analyzer.py

import openai
import os

def analyze_code(code: str) -> str:
    """
    Uses OpenAI's GPT-4.1 mini model to analyze the given code.
    Returns the analysis as a string.
    """
    system_prompt = "You are a helpful assistant. Analyze the code given to you. Provide insights, strengths, weaknesses, and suggestions for improvement."
    response = openai.ChatCompletion.create(
        model="gpt-4.1-mini",  # GPT-4.1 mini
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": code}
        ],
        max_tokens=512,
        temperature=0.7
    )
    return response.choices[0].message["content"]

def combine_repo_files_for_llm(repo_dir="repo_files", output_file="combined_repo.txt"):
    """
    Combines all .py and .md files in the given directory (recursively) into a single text file.
    Returns the path to the combined file.
    """
    combined_content = []
    for root, _, files in os.walk(repo_dir):
        for file in files:
            if file.endswith(".py") or file.endswith(".md"):
                file_path = os.path.join(root, file)
                try:
                    with open(file_path, "r", encoding="utf-8") as f:
                        combined_content.append(f"\n# ===== File: {file} =====\n")
                        combined_content.append(f.read())
                except Exception as e:
                    combined_content.append(f"\n# Could not read {file_path}: {e}\n")
    with open(output_file, "w", encoding="utf-8") as out_f:
        out_f.write("\n".join(combined_content))
    return output_file

def analyze_combined_file(output_file="combined_repo.txt"):
    """
    Reads the combined file and passes its contents to analyze_code, returning the LLM's output.
    """
    try:
        with open(output_file, "r", encoding="utf-8") as f:
            code = f.read()
        return analyze_code(code)
    except Exception as e:
        return f"Error analyzing combined file: {e}"