mirror of
https://github.com/arc53/DocsGPT
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96 lines
2.8 KiB
Python
96 lines
2.8 KiB
Python
import ast
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import json
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from pathlib import Path
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import dotenv
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from langchain.llms import OpenAI
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from langchain.prompts import PromptTemplate
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dotenv.load_dotenv()
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ps = list(Path("inputs").glob("**/*.py"))
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data = []
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sources = []
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for p in ps:
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with open(p) as f:
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data.append(f.read())
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sources.append(p)
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def get_functions_in_class(node):
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functions = []
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functions_code = []
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for child in node.body:
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if isinstance(child, ast.FunctionDef):
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functions.append(child.name)
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functions_code.append(ast.unparse(child))
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return functions, functions_code
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def get_classes_and_functions(source_code):
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tree = ast.parse(source_code)
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classes = {}
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for node in tree.body:
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if isinstance(node, ast.ClassDef):
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class_name = node.name
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function_name, function = get_functions_in_class(node)
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# join function name and function code
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functions = dict(zip(function_name, function))
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classes[class_name] = functions
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return classes
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structure_dict = {}
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c1 = 0
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for code in data:
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classes = get_classes_and_functions(ast.parse(code))
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source = str(sources[c1])
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structure_dict[source] = classes
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c1 += 1
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# save the structure dict as json
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with open('structure_dict.json', 'w') as f:
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json.dump(structure_dict, f)
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if not Path("outputs").exists():
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Path("outputs").mkdir()
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c1 = len(structure_dict)
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c2 = 0
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for source, classes in structure_dict.items():
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c2 += 1
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print(f"Processing file {c2}/{c1}")
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f1 = len(classes)
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f2 = 0
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for class_name, functions in classes.items():
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f2 += 1
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print(f"Processing class {f2}/{f1}")
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source_w = source.replace("inputs/", "")
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source_w = source_w.replace(".py", ".txt")
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if not Path(f"outputs/{source_w}").exists():
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with open(f"outputs/{source_w}", "w") as f:
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f.write(f"Class: {class_name}")
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else:
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with open(f"outputs/{source_w}", "a") as f:
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f.write(f"\n\nClass: {class_name}")
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# append class name to the front
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for function in functions:
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b1 = len(functions)
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b2 = 0
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print(f"Processing function {b2}/{b1}")
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b2 += 1
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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=functions[function]))
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if not Path(f"outputs/{source_w}").exists():
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with open(f"outputs/{source_w}", "w") as f:
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f.write(f"Function: {functions[function]}, \nDocumentation: {response}")
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else:
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with open(f"outputs/{source_w}", "a") as f:
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f.write(f"\n\nFunction: {functions[function]}, \nDocumentation: {response}")
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