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README.md

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 ---
-title: MockInterviewBot
-emoji: 🏃
-colorFrom: yellow
-colorTo: purple
+title: Codingprep
+emoji: 💻
+colorFrom: purple
+colorTo: yellow
 sdk: streamlit
 sdk_version: 1.40.1
 app_file: app.py
 pinned: false
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+from openai import OpenAI
+import os
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import torch
+import re
+
+# Set up OpenAI client
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# Check if GPU is available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# Load metadata and embeddings (ensure these files are in your working directory or update paths)
+metadata_path = 'question_metadata.csv'  # Update this path if needed
+embeddings_path = 'question_dataset_embeddings.npy'  # Update this path if needed
+
+metadata = pd.read_csv(metadata_path)
+embeddings = np.load(embeddings_path)
+
+# Load the SentenceTransformer model
+model = SentenceTransformer("all-MiniLM-L6-v2").to(device)
+
+# Load prompts from files
+with open("technical_interviewer_prompt.txt", "r") as file:
+    technical_interviewer_prompt = file.read()
+
+with open("question_generation_prompt.txt", "r") as file:
+    question_generation_prompt = file.read()
+    
+st.title("Real-World Programming Question Mock Interview")
+
+# Initialize session state variables
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "follow_up_mode" not in st.session_state:
+    st.session_state.follow_up_mode = False  # Tracks whether we're in follow-up mode
+
+if "generated_question" not in st.session_state:
+    st.session_state.generated_question = None  # Stores the generated question for persistence
+
+if "code_template" not in st.session_state:
+    st.session_state.code_template = ""  # Stores the code template
+
+if "sample_test_case" not in st.session_state:
+    st.session_state.sample_test_case = ""  # Stores the sample test case
+
+if "expected_output" not in st.session_state:
+    st.session_state.expected_output = ""  # Stores the expected output
+
+if "debug_logs" not in st.session_state:
+    st.session_state.debug_logs = None  # Stores debug logs for toggling
+
+# Function to find the top 1 most similar question based on user input
+def find_top_question(query):
+    # Generate embedding for the query
+    query_embedding = model.encode(query, convert_to_tensor=True, device=device).cpu().numpy()
+    
+    # Reshape query_embedding to ensure it is a 2D array
+    query_embedding = query_embedding.reshape(1, -1)  # Reshape to (1, n_features)
+
+    # Compute cosine similarity between query embedding and dataset embeddings
+    similarities = cosine_similarity(query_embedding, embeddings).flatten()  # Flatten to get a 1D array of similarities
+
+    # Get the index of the most similar result (top 1)
+    top_index = similarities.argsort()[-1]  # Index of highest similarity
+
+    # Retrieve metadata for the top result
+    top_result = metadata.iloc[top_index].copy()
+    top_result['similarity_score'] = similarities[top_index]
+
+    return top_result
+
+# Function to generate response using OpenAI API with debugging logs
+def generate_response(messages):
+    # For debug logs, store only the follow-up conversation history
+    st.session_state.debug_logs = st.session_state.messages  # Update debug logs with current conversation
+    
+    response = client.chat.completions.create(
+        model="o1-mini",
+        messages=messages,
+    )
+    
+    return response.choices[0].message.content
+
+# Function to extract code template and sample test case from the generated question
+def extract_code_and_test_case(generated_question):
+    code_template = ""
+    sample_test_case = ""
+    expected_output = ""
+    
+    # Extract code template
+    code_match = re.search(r'```python(.*?)```', generated_question, re.DOTALL)
+    if code_match:
+        code_template = code_match.group(1).strip()
+    else:
+        # Default code template if none is found
+        code_template = "# Write your code here\n"
+
+    # Extract sample test case and expected output
+    test_case_match = re.search(r'Sample Input:\s*(.*?)\n', generated_question, re.DOTALL)
+    expected_output_match = re.search(r'Expected Output:\s*(.*?)\n', generated_question, re.DOTALL)
+    if test_case_match and expected_output_match:
+        sample_test_case = test_case_match.group(1).strip()
+        expected_output = expected_output_match.group(1).strip()
+    else:
+        sample_test_case = ""
+        expected_output = ""
+
+    return code_template, sample_test_case, expected_output
+
+# Move the input form to the sidebar to make it always visible and more compact
+with st.sidebar.form(key="input_form"):
+    st.markdown("## Generate a New Question")
+    company = st.text_input("Company", value="Google")  # Default value: Google
+    difficulty = st.selectbox("Difficulty", ["Easy", "Medium", "Hard"], index=1)  # Default: Medium
+    topic = st.text_input("Topic", value="Binary Search")  # Default: Binary Search
+    generate_button = st.form_submit_button(label="Generate")
+
+if generate_button:
+    # Clear session state and start fresh with follow-up mode disabled
+    st.session_state.messages = []
+    st.session_state.follow_up_mode = False
+
+    # Create a query from user inputs and find the most relevant question
+    query = f"{company} {difficulty} {topic}"
+    top_question = find_top_question(query)
+    
+    # Prepare a detailed prompt for GPT using the top question's details
