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app.py

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+# from flask import Flask, request, jsonify, render_template
+# import pdfplumber
+# import io
+# from transformers import T5ForConditionalGeneration, T5Tokenizer
+# import torch
+
+# app = Flask(__name__)
+
+# # Load the T5 model and tokenizer
+# model_name = "t5-large"
+# tokenizer = T5Tokenizer.from_pretrained(model_name)
+# model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# @app.route('/')
+# def index():
+#     return render_template('index.html')  # Ensure index.html exists in templates/
+
+# @app.route('/upload-resume', methods=['POST'])
+# def upload_resume():
+#     try:
+#         file = request.files['resume']
+#         if not file.filename.endswith('.pdf'):
+#             return jsonify({"error": "Only PDF files are supported"}), 400
+
+#         # Extract text from PDF
+#         text = ""
+#         with pdfplumber.open(io.BytesIO(file.read())) as pdf:
+#             for page in pdf.pages:
+#                 page_text = page.extract_text()
+#                 if page_text:
+#                     text += page_text + "\n"
+
+#         # Create a prompt dynamically based on resume content
+#         prompt = f"Generate 5 technical and 3 behavioral interview questions based on this resume:\n{text.strip()}"
+
+#         # Tokenize and generate questions
+#         inputs = tokenizer(prompt, return_tensors="pt", max_length=1024, truncation=True)
+#         output = model.generate(**inputs, max_length=512, num_beams=4, early_stopping=True)
+
+#         questions = tokenizer.decode(output[0], skip_special_tokens=True)
+#         questions_list = [q.strip() for q in questions.split("\n") if q.strip()]
+
+#         return jsonify(questions_list)
+
+#     except Exception as e:
+#         print("Error:", e)
+#         return jsonify({"error": str(e)}), 500
+
+# if __name__ == '__main__':
+#     app.run(debug=True)
+
+
+
+
+from flask import Flask, request, jsonify, render_template, send_from_directory
+import pdfplumber
+import io
+import re
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+import spacy
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import torch
+import logging
+from collections import Counter
+import os
+
+
+os.environ["TRANSFORMERS_CACHE"] = os.path.join(os.getcwd(), "models")
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, 
+                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+app = Flask(__name__, static_folder='static')
+
+# Download required NLTK data
+try:
+    nltk.download('punkt', quiet=True)
+    nltk.download('stopwords', quiet=True)
+    stop_words = set(stopwords.words('english'))
+except Exception as e:
+    logger.warning(f"NLTK download error: {str(e)}")
+    stop_words = set()
+
+# Load spaCy model for entity recognition
+try:
+    nlp = spacy.load("en_core_web_sm")
+except Exception as e:
+    logger.warning(f"SpaCy model loading error: {str(e)}")
+    
+    # Optional fallback if spaCy model isn't installed
+    def download_spacy_model():
+        import subprocess
+        subprocess.call(["python", "-m", "spacy", "download", "en_core_web_sm"])
+        
+    try:
+        download_spacy_model()
+        nlp = spacy.load("en_core_web_sm")
+    except:
+        logger.error("Failed to load spaCy model")
+        nlp = None
+
+# Load GPT-2 model and tokenizer for better text generation
+try:
+    model_name = "distilgpt2"  
+    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+    model = GPT2LMHeadModel.from_pretrained(model_name)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+except Exception as e:
+    logger.error(f"Model loading error: {str(e)}")
+    model = None
+    tokenizer = None
+
+def extract_skills(text):
+    """Extract technical skills from resume text"""
+    # Common technical skills to look for
+    common_skills = {
+        'programming': ['python', 'java', 'javascript', 'c++', 'c#', 'ruby', 'php', 'swift', 'kotlin', 'go', 'rust', 'typescript', 'scala', 'perl', 'shell', 'bash', 'sql', 'html', 'css'],
+        'frameworks': ['react', 'angular', 'vue', 'django', 'flask', 'spring', 'express', 'rails', 'asp.net', 'laravel', 'node.js', 'bootstrap', 'jquery', 'tensorflow', 'pytorch', 'numpy', 'pandas'],
+        'databases': ['mysql', 'postgresql', 'mongodb', 'oracle', 'sql server', 'sqlite', 'redis', 'cassandra', 'dynamodb', 'firebase', 'elasticsearch'],
+        'tools': ['git', 'docker', 'kubernetes', 'jenkins', 'aws', 'azure', 'gcp', 'terraform', 'ansible', 'jira', 'confluence', 'notion', 'figma', 'photoshop', 'illustrator'],
+        'methodologies': ['agile', 'scrum', 'kanban', 'devops', 'ci/cd', 'test driven development', 'tdd', 'behavior driven development', 'bdd', 'rest', 'soap', 'microservices', 'serverless']
+    }
+    
+    # Flatten the list
