Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+import re
+import random
+
+# uploading and cleaning the knowledge txt file
+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')
+
+# creating the questions based on each interview
+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")
+
+# setting up the users profile
+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
+
+    # interview process
+    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. Thanks for interviewing with Intervu!"
+        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. Thanks for interviewing with Intervu!"
+        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
+
+    # starting the chatbot
+    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 some key points: {correct_answers[0]}"
+        feedback.append(fb)
+    return "\n".join(feedback)
+
+# creating the visual elements
+with gr.Blocks() as demo:
+    user_profile = gr.State({"interview_type": "", "field": "", "interview_in_progress": False})
+    chat_history = gr.State([])
+
+    gr.Markdown("# Welcome to Intervu")
+    gr.Markdown('<p align="center"><img src="images.jpeg" width="200"></p>')
+
+    gr.Markdown("### Step 1: Choose Interview Type")
+    with gr.Row():
+        with gr.Column():
+            btn1 = gr.Button("Technical")
+            btn2 = gr.Button("Competency-Based Interview")
+            btn3 = gr.Button("Case")
+        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])
+
+    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])
+
+    gr.Markdown("### Step 3: Start Interview")
+    chatbot = gr.Chatbot(label="Interview Bot")
+    msg = gr.Textbox(label="Your message")
+    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()