Create app.py

app.py

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+import os
+import gradio as gr
+from openai import OpenAI
+from PyPDF2 import PdfReader
+from docx import Document
+import re
+
+# Securely get the API key from environment variables
+api_key = os.getenv("OPENAI_API_KEY")
+base_url = "https://api.aimlapi.com/v1"
+llama_model = "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo"
+
+# Initialize the OpenAI API
+api = OpenAI(api_key=api_key, base_url=base_url)
+
+# Function to extract text from a PDF file
+def extract_text_from_pdf(pdf_file):
+    reader = PdfReader(pdf_file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to extract user details from CV (e.g., Name, Contact Info, etc.)
+def extract_user_details(cv_text):
+    lines = cv_text.splitlines()
+    name = lines[0] if len(lines) > 0 else "Name Not Found"
+    title = lines[1] if len(lines) > 1 else "Title Not Found"
+    contact_info = lines[2] if len(lines) > 2 else "Contact Info Not Found"
+    
+    return name, title, contact_info
+
+# Function to match and regenerate CV content
+def regenerate_cv(cv_text, job_description):
+    system_prompt = "You are a world class pro CV writing assistant."
+    user_prompt = (f"Here is a CV: {cv_text}\n\n"
+                   f"And here is the job description: {job_description}\n\n"
+                   "Please regenerate the CV according to the job description, "
+                   "fill the gaps, add all sufficient skills that match the job description and provide it in a professional US template that has the highest possible ATS score.")
+
+    completion = api.chat.completions.create(
+        model=llama_model,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+        temperature=0.7,
+        max_tokens=4096,  # Increase max_tokens to handle larger outputs
+    )
+
+    response = completion.choices[0].message.content
+    return response
+
+# Function to generate the interview preparation note
+def generate_interview_note(cv_text, updated_cv_text):
+    system_prompt = "You are a world class pro interview preparation assistant."
+    user_prompt = (f"Here is the original CV: {cv_text}\n\n"
+                   f"Here is the updated CV: {updated_cv_text}\n\n"
+                   "Identify the changes made and provide a list of topics the candidate should prepare for the interview based on the updates in a very concise and professional manner with proper headings and bullet points.")
+
+    completion = api.chat.completions.create(
+        model=llama_model,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+        temperature=0.7,
+        max_tokens=1024,
+    )
+
+    response = completion.choices[0].message.content
+    return response
+
+# Function to calculate ATS score
+def calculate_ats_score(cv_text, job_description):
+    job_keywords = re.findall(r'\b\w+\b', job_description.lower())
+    cv_keywords = re.findall(r'\b\w+\b', cv_text.lower())
+    
+    matching_keywords = set(job_keywords) & set(cv_keywords)
+    ats_score = len(matching_keywords) / len(set(job_keywords)) * 100
+    return round(ats_score, 2)
+
+# Function to create and save the regenerated CV as DOCX (formatted)
+def create_formatted_cv(cv_text, file_name="Updated_CV.docx"):
+    doc = Document()
+
+    # Extract user details (name, title, contact info)
+    name, title, contact_info = extract_user_details(cv_text)
+
+    # Adding CV content with formatting
+    doc.add_heading(name, level=1)
+    doc.add_paragraph(title)
+    doc.add_paragraph(contact_info)
+
+    # Split the CV into sections
+    sections = cv_text.split('\n\n')
+
+    # Define headings for each section based on the template
+    headings = ["ACHIEVEMENTS", "EDUCATION", "SKILLS", "WORK EXPERIENCE", "VOLUNTEER EXPERIENCE", "HACKATHON PROJECTS", "WORKSHOPS AND WEBINARS"]
+
+    for section in sections:
+        for heading in headings:
+            if heading in section.upper():
+                doc.add_heading(heading, level=2)
+        doc.add_paragraph(section)
+
+    # Save the document
+    doc.save(file_name)
+    return file_name
+
+# Function to process the CV and generate outputs
+def process_cv(cv_pdf, job_description):
+    # Extract text from the uploaded CV PDF
+    cv_text = extract_text_from_pdf(cv_pdf)
+
+    # Calculate initial ATS score
+    initial_ats_score = calculate_ats_score(cv_text, job_description)
+
+    # Regenerate the CV
+    updated_cv_text = regenerate_cv(cv_text, job_description)
+
+    # Calculate updated ATS score
+    updated_ats_score = calculate_ats_score(updated_cv_text, job_description)
+
+    # Generate interview preparation note
+    interview_note = generate_interview_note(cv_text, updated_cv_text)
+
+    # Create and save the formatted CV
+    formatted_cv_path = create_formatted_cv(updated_cv_text)
+
+    # Create the interview notes file
+    interview_notes_path = "Interview_Notes.txt"
+    with open(interview_notes_path, "w") as f:
+        f.write(interview_note)
+    
+    return updated_cv_text, initial_ats_score, updated_ats_score, interview_note, formatted_cv_path, interview_notes_path
+
+# Define the Gradio app
+def app_interface():
+    with gr.Blocks() as interface:
+        gr.Markdown("### **HireFit** By team Mixed Intelligence")
+
+        # Short description
+        gr.Markdown("""
+        **Version 1.0**
+
+        This project takes your CV and job description, then provides a new CV optimized for the specific job description. It also highlights gaps in your CV and provides detailed interview preparation notes to help you succeed.
+        """)
+
+        with gr.Row():
+            # Left panel
+            with gr.Column(scale=1):
+                gr.Markdown("### Upload and Generate")
+                cv_pdf = gr.File(label="Upload Your CV (PDF)")
+                job_description = gr.Textbox(label="Paste Job Description")
+                process_btn = gr.Button("Regenerate CV", elem_id="process_btn")
+
+            # Middle panel
+            with gr.Column(scale=2):
+                gr.Markdown("### Regenerated CV and Interview Notes")
+                updated_cv_text = gr.Textbox(label="Regenerated CV", lines=20, interactive=False)
+                interview_note = gr.Textbox(label="Interview Preparation Notes", lines=10, interactive=False)
+
+            # Right panel
+            with gr.Column(scale=1):
+                gr.Markdown("### ATS Scores")
+                ats_score_before = gr.Number(label="Original ATS Score", value=0)
+                ats_score_after = gr.Number(label="Updated ATS Score", value=0)
+
+                # Adding download buttons to the right panel
+                download_cv_btn = gr.File(label="Download Updated CV", elem_id="download_cv_btn")
+                download_notes_btn = gr.File(label="Download Interview Notes", elem_id="download_notes_btn")
+
+        # Button click event
+        process_btn.click(
+            fn=process_cv,
+            inputs=[cv_pdf, job_description],
+            outputs=[updated_cv_text, ats_score_before, ats_score_after, interview_note, download_cv_btn, download_notes_btn]
+        )
+
+    return interface
+
+if __name__ == "__main__":
+    app_interface().launch()