |
| with st.spinner("📝 Creating Resume..."): | |
| resume_text = generate_resume(first_name, last_name, location, work_experience, school_experience, skills, contact_number, email_address, linkedin_profile) | |
| if resume_text: | |
| st.success("✅ Resume Generated!") | |
| st.text_area("📜 Your Resume:", resume_text, height=400) | |
| filename = f"{first_name}_{last_name}_resume" | |
| file_format = st.selectbox("Select download format:", ["txt", "docx", "pdf"]) | |
| save_text_as_file(resume_text, filename, file_format) | |
| if __name__ == "__main__": | |
| main() |