Delete codingprepdemo

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codingprepdemo/README.md

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----
-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

codingprepdemo/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)

codingprepdemo/question_dataset_embeddings.npy

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-oid sha256:c0e8003615af2eb6c5fa4657d5f06c1b4ed26e152173f7c9ef928a9a86d7f170
-size 132

codingprepdemo/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."

codingprepdemo/requirements.txt

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

codingprepdemo/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
-