import gradio as gr from huggingface_hub import InferenceClient import re import random # Load questions (your original backend) def load_questions(file_path): with open(file_path, 'r') as f: data = f.read() question_blocks = re.split(r'Question:\s*', data)[1:] questions = [] for block in question_blocks: parts = block.split('Possible Answers:') question_text = parts[0].strip() answers_text = parts[1].strip() possible_answers = [ans.strip() for ans in re.split(r'\d+\.\s+', answers_text) if ans.strip()] questions.append({'question': question_text, 'answers': possible_answers}) return questions all_questions = load_questions('knowledge.txt') # Question categorization (same as your existing code) questions_by_type = { 'Technical': [q for q in all_questions if any(keyword in q['question'].lower() for keyword in [ 'function', 'linked list', 'url', 'rest', 'graphql', 'garbage', 'cap theorem', 'sql', 'hash table', 'stack', 'queue', 'recursion', 'reverse', 'bfs', 'dfs', 'time complexity', 'binary search tree', 'web application', 'chat system', 'load balancing', 'caching', 'normalization', 'acid', 'indexing', 'sql injection', 'https', 'xss', 'hash', 'vulnerabilities'])], 'Competency-Based Interview': [q for q in all_questions if any(keyword in q['question'].lower() for keyword in [ "debugging", "learning", "deadlines", "teamwork", "leadership", "mistake", "conflict", "decision"])], 'Case': [q for q in all_questions if any(keyword in q['question'].lower() for keyword in [ "testing", "financial", "automation", "analysis", "regression", "business", "stakeholder"])] } client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") # Backend logic (all functions same as before — no changes) def set_type(choice, user_profile): user_profile["interview_type"] = choice return "Great! What’s your background and what field/role are you aiming for?", user_profile def save_background(info, user_profile): user_profile["field"] = info return "Awesome! Type 'start' below to begin your interview.", user_profile def respond(message, chat_history, user_profile): message_lower = message.strip().lower() if not user_profile.get("interview_type") or not user_profile.get("field"): bot_msg = "Please finish steps 1 and 2 before starting the interview." chat_history.append((message, bot_msg)) return chat_history if message_lower == 'start': interview_type = user_profile['interview_type'] selected_questions = questions_by_type.get(interview_type, []) random.shuffle(selected_questions) selected_questions = selected_questions[:10] user_profile['questions'] = selected_questions user_profile['current_q'] = 0 user_profile['user_answers'] = [] user_profile['interview_in_progress'] = True intro = f"Welcome to your {interview_type} interview for a {user_profile['field']} position. I will ask you up to 10 questions. Type 'stop' anytime to end." first_q = f"First question: {selected_questions[0]['question']}" chat_history.append((message, intro)) chat_history.append(("", first_q)) return chat_history if message_lower == 'stop' and user_profile.get("interview_in_progress"): user_profile['interview_in_progress'] = False bot_msg = "Interview stopped. Type 'feedback' if you'd like me to analyze your answers." chat_history.append((message, bot_msg)) return chat_history if user_profile.get("interview_in_progress"): q_index = user_profile['current_q'] user_profile['user_answers'].append(message) q_index += 1 user_profile['current_q'] = q_index if q_index < len(user_profile['questions']): bot_msg = f"Next question: {user_profile['questions'][q_index]['question']}" else: user_profile['interview_in_progress'] = False bot_msg = "Interview complete! Type 'feedback' if you'd like me to analyze your answers." chat_history.append((message, bot_msg)) return chat_history if message_lower == 'feedback': feedback = generate_feedback(user_profile) chat_history.append((message, feedback)) return chat_history # Normal chatbot conversation messages = [{"role": "system", "content": f"You are a professional interviewer conducting a {user_profile['interview_type']} interview for a candidate in {user_profile['field']}."}] for q, a in chat_history: messages.append({"role": "user", "content": q}) messages.append({"role": "assistant", "content": a}) messages.append({"role": "user", "content": message}) response = client.chat_completion(messages, max_tokens=150, stream=False) bot_msg = response.choices[0].message.content chat_history.append((message, bot_msg)) return chat_history def generate_feedback(user_profile): feedback = [] questions = user_profile.get('questions', []) answers = user_profile.get('user_answers', []) for i, user_ans in enumerate(answers): correct_answers = questions[i]['answers'] match = any(ans.lower() in user_ans.lower() for ans in correct_answers) if match: fb = f"Question {i+1}: ✅ Good job!" else: fb = f"Question {i+1}: ❌ Missed key points: {correct_answers[0]}" feedback.append(fb) return "\n".join(feedback) # The new Intervu 2.0 UI with your design! with gr.Blocks(css=""" body { background-color: #161b24; font-family: 'Nato', sans-serif !important; } h1 { text-align: center; color: #2c3e50; } img { display: block; margin: auto; width: 100px; border-radius: 20px; } button { font-size: 16px; padding: 10px 20px; border-radius: 10px; border: 2px solid rgba(124, 248, 255, 0.4); background-color: rgba(124, 248, 255, 0.4); color: #fafdff; transition: all 0.2s ease; } button:hover { background-color: #49888f; border-color: #7cf8ff; transform: scale(1.05); } .gr-chatbot { background-color: white; border-radius: 15px; padding: 20px; } """) as demo: gr.Markdown("""
""") user_profile = gr.State({"interview_type": "", "field": "", "interview_in_progress": False}) chat_history = gr.State([]) #Header gr.Markdown("""

Welcome to Intervu

Before you begin, complete Step 1 to select your interview type and Step 2 to enter your background. Practice is available through text, speech, or webcam.

""") # Step 1 - Choose Interview Type # gr.Markdown("### Step 1: Choose Interview Type") # with gr.Row(): # btn1 = gr.Button("Technical") # btn2 = gr.Button("Competency-Based Interview") # btn3 = gr.Button("Case") # type_output = gr.Textbox(label="Bot response", interactive=False) with gr.Row(): # LEFT SIDE: Transparent box with interview type buttons with gr.Column(scale=2): gr.Markdown("""

Step 1: Choose Interview Type

Select the type of interview you want to practice.

""") btn1 = gr.Button("Technical") btn2 = gr.Button("Competency-Based Interview") btn3 = gr.Button("Case") # RIGHT SIDE: Bot response display (type_output) with gr.Column(scale=2): type_output = gr.Textbox(label="Bot Response", interactive=False) btn1.click(set_type, inputs=[gr.Textbox(value="Technical", visible=False), user_profile], outputs=[type_output, user_profile]) btn2.click(set_type, inputs=[gr.Textbox(value="Competency-Based Interview", visible=False), user_profile], outputs=[type_output, user_profile]) btn3.click(set_type, inputs=[gr.Textbox(value="Case", visible=False), user_profile], outputs=[type_output, user_profile]) # Step 2 - Enter Background gr.Markdown("### Step 2: Enter Your Background") background = gr.Textbox(label="Your background and field/goal") background_btn = gr.Button("Submit") background_output = gr.Textbox(label="Bot response", interactive=False) background_btn.click(save_background, inputs=[background, user_profile], outputs=[background_output, user_profile]) # Step 3 - Chatbot Mode Selection gr.Markdown("### Choose Chat Mode") with gr.Row(): gr.Button("Text-Based") # You can build voice & webcam later :) # Chat interface chatbot = gr.Chatbot(label="Interview Chat") msg = gr.Textbox(label="Type 'start' to begin") send_btn = gr.Button("Send") send_btn.click(respond, inputs=[msg, chat_history, user_profile], outputs=[chatbot], queue=False) send_btn.click(lambda: "", None, msg, queue=False) demo.launch()