from .utils import fetch_code
import re
import numpy as np
from core.conversion import noop_logger

def is_applicable(llm, readme, log_fn=noop_logger):
    res_training = "NA"
    res_evaluation = "NA"
    res_weights = "NA"
    
    if (llm):
        log_fn("TITLE", "\nChecking what parts of the evaluations are applicable...")
        res_training = llm.predict("STRICT", f"{readme}\nBased on the readme above, should the repository contain code for training a model?")
        res_evaluation = llm.predict("STRICT", f"{readme}\nBased on the readme above, should the repository contain code for evaluating a model?")
        res_weights = llm.predict("STRICT", f"{readme}\nBased on the readme above, should the repository contain code for loading pre-trained weights?")
    
    applicable = f"{res_training}/{res_evaluation}/{res_weights}"
    return applicable

def evaluate(llm, zip, readmes, log_fn=noop_logger):
  log_fn("TITLE", "\nEvaluating code documentation...")
  overall = "No"

  code_to_comment_ratio = get_code_to_comment_ratio(zip)
  log_fn("LOG", f"Your python scripts have a comment-to-code ratio of {np.round(code_to_comment_ratio, 2)}%.")
  result = { "dependencies": "No", "training": "No", "evaluation": "No", "weights": "No", "scripts": "No" }
  for readme in readmes:
      non_empty_rows = [row for row in readme.split("\n") if row != ""]
      if (len(non_empty_rows) < 5):
          log_fn("ERROR", "Readme file has very few lines")

          if (llm):
              code = fetch_code(zip)
              if (llm):
                  summary = llm.predict("HELP", f"{code}\nBased on the readme file above can you give a quick summary of this repository? Please use references to file names on the repository.")
                  log_fn("LOG", f"Based on the code, your readme file could be something like...\n{summary}")
          return overall

      if (count_code_lines(non_empty_rows) > 2):
          log_fn("LOG", "Readme file contains python examples.")
          result["scripts"] = "Yes"
      

      if (llm):
          prompt = f'{readme}\n \
          Is this README file is enough to find what \
          package dependencies you need to install and how to train \
          and evaluate the proposed model?'
          llm.predict("HELP", prompt)

      if ((len(re.findall("package", readme, re.IGNORECASE)) == 0) & \
          (len(re.findall("dependenc", readme, re.IGNORECASE)) == 0) & \
          (len(re.findall("requirement", readme, re.IGNORECASE)) == 0)):
          log_fn("ERROR", "Readme file missing information about package dependencies")
      else:
          result["dependencies"] = "Yes"

      if ((len(re.findall("train", readme, re.IGNORECASE)) == 0)):
          log_fn("ERROR", "Readme file missing training information")
      else:
          result["training"] = "Yes"

      if ((len(re.findall("demo", readme, re.IGNORECASE)) == 0) | (len(re.findall("evaluat", readme, re.IGNORECASE)) == 0)):
          log_fn("ERROR", "Readme file missing testing information")
      else:
          result["evaluating"] = "Yes"

      if ((len(re.findall("example", readme, re.IGNORECASE)) == 0)):
          log_fn("LOG", "Readme file contains no links to examples")
      else:
          result["evaluating"] = "Yes"

  score = np.sum(np.array(list(result.values()), dtype=str) == "Yes")
  return "Yes" if score >= 2 else "No"

def count_comment_lines(lines):
    # Initialize counters
    single_line_comments = 0
    multi_line_comments = 0
    in_multiline_comment = False

    for line in lines:
        stripped_line = line.strip()

        # Check for single-line comments
        if stripped_line.startswith('#'):
            single_line_comments += 1

        # Check for multi-line comment (docstring) start or end
        if stripped_line.startswith('"""') or stripped_line.startswith("'''"):
            if not in_multiline_comment:
                # Starting a new multi-line comment
                in_multiline_comment = True
                multi_line_comments += 1  # Count the start line itself
            else:
                # Ending an existing multi-line comment
                in_multiline_comment = False
                multi_line_comments += 1  # Count the end line itself
        elif in_multiline_comment:
            # Continue counting lines within a multi-line comment
            multi_line_comments += 1

    return single_line_comments, multi_line_comments

def get_code_to_comment_ratio(zip):
    python_files = [file_path for file_path in zip.namelist() if (file_path.endswith(".py") | file_path.endswith(".ipynb"))]
    code_line_count = 1
    comment_line_count = 0
    for file in python_files:
        file_lines = zip.open(file).read().decode("utf-8").split('\n')
        sl_comm, ml_comm = count_comment_lines(file_lines)
        comment_line_count += sl_comm + ml_comm
        code_line_count += len(file_lines) - (sl_comm + ml_comm)
    code_to_comment_ratio = 100 * comment_line_count / code_line_count

    return code_to_comment_ratio

def count_code_lines(lines):
    is_code_snippet = False
    code_line_count = 0

    for line in lines:
        stripped_line = line.strip()

        if stripped_line.startswith('```'):
            if not is_code_snippet:
                is_code_snippet = True
                code_line_count += 1
            else:
                is_code_snippet = False
                code_line_count += 1
        elif is_code_snippet:
            code_line_count += 1

    return int(code_line_count / 2)