krishshharma/Code-Explainer

1code_snippet,output,flag,username,timestamp
2"print(""Hello world"")3"," The provided code is not related to the Fibonacci function you've shared. Instead, it is a separate piece of Python code that prints ""Hello world"" when executed.4 5This particular code does not involve any computations or loops, making its time complexity O(1) (constant). It is most commonly used as an introductory example to demonstrate the process of writing and running a simple program in various programming languages. The output of this code is always the same string ""Hello world"" regardless of any input provided to the function.6 7In summary, while the Fibonacci function calculates Fibonacci numbers based on the input `n`, the ""Hello world"" code simply prints the given string without any dependencies on inputs or calculations.",,,2025-05-08 23:25:15.166055
8"'from langchain_community.vectorstores import Chroma9from langchain_community.embeddings import HuggingFaceEmbeddings10from langchain_text_splitters import CharacterTextSplitter11from langchain.chains import RetrievalQA12from langchain_community.llms import Ollama13import gradio as gr14import os15from dotenv import load_dotenv16 17# Load environment variables18load_dotenv()19 20# 1. Load and prepare documents21def load_docs():22    docs = []23    # Load code examples24    try:25        with open(""data/sample_code.py"", ""r"") as f:26            code = f.read()27            docs.append(code)28    except FileNotFoundError:29        print(""Warning: data/sample_code.py not found"")30    31    # Load documentation32    try:33        with open(""data/docs.txt"", ""r"") as f:34            text = f.read()35            docs.append(text)36    except FileNotFoundError:37        print(""Warning: data/docs.txt not found"")38    39    if not docs:40        raise ValueError(""No documents found to process"")41    42    # Split into chunks43    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=50)44    return text_splitter.split_text(""\n\n"".join(docs))45 46# 2. Create vector database47def setup_vectordb():48    documents = load_docs()49    # Using HuggingFace embeddings as a reliable alternative50    embeddings = HuggingFaceEmbeddings(model_name=""sentence-transformers/all-MiniLM-L6-v2"")51    return Chroma.from_texts(documents, embeddings)52 53# 3. Initialize LLM54llm = Ollama(model=""mistral"")55 56# 4. Create RAG chain57db = setup_vectordb()58qa_chain = RetrievalQA.from_chain_type(59    llm,60    retriever=db.as_retriever(search_kwargs={""k"": 2}),61    chain_type=""stuff""62)63 64# 5. Gradio interface65def explain_code(code_snippet):66    if not code_snippet.strip():67        return ""Please enter some code to explain.""68    69    response = qa_chain.invoke({70        ""query"": f""Explain this code: {code_snippet}. Include time complexity and common use cases.""71    })72    return response[""result""]73 74interface = gr.Interface(75    fn=explain_code,76    inputs=gr.Textbox(lines=5, placeholder=""Paste code here...""),77    outputs=""text"",78    title=""AI Code Explainer"",79    description=""Enter code to get an explanation of how it works, its time complexity, and common use cases.""80)81 82if __name__ == ""__main__"":83    interface.launch()84"," This code creates a simple web application using the Gradio library that provides an explanation for given code snippets. The code includes several modules from the ""langchain_community"" library, which seems to be custom or a third-party library not commonly used.85 861. Load and prepare documents: This function loads two files (sample_code.py and docs.txt) containing code examples and documentation respectively, splits them into smaller chunks, and returns the chunked text. If either file is missing, it prints a warning message.87 882. Create vector database: This function sets up a vector database using the Chroma library from the ""langchain_community"" module. It loads the prepared documents, applies HuggingFace embeddings to generate vectors for each document, and creates a Chroma database from these vectors.89 903. Initialize LLM: This line initializes an instance of the Ollama language model (LLM) using the ""mistral"" model from the ""langchain_community"" library.91 924. Create RAG chain: A RetrievalQA chain is created, which is a combination of a language model and a retrieval function for answering questions by searching the vector database for relevant information and using the LLM to provide an answer based on that information. The search keyword ""k"" is set to 2, meaning it will retrieve two most similar documents from the database.93 945. Gradio interface: This part of the code defines a web application using the Gradio library. The `explain_code` function takes a code snippet as input, creates an explanation using the RAG chain, and returns the result as output. The application has a title ""AI Code Explainer"" and a description explaining its purpose. Finally, if the script is run directly (not imported as a module), it launches the web application.95 96Regarding your question about time complexity and common use cases: The time complexity of this code largely depends on the specific functions being used within the loaded code examples and the model used by Ollama for generating explanations. However, the main computational operations include reading files, vectorizing documents using embeddings, and searching in the database. These operations have a time complexity of O(n), where n is the size of the data (number of lines in the file, number of documents, or number of chunks).97 98As for common use cases, this code can be used as an educational tool to help people understand given code snippets better, providing explanations, time complexity information, and common use cases. It's important to note that the quality of generated explanations heavily depends on the accuracy and completeness of the documentation loaded during initialization.",,,2025-05-08 23:27:42.883734
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