mirror of
https://github.com/arc53/DocsGPT
synced 2024-11-17 21:26:26 +00:00
122 lines
4.7 KiB
Python
122 lines
4.7 KiB
Python
import ast
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import os
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from pathlib import Path
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import tiktoken
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from langchain.llms import OpenAI
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from langchain.prompts import PromptTemplate
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def find_files(directory):
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files_list = []
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for root, dirs, files in os.walk(directory):
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for file in files:
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if file.endswith('.py'):
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files_list.append(os.path.join(root, file))
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return files_list
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def extract_functions(file_path):
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with open(file_path, 'r') as file:
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source_code = file.read()
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functions = {}
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tree = ast.parse(source_code)
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for node in ast.walk(tree):
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if isinstance(node, ast.FunctionDef):
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func_name = node.name
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func_def = ast.get_source_segment(source_code, node)
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functions[func_name] = func_def
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return functions
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def extract_classes(file_path):
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with open(file_path, 'r') as file:
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source_code = file.read()
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classes = {}
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tree = ast.parse(source_code)
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for node in ast.walk(tree):
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if isinstance(node, ast.ClassDef):
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class_name = node.name
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function_names = []
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for subnode in ast.walk(node):
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if isinstance(subnode, ast.FunctionDef):
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function_names.append(subnode.name)
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classes[class_name] = ", ".join(function_names)
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return classes
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def extract_functions_and_classes(directory):
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files = find_files(directory)
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functions_dict = {}
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classes_dict = {}
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for file in files:
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functions = extract_functions(file)
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if functions:
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functions_dict[file] = functions
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classes = extract_classes(file)
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if classes:
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classes_dict[file] = classes
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return functions_dict, classes_dict
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def parse_functions(functions_dict, formats, dir):
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c1 = len(functions_dict)
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for i, (source, functions) in enumerate(functions_dict.items(), start=1):
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print(f"Processing file {i}/{c1}")
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source_w = source.replace(dir + "/", "").replace("." + formats, ".md")
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subfolders = "/".join(source_w.split("/")[:-1])
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Path(f"outputs/{subfolders}").mkdir(parents=True, exist_ok=True)
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for j, (name, function) in enumerate(functions.items(), start=1):
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print(f"Processing function {j}/{len(functions)}")
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prompt = PromptTemplate(
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input_variables=["code"],
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template="Code: \n{code}, \nDocumentation: ",
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)
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llm = OpenAI(temperature=0)
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response = llm(prompt.format(code=function))
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mode = "a" if Path(f"outputs/{source_w}").exists() else "w"
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with open(f"outputs/{source_w}", mode) as f:
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f.write(
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f"\n\n# Function name: {name} \n\nFunction: \n```\n{function}\n```, \nDocumentation: \n{response}")
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def parse_classes(classes_dict, formats, dir):
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c1 = len(classes_dict)
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for i, (source, classes) in enumerate(classes_dict.items()):
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print(f"Processing file {i + 1}/{c1}")
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source_w = source.replace(dir + "/", "").replace("." + formats, ".md")
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subfolders = "/".join(source_w.split("/")[:-1])
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Path(f"outputs/{subfolders}").mkdir(parents=True, exist_ok=True)
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for name, function_names in classes.items():
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print(f"Processing Class {i + 1}/{c1}")
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prompt = PromptTemplate(
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input_variables=["class_name", "functions_names"],
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template="Class name: {class_name} \nFunctions: {functions_names}, \nDocumentation: ",
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)
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llm = OpenAI(temperature=0)
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response = llm(prompt.format(class_name=name, functions_names=function_names))
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with open(f"outputs/{source_w}", "a" if Path(f"outputs/{source_w}").exists() else "w") as f:
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f.write(f"\n\n# Class name: {name} \n\nFunctions: \n{function_names}, \nDocumentation: \n{response}")
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def transform_to_docs(functions_dict, classes_dict, formats, dir):
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docs_content = ''.join([str(key) + str(value) for key, value in functions_dict.items()])
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docs_content += ''.join([str(key) + str(value) for key, value in classes_dict.items()])
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num_tokens = len(tiktoken.get_encoding("cl100k_base").encode(docs_content))
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total_price = ((num_tokens / 1000) * 0.02)
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print(f"Number of Tokens = {num_tokens:,d}")
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print(f"Approx Cost = ${total_price:,.2f}")
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user_input = input("Price Okay? (Y/N)\n").lower()
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if user_input == "y" or user_input == "":
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if not Path("outputs").exists():
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Path("outputs").mkdir()
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parse_functions(functions_dict, formats, dir)
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parse_classes(classes_dict, formats, dir)
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print("All done!")
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else:
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print("The API was not called. No money was spent.")
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