+    detailed_prompt = (
+        f"Transform this LeetCode question into a real-world interview scenario.\n\n"
+        f"**Company**: {top_question['company']}\n"
+        f"**Question Name**: {top_question['questionName']}\n"
+        f"**Difficulty Level**: {top_question['difficulty level']}\n"
+        f"**Tags**: {top_question['Tags']}\n"
+        f"**Content**: {top_question['Content']}\n"
+        f"\nPlease create a real-world interview question based on this information. "
+        f"Include the following sections:\n\n"
+        f"- Problem Description\n"
+        f"- Code Template (in a Python code block)\n"
+        f"- Sample Input and Expected Output (clearly separated)\n"
+    )
+    
+    # Generate response using OpenAI API with detailed prompt and debugging logs
+    response = generate_response([{"role": "user", "content": detailed_prompt}])  # Question generation prompt excluded here
+
+    # Store generated question in session state for persistence in sidebar and follow-up conversation state
+    st.session_state.generated_question = response
+
+    # Extract code template and sample test case
+    code_template, sample_test_case, expected_output = extract_code_and_test_case(response)
+    st.session_state.code_template = code_template
+    st.session_state.sample_test_case = sample_test_case
+    st.session_state.expected_output = expected_output
+
+    # Enable follow-up mode after generating the initial question
+    st.session_state.follow_up_mode = True
+
+# Display chat messages from history on app rerun (for subsequent conversation)
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Chatbox for subsequent conversations with assistant (follow-up mode)
+if st.session_state.follow_up_mode:
+    if user_input := st.chat_input("Continue your conversation or ask follow-up questions here:"):
+        # Display user message in chat message container and add to session history
+        with st.chat_message("user"):
+            st.markdown(user_input)
+        
+        st.session_state.messages.append({"role": "user", "content": user_input})
+
+        # Prepare messages to send to the assistant
+        # Include the technical interviewer prompt and generated question, but do not display them
+        # Add an instruction for the assistant to reply as a real-world interviewer would
+        assistant_instruction = (
+            "As a real-world interviewer, please reply to the candidate's follow-up questions "
+            "specific to the generated interview question, to the point, and in a natural, human-sounding way."
+        )
+
+        messages_to_send = [
+            {"role": "user", "content": technical_interviewer_prompt},
+            {"role": "assistant", "content": st.session_state.generated_question},
+            {"role": "user", "content": assistant_instruction}
+        ] + st.session_state.messages
+
+        assistant_response = generate_response(messages_to_send)
+
+        with st.chat_message("assistant"):
+            st.markdown(assistant_response)
+        
+        st.session_state.messages.append({"role": "assistant", "content": assistant_response})
+
+st.sidebar.markdown("---")
+st.sidebar.markdown("## Generated Question")
+if st.session_state.generated_question:
+    st.sidebar.markdown(st.session_state.generated_question)
+else:
+    st.sidebar.markdown("_No question generated yet._")
+
+st.sidebar.markdown("---")
+st.sidebar.markdown("## Python Code Interpreter")
+
+# Pre-fill code interpreter with code template after question generation
+if st.session_state.code_template:
+    code_input = st.sidebar.text_area("Write your Python code here:", value=st.session_state.code_template, height=300)
+else:
+    code_input = st.sidebar.text_area("Write your Python code here:", height=300)
+
+if st.sidebar.button("Run Code"):
+    try:
+        # Prepare the code for execution
+        exec_globals = {}
+        # Create a function wrapper to execute the user's code
+        exec(f"def user_solution():\n{code_input}", exec_globals)
+        user_solution = exec_globals.get('user_solution', None)
+        
+        # Prepare sample test case execution
+        if st.session_state.sample_test_case:
+            # Assume the sample test case is in the format of arguments to the function
+            test_case = st.session_state.sample_test_case
+            # Evaluate the test case safely
+            test_args = eval(test_case)
+            if not isinstance(test_args, tuple):
+                test_args = (test_args,)
+            # Capture the output
+            returned_output = user_solution(*test_args)
+        else:
+            returned_output = user_solution()
+        
+        # Display the expected output and returned output
+        st.sidebar.markdown("### Sample Test Case Result:")
+        st.sidebar.markdown(f"**Sample Input:** {st.session_state.sample_test_case}")
+        st.sidebar.markdown(f"**Expected Output:** {st.session_state.expected_output}")
+        st.sidebar.markdown(f"**Your Output:** {returned_output}")
+        
+        # Compare outputs
+        if str(returned_output) == st.session_state.expected_output:
+            st.sidebar.success("Your output matches the expected output!")
+        else:
+            st.sidebar.error("Your output does not match the expected output.")
+    except Exception as e:
+        st.sidebar.error(f"Error: {e}")
+
+# Right sidebar toggleable debug logs and code interpreter section
+with st.expander("Debug Logs (Toggle On/Off)", expanded=False):
+    if st.session_state.debug_logs:
+        st.write(st.session_state.debug_logs)