+    all_skills = [skill for category in common_skills.values() for skill in category]
+    
+    # Find matches in the text
+    found_skills = []
+    text_lower = text.lower()
+    
+    for skill in all_skills:
+        # Check for whole word matches
+        pattern = r'\b' + re.escape(skill) + r'\b'
+        if re.search(pattern, text_lower):
+            found_skills.append(skill)
+    
+    # If spaCy is available, also look for named entities that might be technologies
+    if nlp:
+        doc = nlp(text)
+        for ent in doc.ents:
+            if ent.label_ in ["ORG", "PRODUCT"] and len(ent.text) > 2:
+                entity = ent.text.lower()
+                # Check if entity might be a technology
+                if any(tech_word in entity for tech_word in ["tech", "software", "platform", "system", "framework", "api", "cloud"]):
+                    found_skills.append(ent.text)
+    
+    # Count occurrences to identify most important skills
+    skill_counter = Counter(found_skills)
+    top_skills = [skill for skill, _ in skill_counter.most_common(10)]
+    
+    return top_skills
+
+def extract_experience(text):
+    """Extract work experience information from resume"""
+    experience_data = []
+    
+    # Look for common experience section headers
+    experience_headers = ["experience", "work experience", "employment history", "professional experience"]
+    
+    # Simple pattern matching for job titles and dates
+    job_title_pattern = r"(?:^|\n)(?:Senior |Lead |Junior |Staff |Principal )?\b(?:Developer|Engineer|Designer|Manager|Director|Analyst|Consultant|Administrator|Architect|Specialist)\b"
+    date_pattern = r"\b(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]* \d{4}\s*(?:-|–|to)\s*(?:(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]* \d{4}|Present|Current|Now)"
+    
+    # Find experience section
+    text_lower = text.lower()
+    section_start = None
+    for header in experience_headers:
+        if header in text_lower:
+            section_start = text_lower.find(header)
+            break
+    
+    if section_start is not None:
+        # Extract the section (assume it ends at the next major section)
+        next_section_start = float('inf')
+        for next_header in ["education", "skills", "projects", "certifications", "references"]:
+            pos = text_lower.find(next_header, section_start + 1)
+            if pos > section_start and pos < next_section_start:
+                next_section_start = pos
+        
+        experience_section = text[section_start:next_section_start] if next_section_start < float('inf') else text[section_start:]
+        
+        # Extract job titles
+        job_titles = re.findall(job_title_pattern, experience_section, re.IGNORECASE)
+        
+        # Extract date ranges
+        date_ranges = re.findall(date_pattern, experience_section)
+        
+        # Combine the information
+        for i, title in enumerate(job_titles[:3]):  # Limit to top 3 positions
+            date = date_ranges[i] if i < len(date_ranges) else "Unknown date"
+            experience_data.append({"title": title.strip(), "date": date})
+    
+    return experience_data
+
+def extract_education(text):
+    """Extract education information from resume"""
+    education_data = []
+    
+    # Look for degrees and institutions
+    degree_pattern = r"\b(?:Bachelor|Master|PhD|Doctorate|BSc|MSc|BA|MA|MBA|MD|JD|BS|MS|B\.S\.|M\.S\.|B\.A\.|M\.A\.)['\s\w]*\b"
+    institution_pattern = r"\b(?:University|College|Institute|School) of [\w\s]+\b"
+    
+    # Find matches
+    degrees = re.findall(degree_pattern, text)
+    institutions = re.findall(institution_pattern, text)
+    
+    # Combine the information
+    for i, degree in enumerate(degrees[:2]):  # Limit to top 2 degrees
+        institution = institutions[i] if i < len(institutions) else "Unknown institution"
+        education_data.append({"degree": degree.strip(), "institution": institution})
+    
+    return education_data
+
+def preprocess_resume(text):
+    """Extract structured information from resume text"""
+    # Basic text cleaning
+    text = text.replace('\n\n', ' [BREAK] ')
+    text = re.sub(r'\s+', ' ', text)
+    text = text.replace(' [BREAK] ', '\n\n')
+    
+    # Extract key information
+    skills = extract_skills(text)
+    experience = extract_experience(text)
+    education = extract_education(text)
+    
+    # Create structured resume data
+    resume_data = {
+        "skills": skills,
+        "experience": experience,
+        "education": education,
+        "full_text": text
+    }
+    
+    return resume_data
+
+def generate_interview_questions(resume_data):
+    """Generate interview questions based on resume data"""
+    # Default set of questions if model fails
+    default_questions = [
+        # Technical questions
+        "What challenges have you faced when working with databases, and how did you overcome them?",
