import openai
import os
import json

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.
    """
    from openai import OpenAI
    client = OpenAI()
    system_prompt = (
        "You are a helpful assistant. Analyze the code given to you. "
        "Return your response strictly in JSON format with the following keys: "
        "'strength', 'weaknesses', 'speciality', 'relevance rating'. "
        "Do not include any other text outside the JSON."
    )
    response = client.chat.completions.create(
        model="gpt-4-1106-preview",  # 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 parse_llm_json_response(response: str):
    try:
        return json.loads(response)
    except Exception as e:
        return {"error": f"Failed to parse JSON: {e}", "raw": response}

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 = []
    seen_files = set()
    # Priority files
    priority_files = ["app.py", "README.md"]
    for pf in priority_files:
        pf_path = os.path.join(repo_dir, pf)
        if os.path.isfile(pf_path):
            try:
                with open(pf_path, "r", encoding="utf-8") as f:
                    combined_content.append(f"\n# ===== File: {pf} =====\n")
                    combined_content.append(f.read())
                seen_files.add(os.path.abspath(pf_path))
            except Exception as e:
                combined_content.append(f"\n# Could not read {pf_path}: {e}\n")
    # All other .py and .md files
    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)
                abs_path = os.path.abspath(file_path)
                if abs_path in seen_files:
                    continue
                try:
                    with open(file_path, "r", encoding="utf-8") as f:
                        combined_content.append(f"\n# ===== File: {file} =====\n")
                        combined_content.append(f.read())
                    seen_files.add(abs_path)
                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:
            lines = f.readlines()
        code = "".join(lines[:500])
        return analyze_code(code)
    except Exception as e:
        return f"Error analyzing combined file: {e}"