question_dataset_embeddings.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c0e8003615af2eb6c5fa4657d5f06c1b4ed26e152173f7c9ef928a9a86d7f170
+size 132

question_generation_prompt.txt

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+"You are an expert technical interviewer tasked with transforming algorithmic problems into engaging real-world scenarios. Please help me generate interview questions that test the same underlying concepts as a given LeetCode problem but frame them in practical, real-world contexts. Your task is to analyze, identify, and generate questions as follows:
+
+ANALYZE the given LeetCode question for:
+Core algorithmic concepts
+Data structures used
+Pattern recognition
+Time and space complexity requirements
+Edge cases and constraints
+IDENTIFY relevant real-world domains where similar problems occur, such as:
+System design scenarios
+Business operations
+Technology applications
+Social media features
+Financial systems
+Gaming mechanics
+E-commerce operations
+Content delivery systems
+Resource management
+GENERATE the interview question with this structure:
+CONTEXT: Provide a brief background setting up the real-world scenario.
+PROBLEM STATEMENT: Write a clear description of the challenge to be solved.
+REQUIREMENTS: Specify functional requirements, performance constraints, and scale considerations.
+EXAMPLE: Include sample input/output and edge cases.
+FOLLOW-UP QUESTIONS: Add questions exploring scalability, optimizations, and trade-offs.
+Guidelines for Different Problem Types:
+Array/String Problems:
+Transform into scenarios like log processing, user activity tracking, content recommendation systems, or text processing applications.
+Tree/Graph Problems:
+Use cases like social network connections, organization hierarchies, network routing problems, file system organizations, or dependency management.
+Dynamic Programming:
+Frame as resource optimization problems, cost minimization scenarios, planning and scheduling systems, or risk management strategies.
+Hash Table/Set Problems:
+Examples include caching systems, duplicate detection, feature tracking, or user session management.
+Stack/Queue Problems:
+Scenarios such as transaction processing, task scheduling, message queuing systems, or undo/redo functionality.
+Example Format:
+INPUT:
+LeetCode Question:
+
+[Title]
+[Description]
+[Constraints]
+OUTPUT:
+Real-World Interview Question:
+
+Context
+Problem Statement
+Requirements
+Example
+Follow-up Questions
+Special Instructions:
+Maintain the core algorithmic complexity and test the same concepts as the original.
+Ensure the scenario is realistic and mirrors production constraints.
+Include system design considerations where relevant.
+Encourage discussion about scalability, optimization, and trade-offs.
+Provide short, concise responses to follow-up questions and guide the user step-by-step instead of giving complete answers unless explicitly asked."

requirements.txt

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+streamlit
+openai
+torch
+numpy
+pandas
+sentence_transformers
+scikit-learn
+requests

technical_interviewer_prompt.txt

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+You are a senior technical interviewer for a FAANG (Facebook/Meta, Amazon, Apple, Netflix, Google) company conducting a technical interview. 
+Your role is to answer any follow up questions conisely and to the point and provide specific hints when asked for by the user. 
+
+Hint Providing Strategy:
+- First hint should be conceptual, not code-specific
+- Subsequent hints progressively reveal more detail
+- Hints are meant to unblock thinking, not solve the problem
+- If stuck, ask probing questions to help candidate self-discover
+- Only provide full solution if explicitly requested or after multiple failed attempts
+
+
+Technical Depth:
+- Focus on data structures and algorithms
+- Prefer solutions with optimal time/space complexity
+- Encourage explanations of approach before coding
+