+        "Describe a project where you had to optimize code for performance. What approach did you take?",
+        "How do you ensure your code is maintainable and follows best practices?",
+        "What software development methodologies are you familiar with, and which do you prefer?",
+        "How do you approach testing your code?",
+        
+        # Behavioral questions
+        "Tell me about a challenging project you worked on and how you approached it.",
+        "Describe a situation where you had to learn a new technology quickly.",
+        "How do you handle tight deadlines and pressure?"
+    ]
+    
+    # If no model is loaded, return default questions
+    if model is None or tokenizer is None:
+        return default_questions
+    
+    # Extract key resume information
+    skills_str = ", ".join(resume_data["skills"])
+    
+    experience_str = ""
+    for exp in resume_data["experience"]:
+        experience_str += f"{exp['title']} ({exp['date']}), "
+    
+    # Build a prompt for the model
+    prompt = f"""Generate 8 interview questions based on this resume information:
+Skills: {skills_str}
+Experience: {experience_str}
+
+Include 5 technical questions specific to the candidate's skills and 3 behavioral questions.
+Format each question on a new line and make them realistic interview questions.
+"""
+    
+    try:
+        # Generate text
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=device)
+        
+        # Generate with better parameters for coherent questions
+        output = model.generate(
+            input_ids,
+            attention_mask=attention_mask,
+            max_length=1024,
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,
+            do_sample=True,
+            top_p=0.92,
+            top_k=50,
+            temperature=0.85
+        )
+        
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        
+        # Process the output to extract questions
+        text_split = generated_text.replace(prompt, "").strip().split("\n")
+        
+        # Clean up and format questions
+        questions = []
+        for line in text_split:
+            # Remove question numbers and clean
+            line = re.sub(r'^\d+[\.\)]\s*', '', line.strip())
+            
+            # Only keep lines that look like questions
+            if line and ('?' in line or any(q_word in line.lower() for q_word in ["how", "what", "why", "when", "where", "describe", "tell", "explain"])):
+                # Ensure questions end with question mark
+                if not line.endswith('?') and any(q_word in line.lower() for q_word in ["how", "what", "why", "when", "where"]):
+                    line += '?'
+                questions.append(line)
+        
+        # Check if we got enough questions
+        if len(questions) >= 5:
+            return questions[:8]  # Return up to 8 questions
+        else:
+            # Fallback to default questions
+            logger.warning("Generated questions insufficient, using defaults")
+            return default_questions
+            
+    except Exception as e:
+        logger.error(f"Question generation error: {str(e)}")
+        return default_questions
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/static/<path:path>')
+def serve_static(path):
+    return send_from_directory('static', path)
+
+@app.route('/upload-resume', methods=['POST'])
+def upload_resume():
+    try:
+        if 'resume' not in request.files:
+            return jsonify({"error": "No file part"}), 400
+            
+        file = request.files['resume']
+        
+        if file.filename == '':
+            return jsonify({"error": "No selected file"}), 400
+            
+        if not file.filename.endswith('.pdf'):
+            return jsonify({"error": "Only PDF files are supported"}), 400
+
+        # Extract text from PDF
+        text = ""
+        try:
+            with pdfplumber.open(io.BytesIO(file.read())) as pdf:
+                for page in pdf.pages:
+                    page_text = page.extract_text()
+                    if page_text:
+                        text += page_text + "\n"
+        except Exception as e:
+            logger.error(f"PDF extraction error: {str(e)}")
+            return jsonify({"error": "Unable to extract text from PDF. Is the file corrupt?"}), 500
+
+        if not text.strip():
+            return jsonify({"error": "No text could be extracted from the PDF"}), 400
+
+        # Process resume and generate questions
+        resume_data = preprocess_resume(text)
+        questions = generate_interview_questions(resume_data)
+
+        # Add tailored question based on skills
+        if resume_data["skills"]:
+            top_skill = resume_data["skills"][0]
+            skill_question = f"Tell me about your experience with {top_skill} and how you've applied it in your projects."
