Added Beta of system design interview

app.py

@@ -5,9 +5,8 @@ import gradio as gr
 from api.audio import STTManager, TTSManager
 from api.llm import LLMManager
 from config import config
-from docs.instruction import instruction
 from resources.prompts import prompts
-from ui.coding import get_codding_ui
+from ui.coding import get_problem_solving_ui
 from ui.instructions import get_instructions_ui
 from utils.params import default_audio_params
 
@@ -22,9 +21,13 @@ default_audio_params["streaming"] = stt.streaming
 with gr.Blocks(title="AI Interviewer") as demo:
     audio_output = gr.Audio(label="Play audio", autoplay=True, visible=os.environ.get("DEBUG", False), streaming=tts.streaming)
     instructions_tab = get_instructions_ui(llm, tts, stt, default_audio_params)
-    coding_tab = get_codding_ui(llm, tts, stt, default_audio_params, audio_output)
+    coding_tab = get_problem_solving_ui(llm, tts, stt, default_audio_params, audio_output, name="Coding", interview_type="coding")
+    system_design_tab = get_problem_solving_ui(
+        llm, tts, stt, default_audio_params, audio_output, name="System Design (Beta)", interview_type="system_design"
+    )
 
     instructions_tab.render()
     coding_tab.render()
+    system_design_tab.render()
 
 demo.launch(show_api=False)

resources/data.py

@@ -1,25 +1,39 @@
-coding_topics_list = [
-    "Arrays",
-    "Strings",
-    "Linked Lists",
-    "Hash Tables",
-    "Dynamic Programming",
-    "Trees",
-    "Graphs",
-    "Sorting Algorithms",
-    "Binary Search",
-    "Recursion",
-    "Greedy Algorithms",
-    "Stack",
-    "Queue",
-    "Heaps",
-    "Depth-First Search (DFS)",
-    "Breadth-First Search (BFS)",
-    "Backtracking",
-    "Bit Manipulation",
-    "Binary Search Trees",
-    "Tries",
-]
+topic_lists = {
+    "coding": [
+        "Arrays",
+        "Strings",
+        "Linked Lists",
+        "Hash Tables",
+        "Dynamic Programming",
+        "Trees",
+        "Graphs",
+        "Sorting Algorithms",
+        "Binary Search",
+        "Recursion",
+        "Greedy Algorithms",
+        "Stack",
+        "Queue",
+        "Heaps",
+        "Depth-First Search (DFS)",
+        "Breadth-First Search (BFS)",
+        "Backtracking",
+        "Bit Manipulation",
+        "Binary Search Trees",
+        "Tries",
+    ],
+    "system_design": [
+        "Machine Learning Systems",
+        "Databases",
+        "Mobile Application Architecture",
+        "Web Services and APIs",
+        "Cloud Computing and Storage",
+        "Network Architecture and Protocols",
+        "Security and Compliance",
+        "Distributed Systems",
+        "Real-time and Batch Processing",
+        "Content Delivery Networks",
+    ],
+}
 
 fixed_messages = {
     "intro": "Nice to meet you! I'm your AI interviewer. Click 'Generate a problem' to start.",

resources/prompts.py

@@ -34,4 +34,39 @@ prompts = {
         "Use code examples to illustrate points where necessary. Your feedback should be critical, aiming to fail candidates who do not meet high standards while providing detailed improvement areas. "
         "Format all feedback in clear, structured markdown for readability."
     ),
+    "system_design_problem_generation_prompt": (
+        "You are an AI acting as an interviewer. "
+        "Generate a scenario that tests the candidate's ability to architect scalable and robust systems. "
+        "Ensure the scenario tests for architectural understanding, integration of different technologies, security considerations, and scalability. "
+        "The scenario should be clearly stated, well-formatted, and solvable within 30 minutes. "
+        "Ensure the scenario varies each time to provide a wide range of challenges."
+    ),
+    "system_design_interviewer_prompt": (
+        "As an AI interviewer, maintain a professional and analytical demeanor. "
+        "Encourage candidates to discuss various architectural choices and trade-offs without giving away direct solutions. Provide hints subtly only after observing significant struggles or upon explicit request. "
+        "Probe the candidate with questions related to system scalability, choice of technologies, data flow, security implications, and maintenance strategies to assess their architectural proficiency comprehensively. "
+        "If the candidate deviates from the core architectural focus, gently guide them back to the main issues. "
+        "After multiple unsuccessful attempts by the candidate to articulate or resolve design flaws, provide more direct hints or rephrase the scenario slightly to aid understanding. "
+        "Encourage the candidate to consider the practical implications of their design choices, asking how changes in system requirements might impact their architecture. "
+        "Discuss the trade-offs in their design decisions, encouraging them to justify their choices based on performance, cost, and complexity. "
+        "Prompt the candidate to explain potential scaling strategies and how they would handle increased load or data volume. "
+        "Keep your interactions concise and clear, avoiding overly technical language or complex explanations that could confuse the candidate."
+    ),
+    "system_design_grading_feedback_prompt": (
+        "You are the AI grader for an interview. "
+        "The following is the interview transcript with the candidate's responses. "
+        "Ignore minor transcription errors unless they impact comprehension. "
+        "Evaluate the candidate’s performance based on the following criteria: "
+        "\n- **Architectural Understanding**: Knowledge of system components and their interactions."
+        "\n- **Technology Integration**: Usage of appropriate technologies and frameworks considering the problem's context."
+        "\n- **Scalability and Performance**: Ability to design systems that can scale efficiently and maintain performance."
+        "\n- **Security Awareness**: Consideration of potential security risks and mitigation strategies."
+        "\n- **System Robustness**: Design resilience and handling of potential system failures."
+        "\n- **Communication Skills**: Ability to articulate design decisions and respond to hypothetical changes."
+        "\n- **Problem Solving and Creativity**: Creativity in approaching complex system issues and solving problems."
+        "\n- **Decision Making**: Justification of design choices and trade-offs made during the discussion."
+        "\nProvide comprehensive feedback, detailing overall performance, specific design flaws, areas for improvement, communication issues, and other relevant observations. "
+        "Use system diagrams or pseudo-code to illustrate points where necessary. Your feedback should be critical, aiming to fail candidates who do not meet high standards while providing constructive areas for improvement. "
+        "Format all feedback in clear, structured markdown for readability."
+    ),
 }

