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Update app.py
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app.py
CHANGED
@@ -6,6 +6,7 @@ import numpy as np
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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import torch
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# Set up OpenAI client
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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@@ -43,6 +44,15 @@ if "follow_up_mode" not in st.session_state:
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if "generated_question" not in st.session_state:
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st.session_state.generated_question = None # Stores the generated question for persistence
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if "debug_logs" not in st.session_state:
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st.session_state.debug_logs = None # Stores debug logs for toggling
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@@ -78,6 +88,32 @@ def generate_response(messages):
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return response.choices[0].message.content
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# User input form for generating a new question
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with st.form(key="input_form"):
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company = st.text_input("Company", value="Google") # Default value: Google
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@@ -97,13 +133,17 @@ if generate_button:
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# Prepare a detailed prompt for GPT using the top question's details
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detailed_prompt = (
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f"Transform this LeetCode question into a real-world interview scenario
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f"**Company**: {top_question['company']}\n"
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f"**Question Name**: {top_question['questionName']}\n"
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f"**Difficulty Level**: {top_question['difficulty level']}\n"
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f"**Tags**: {top_question['Tags']}\n"
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f"**Content**: {top_question['Content']}\n"
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f"\nPlease create a real-world interview question based on this information."
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)
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# Generate response using OpenAI API with detailed prompt and debugging logs
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@@ -112,6 +152,12 @@ if generate_button:
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# Store generated question in session state for persistence in sidebar and follow-up conversation state
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st.session_state.generated_question = response
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# Enable follow-up mode after generating the initial question
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st.session_state.follow_up_mode = True
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@@ -164,23 +210,51 @@ if st.session_state.generated_question:
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else:
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st.sidebar.markdown("_No question generated yet._")
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# Right sidebar toggleable debug logs and code interpreter section
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with st.expander("Debug Logs (Toggle On/Off)", expanded=False):
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if st.session_state.debug_logs:
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st.write(st.session_state.debug_logs)
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st.sidebar.markdown("---")
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st.sidebar.markdown("## Python Code Interpreter")
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if st.sidebar.button("Run Code"):
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try:
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exec_globals = {}
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else:
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st.sidebar.
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except Exception as e:
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st.sidebar.error(f"Error: {e}")
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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import torch
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import re
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# Set up OpenAI client
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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if "generated_question" not in st.session_state:
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st.session_state.generated_question = None # Stores the generated question for persistence
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if "code_template" not in st.session_state:
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st.session_state.code_template = "" # Stores the code template
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if "sample_test_case" not in st.session_state:
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st.session_state.sample_test_case = "" # Stores the sample test case
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if "expected_output" not in st.session_state:
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st.session_state.expected_output = "" # Stores the expected output
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if "debug_logs" not in st.session_state:
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st.session_state.debug_logs = None # Stores debug logs for toggling
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return response.choices[0].message.content
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# Function to extract code template and sample test case from the generated question
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def extract_code_and_test_case(generated_question):
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code_template = ""
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sample_test_case = ""
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expected_output = ""
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# Extract code template
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code_match = re.search(r'```python(.*?)```', generated_question, re.DOTALL)
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if code_match:
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code_template = code_match.group(1).strip()
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else:
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# Default code template if none is found
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code_template = "# Write your code here\n"
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# Extract sample test case and expected output
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test_case_match = re.search(r'Sample Input:\s*(.*?)\n', generated_question, re.DOTALL)
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expected_output_match = re.search(r'Expected Output:\s*(.*?)\n', generated_question, re.DOTALL)
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if test_case_match and expected_output_match:
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sample_test_case = test_case_match.group(1).strip()
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expected_output = expected_output_match.group(1).strip()
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else:
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sample_test_case = ""
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expected_output = ""
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return code_template, sample_test_case, expected_output
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# User input form for generating a new question
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with st.form(key="input_form"):
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company = st.text_input("Company", value="Google") # Default value: Google
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# Prepare a detailed prompt for GPT using the top question's details
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detailed_prompt = (
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f"Transform this LeetCode question into a real-world interview scenario.\n\n"
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f"**Company**: {top_question['company']}\n"
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f"**Question Name**: {top_question['questionName']}\n"
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f"**Difficulty Level**: {top_question['difficulty level']}\n"
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f"**Tags**: {top_question['Tags']}\n"
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f"**Content**: {top_question['Content']}\n"
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f"\nPlease create a real-world interview question based on this information. "
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f"Include the following sections:\n\n"
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f"- Problem Description\n"
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f"- Code Template (in a Python code block)\n"
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f"- Sample Input and Expected Output (clearly separated)\n"
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)
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# Generate response using OpenAI API with detailed prompt and debugging logs
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# Store generated question in session state for persistence in sidebar and follow-up conversation state
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st.session_state.generated_question = response
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# Extract code template and sample test case
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code_template, sample_test_case, expected_output = extract_code_and_test_case(response)
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st.session_state.code_template = code_template
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st.session_state.sample_test_case = sample_test_case
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st.session_state.expected_output = expected_output
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# Enable follow-up mode after generating the initial question
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st.session_state.follow_up_mode = True
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else:
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st.sidebar.markdown("_No question generated yet._")
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st.sidebar.markdown("---")
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st.sidebar.markdown("## Python Code Interpreter")
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# Pre-fill code interpreter with code template after question generation
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if st.session_state.code_template:
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code_input = st.sidebar.text_area("Write your Python code here:", value=st.session_state.code_template, height=300)
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else:
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code_input = st.sidebar.text_area("Write your Python code here:", height=300)
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if st.sidebar.button("Run Code"):
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try:
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# Prepare the code for execution
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exec_globals = {}
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# Create a function wrapper to execute the user's code
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exec(f"def user_solution():\n{code_input}", exec_globals)
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user_solution = exec_globals.get('user_solution', None)
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# Prepare sample test case execution
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if st.session_state.sample_test_case:
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# Assume the sample test case is in the format of arguments to the function
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test_case = st.session_state.sample_test_case
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# Evaluate the test case safely
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test_args = eval(test_case)
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if not isinstance(test_args, tuple):
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test_args = (test_args,)
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# Capture the output
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returned_output = user_solution(*test_args)
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else:
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returned_output = user_solution()
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# Display the expected output and returned output
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st.sidebar.markdown("### Sample Test Case Result:")
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st.sidebar.markdown(f"**Sample Input:** {st.session_state.sample_test_case}")
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st.sidebar.markdown(f"**Expected Output:** {st.session_state.expected_output}")
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st.sidebar.markdown(f"**Your Output:** {returned_output}")
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# Compare outputs
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if str(returned_output) == st.session_state.expected_output:
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st.sidebar.success("Your output matches the expected output!")
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else:
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st.sidebar.error("Your output does not match the expected output.")
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except Exception as e:
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st.sidebar.error(f"Error: {e}")
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# Right sidebar toggleable debug logs and code interpreter section
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with st.expander("Debug Logs (Toggle On/Off)", expanded=False):
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if st.session_state.debug_logs:
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st.write(st.session_state.debug_logs)
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