+            questions.append(skill_question)
+
+        return jsonify(questions)
+
+    except Exception as e:
+        logger.error(f"General error: {str(e)}")
+        return jsonify({"error": "An error occurred processing your request. Please try again."}), 500
+
+if __name__ == '__main__':
+    # Make sure templates and static directories exist
+    for directory in ['templates', 'static']:
+        os.makedirs(directory, exist_ok=True)
+    
+    # Create index.html in templates if needed
+    templates_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'templates')
+    if not os.path.exists(os.path.join(templates_dir, 'index.html')):
+        with open(os.path.join(templates_dir, 'index.html'), 'w') as f:
+            f.write('''<!DOCTYPE html>
+<html>
+<head>
+    <title>Resume Question Generator</title>
+    <meta http-equiv="refresh" content="0;url=/" />
+</head>
+<body>
+    <p>Redirecting...</p>
+</body>
+</html>''')
+    
+    # Start the Flask app
+    app.run(debug=True)

requirements.txt

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+Flask==2.2.3
+gunicorn==20.1.0
+pdfplumber==0.7.6
+nltk==3.8.1
+spacy==3.5.0
+torch==2.0.0
+transformers==4.27.1

templates/index.html

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+<!-- <!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <title>AI Interview Simulator</title>
+</head>
+<body>
+  <h1>Upload Your Resume</h1>
+  <form id="upload-form" enctype="multipart/form-data">
+    <input type="file" name="resume" accept=".pdf" required />
+    <button type="submit">Generate Questions</button>
+  </form>
+
+  <h2>Generated Questions:</h2>
+  <ul id="question-list"></ul>
+
+  <script>
+    const form = document.getElementById("upload-form");
+    const questionList = document.getElementById("question-list");
+
+    form.addEventListener("submit", async (e) => {
+      e.preventDefault();
+      const formData = new FormData(form);
+      const response = await fetch("/upload-resume", {
+        method: "POST",
+        body: formData
+      });
+      const questions = await response.json();
+      questionList.innerHTML = "";
+      questions.forEach(q => {
+        const li = document.createElement("li");
+        li.textContent = q;
+        questionList.appendChild(li);
+      });
+    });
+  </script>
+</body>
+</html> -->
+
+
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>AI Interview Simulator</title>
+  <style>
+    :root {
+      --primary: #4f46e5;
+      --primary-dark: #4338ca; 
+      --secondary: #f3f4f6;
+      --text: #1f2937;
+      --light-text: #6b7280;
+      --accent: #fef3c7;
+      --border: #e5e7eb;
+      --shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
+    }
+    
+    * {
+      margin: 0;
+      padding: 0;
+      box-sizing: border-box;
+      font-family: 'Inter', system-ui, -apple-system, sans-serif;
+    }
+    
+    body {
+      background-color: #f9fafb;
+      color: var(--text);
+      line-height: 1.5;
+    }
+    
+    .container {
+      max-width: 800px;
+      margin: 0 auto;
+      padding: 2rem 1rem;
+    }
+    
+    header {
+      text-align: center;
+      margin-bottom: 3rem;
+    }
+    
+    h1 {
+      color: var(--primary);
+      font-size: 2.5rem;
+      margin-bottom: 1rem;
+    }
+    
+    .tagline {
+      color: var(--light-text);
+      font-size: 1.2rem;
+      max-width: 600px;
+      margin: 0 auto;
+    }
+    
+    .card {
+      background-color: white;
+      border-radius: 12px;
+      box-shadow: var(--shadow);
+      padding: 2rem;
+      margin-bottom: 2rem;
+    }
+    
+    .card-title {
+      color: var(--text);
+      font-size: 1.5rem;
+      margin-bottom: 1.5rem;
+      display: flex;
+      align-items: center;
+      gap: 10px;
+    }
+    
+    .icon {
+      background-color: var(--accent);
+      width: 40px;
+      height: 40px;
+      border-radius: 50%;
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      font-weight: bold;
+      color: var(--primary);
+    }
+    
+    .upload-area {
+      border: 2px dashed var(--border);
+      border-radius: 8px;
+      padding: 2rem;
+      text-align: center;
+      transition: all 0.3s;
+      cursor: pointer;
+      position: relative;