ui/coding.py

@@ -1,16 +1,16 @@
 import gradio as gr
 import numpy as np
 
-from resources.data import coding_topics_list, fixed_messages
+from resources.data import fixed_messages, topic_lists
 from utils.ui import add_candidate_message, add_interviewer_message
 
 
-def get_codding_ui(llm, tts, stt, default_audio_params, audio_output):
-    with gr.Tab("Coding", render=False) as coding_tab:
+def get_problem_solving_ui(llm, tts, stt, default_audio_params, audio_output, name="Coding", interview_type="coding"):
+    with gr.Tab(name, render=False) as problem_tab:
         chat_history = gr.State([])
         previous_code = gr.State("")
         started_coding = gr.State(False)
-        interview_type = gr.State("coding")
+        interview_type = gr.State(interview_type)
         with gr.Accordion("Settings") as init_acc:
             with gr.Row():
                 with gr.Column():
@@ -27,7 +27,11 @@ def get_codding_ui(llm, tts, stt, default_audio_params, audio_output):
                     with gr.Row():
                         gr.Markdown("Topic (can type custom value)")
                         topic_select = gr.Dropdown(
-                            label="Select topic", choices=coding_topics_list, value="Arrays", container=False, allow_custom_value=True
+                            label="Select topic",
+                            choices=topic_lists[interview_type.value],
+                            value=topic_lists[interview_type.value][0],
+                            container=False,
+                            allow_custom_value=True,
                         )
                 with gr.Column(scale=2):
                     requirements = gr.Textbox(label="Requirements", placeholder="Specify additional requirements", lines=5)
@@ -38,11 +42,19 @@ def get_codding_ui(llm, tts, stt, default_audio_params, audio_output):
         with gr.Accordion("Solution", open=False) as solution_acc:
             with gr.Row() as content:
                 with gr.Column(scale=2):
-                    code = gr.Code(
-                        label="Please write your code here. You can use any language, but only Python syntax highlighting is available.",
-                        language="python",
-                        lines=46,
-                    )
+                    if interview_type == "coding":
+                        code = gr.Code(
+                            label="Please write your code here. You can use any language, but only Python syntax highlighting is available.",
+                            language="python",
+                            lines=46,
+                        )
+                    else:
+                        code = gr.Textbox(
+                            label="Please write any notes for your solution here.",
+                            lines=46,
+                            max_lines=46,
+                            show_label=False,
+                        )
                 with gr.Column(scale=1):
                     end_btn = gr.Button("Finish the interview", interactive=False)
                     chat = gr.Chatbot(label="Chat", show_label=False, show_share_button=False)
@@ -119,8 +131,8 @@ def get_codding_ui(llm, tts, stt, default_audio_params, audio_output):
                 fn=lambda: gr.update(interactive=True), outputs=[send_btn]
             ).success(fn=lambda: None, outputs=[audio_input])
 
-        coding_tab.select(fn=add_interviewer_message(fixed_messages["intro"]), inputs=[chat, started_coding], outputs=[chat]).success(
+        problem_tab.select(fn=add_interviewer_message(fixed_messages["intro"]), inputs=[chat, started_coding], outputs=[chat]).success(
             fn=tts.read_last_message, inputs=[chat], outputs=[audio_output]
         )
 
-    return coding_tab
+    return problem_tab