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import inspect
import json
import ast
import gradio as gr
def function_to_json(func_str, func_description, param_descriptions, required_params):
# Create a new Module instance with the missing field
module_ast = ast.Module(body=[ast.Pass()], type_ignores=[])
# Parse the function string into the AST and replace the body
func_ast = ast.parse(func_str)
module_ast.body = func_ast.body
# Extract the function definition node
func_def = next(node for node in module_ast.body if isinstance(node, ast.FunctionDef))
# Get function signature
code_obj = compile(module_ast, '<string>', 'exec')
func_globals = {}
exec(code_obj, func_globals)
signature = inspect.signature(func_globals[func_def.name])
parameters = signature.parameters
# Convert param_descriptions string to a dictionary
param_desc_dict = json.loads(param_descriptions)
# Create JSON structure
function_json = {
"name": func_def.name,
"description": func_description,
"parameters": {
"type": "object",
"properties": {}
}
}
# Add parameter information to JSON structure
for param_name, param in parameters.items():
param_info = param_desc_dict.get(param_name, {})
param_type = param_info.get("type", str(param.annotation))
param_desc = param_info.get("description", param_name.replace('_', ' '))
function_json["parameters"]["properties"][param_name] = {
"type": param_type,
"description": param_desc
}
# Add required parameters based on user input
if param_name in required_params:
if "required" not in function_json["parameters"]:
function_json["parameters"]["required"] = []
function_json["parameters"]["required"].append(param_name)
return json.dumps(function_json, indent=4)
""" Example uasge:
# Example usage with user-provided function information
sample_function_str = '''
def generate_music(input_text, input_melody):
''' generate music based on an input text '''
client = Client("https://ysharma-musicgendupe.hf.space/", hf_token="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")
result = client.predict(
"melody",
input_text,
input_melody,
5,
250,
0,
1,
3,
fn_index=1
)
return result
'''
sample_func_description = "generate music based on an input text and input melody"
sample_param_descriptions = '''
{
"input_text": {
"type": "str",
"description": "Input text for music generation."
},
"input_melody": {
"type": "str",
"description": "File path of the input melody."
}
}
'''
sample_required_params = ["input_text"]
# Convert the sample function information to JSON
json_str = function_to_json(sample_function_str, sample_func_description, sample_param_descriptions, sample_required_params)
print(json_str)
{
"name": "generate_music",
"description": "generate music based on an input text and input melody",
"parameters": {
"type": "object",
"properties": {
"input_text": {
"type": "str",
"description": "Input text for music generation."
},
"input_melody": {
"type": "str",
"description": "File path of the input melody."
}
},
"required": [
"input_text"
]
}
}
"""
title = "<h1 align='center'>Convert any function to function definitions required for GPT</h1>"
demo = gr.Blocks()
with demo:
gr.HTML(title)
with gr.Row():
input_function_str = gr.Code(label="Enter function definition", language='python', lines=10)
#input_function_str = gr.Textbox(lines=10, label='Enter function definition')
with gr.Column():
input_func_description = gr.Textbox(placeholder='', label='Enter your function description:')
input_param_description = gr.Textbox(
placeholder="""Enter description as a dictionary with keys as param_name and values as param type and description as shown, eg. -
{
"param1": {
"type": "str",
"description": "description of param1"
},
"param2": {
"type": "int/float/list/tuple/dict/set/bool etc..",
"description": "description of param2"
}
}""",
label='Enter descriptions for parameters:')
input_required_params = gr.Textbox(placeholder="""Enter a list of required parameters, eg. - ['param1', 'param2', ...]""",
label='Enter required parameters for your function:')
generate_json = gr.Button('Get JSON definition')
gpt_function = gr.Code(label="GPT function definition", language='python', lines=7)
generate_json.click(function_to_json,
[input_function_str, input_func_description, input_param_description, input_required_params],
[gpt_function])
gr.Examples(
[ ["""
def generate_music(input_text, input_melody):
"generate music based on an input text"
client = Client("https://ysharma-musicgendupe.hf.space/", hf_token="hf_...")
result = client.predict(
"melody",
input_text,
input_melody,
5,
250,
0,
1,
3,
fn_index=1
)
return result
""",
"""Generate music based on an input text.""",
"""{
"input_text": {
"type": "string",
"description": "Input text for music generation."
},
"input_melody": {
"type": "string",
"description": "File path of the input melody."
}
}""",
"""["input_text"]""" ],
["""
def generate_image(prompt):
client = Client("https://jingyechen22-textdiffuser.hf.space/")
result = client.predict(
prompt,
20,
7.5,
1,
"Stable Diffusion v2.1",
fn_index=1)
return result[0]
""",
"""generate image based on the input text prompt""",
"""{
"prompt": {
"type": "string",
"description": "input text prompt for the image generation."
}
}""",
"""["prompt"]""" ,
],
],
[input_function_str, input_func_description, input_param_description, input_required_params],
)
demo.launch() #(debug=True)
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