+      margin-bottom: 1.5rem;
+    }
+    
+    .upload-area:hover {
+      border-color: var(--primary);
+      background-color: rgba(79, 70, 229, 0.03);
+    }
+    
+    .upload-area p {
+      color: var(--light-text);
+      margin: 1rem 0;
+    }
+    
+    .file-input {
+      position: absolute;
+      width: 100%;
+      height: 100%;
+      top: 0;
+      left: 0;
+      opacity: 0;
+      cursor: pointer;
+    }
+    
+    .button {
+      background-color: var(--primary);
+      color: white;
+      border: none;
+      border-radius: 8px;
+      padding: 12px 24px;
+      font-size: 1rem;
+      font-weight: 600;
+      cursor: pointer;
+      transition: all 0.2s;
+      display: inline-flex;
+      align-items: center;
+      justify-content: center;
+      gap: 8px;
+    }
+    
+    .button:hover {
+      background-color: var(--primary-dark);
+      transform: translateY(-1px);
+    }
+    
+    .file-name {
+      background-color: var(--secondary);
+      padding: 8px 16px;
+      border-radius: 6px;
+      display: none;
+      align-items: center;
+      justify-content: space-between;
+      margin-bottom: 1.5rem;
+    }
+    
+    .file-name.active {
+      display: flex;
+    }
+    
+    .remove-file {
+      background: none;
+      border: none;
+      color: var(--light-text);
+      cursor: pointer;
+      font-size: 1.2rem;
+    }
+    
+    .question-list {
+      list-style-type: none;
+    }
+    
+    .question-item {
+      padding: 16px;
+      border-radius: 8px;
+      background-color: var(--secondary);
+      margin-bottom: 12px;
+      animation: fadeIn 0.5s ease-in-out;
+    }
+    
+    .question-number {
+      font-weight: 600;
+      color: var(--primary);
+      margin-right: 8px;
+    }
+    
+    .loading {
+      display: none;
+      align-items: center;
+      justify-content: center;
+      gap: 12px;
+      margin: 2rem 0;
+    }
+    
+    .loading.active {
+      display: flex;
+    }
+    
+    .spinner {
+      width: 24px;
+      height: 24px;
+      border: 3px solid rgba(79, 70, 229, 0.3);
+      border-radius: 50%;
+      border-top-color: var(--primary);
+      animation: spin 1s linear infinite;
+    }
+    
+    .no-questions {
+      text-align: center;
+      color: var(--light-text);
+      padding: 2rem;
+    }
+    
+    @keyframes fadeIn {
+      from { opacity: 0; transform: translateY(10px); }
+      to { opacity: 1; transform: translateY(0); }
+    }
+    
+    @keyframes spin {
+      to { transform: rotate(360deg); }
+    }
+    
+    @media (max-width: 640px) {
+      .container {
+        padding: 1rem;
+      }
+      
+      h1 {
+        font-size: 2rem;
+      }
+      
+      .card {
+        padding: 1.5rem;
+      }
+    }
+  </style>
+</head>
+<body>
+  <div class="container">
+    <header>
+      <h1>AI Interview Simulator</h1>
+      <p class="tagline">Upload your resume and receive tailored interview questions to help you prepare for your next job interview</p>
+    </header>
+    
+    <div class="card">
+      <h2 class="card-title">
+        <span class="icon">1</span>
+        Upload Your Resume
+      </h2>
+      
+      <form id="upload-form" enctype="multipart/form-data">
+        <div class="upload-area" id="drop-area">
+          <svg xmlns="http://www.w3.org/2000/svg" width="48" height="48" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-upload" style="color: var(--primary);">
+            <path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"></path>
+            <polyline points="17 8 12 3 7 8"></polyline>
+            <line x1="12" y1="3" x2="12" y2="15"></line>
+          </svg>
+          
+          <p>Drag and drop your resume, or click to browse</p>
+          <p style="font-size: 0.9rem;">Supported format: PDF</p>
+          
+          <input type="file" name="resume" accept=".pdf" required class="file-input" id="file-input" />
+        </div>
+        
+        <div class="file-name" id="file-display">
+          <span id="file-name">document.pdf</span>
+          <button type="button" class="remove-file" id="remove-file">×</button>
+        </div>
+        
+        <button type="submit" class="button" id="generate-btn">
+          <svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+            <circle cx="12" cy="12" r="10"></circle>
+            <polygon points="10 8 16 12 10 16 10 8"></polygon>
+          </svg>
+          Generate Interview Questions
+        </button>
+      </form>
+    </div>
+    
+    <div class="loading" id="loading">
+      <div class="spinner"></div>
+      <span>Analyzing your resume...</span>
+    </div>
+    
+    <div class="card" id="questions-card" style="display: none;">
+      <h2 class="card-title">
+        <span class="icon">2</span>
+        Your Interview Questions
+      </h2>
+      
+      <div id="questions-container">
+        <ul class="question-list" id="question-list"></ul>
+        <div class="no-questions" id="no-questions">
+          Upload your resume to see personalized interview questions
+        </div>
+      </div>
+    </div>
+  </div>
+  
+  <script>
+    const form = document.getElementById("upload-form");
+    const fileInput = document.getElementById("file-input");
+    const fileDisplay = document.getElementById("file-display");
+    const fileName = document.getElementById("file-name");
+    const removeFile = document.getElementById("remove-file");
+    const questionList = document.getElementById("question-list");
+    const questionsCard = document.getElementById("questions-card");
+    const noQuestions = document.getElementById("no-questions");
+    const loading = document.getElementById("loading");
+    
+    // File input handling
+    fileInput.addEventListener("change", (e) => {
+      if (fileInput.files.length > 0) {
+        fileName.textContent = fileInput.files[0].name;
+        fileDisplay.classList.add("active");
+      }
+    });
+    
+    removeFile.addEventListener("click", () => {
+      fileInput.value = "";
+      fileDisplay.classList.remove("active");
+    });
+    
+    // Form submission
+    form.addEventListener("submit", async (e) => {
+      e.preventDefault();
+      
+      if (fileInput.files.length === 0) {
+        alert("Please select a resume file first");
+        return;
+      }
+      
+      loading.classList.add("active");
+      
+      try {
+        const formData = new FormData(form);
+        
+        // Simulate API call with timeout
+        setTimeout(async () => {
+          // In a real application, this would be an actual API call
+          // const response = await fetch("/upload-resume", {
+          //   method: "POST",
+          //   body: formData
+          // });
+          // const questions = await response.json();
+          
+          // Sample questions for demonstration
+          const questions = [
+            "Tell me about your experience with front-end development frameworks mentioned in your resume.",
+            "Can you explain a challenging project you worked on and how you approached it?",
+            "How do you stay updated with the latest technologies in your field?",
+            "What made you interested in applying for this position?",
+            "Describe a situation where you had to learn a new technology quickly. How did you approach it?",
+            "How do you handle tight deadlines and pressure?",
+            "Can you elaborate on your experience with team collaboration tools?",
+            "What is your process for debugging complex issues in your code?"
+          ];
+          
+          // Display questions
+          questionList.innerHTML = "";
+          questions.forEach((q, index) => {
+            const li = document.createElement("li");
+            li.className = "question-item";
+            li.innerHTML = `<span class="question-number">Q${index + 1}:</span> ${q}`;
+            questionList.appendChild(li);
+          });
+          
+          loading.classList.remove("active");
+          questionsCard.style.display = "block";
+          noQuestions.style.display = "none";
+        }, 2000); // Simulate loading delay
+        
+      } catch (error) {
+        console.error("Error:", error);
+        loading.classList.remove("active");
+        alert("There was an error processing your request. Please try again.");
+      }
+    });
+  </script>
+</body>
+</html>