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import gradio as gr
from langchain_community.chat_models import ChatOpenAI
from meta_prompt.sample_generator import TaskDescriptionGenerator
def process_json(input_json, model_name, generating_batch_size, temperature):
try:
model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
result = generator.process(input_json, generating_batch_size)
description = result["description"]
examples_directly = [[example["input"], example["output"]] for example in result["examples_directly"]["examples"]]
input_analysis = result["examples_from_briefs"]["input_analysis"]
new_example_briefs = result["examples_from_briefs"]["new_example_briefs"]
examples_from_briefs = [[example["input"], example["output"]] for example in result["examples_from_briefs"]["examples"]]
examples = [[example["input"], example["output"]] for example in result["additional_examples"]]
return description, examples_directly, input_analysis, new_example_briefs, examples_from_briefs, examples
except Exception as e:
raise gr.Error(f"An error occurred: {str(e)}")
def generate_description_only(input_json, model_name, temperature):
try:
model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
description = generator.generate_description(input_json)
return description
except Exception as e:
raise gr.Error(f"An error occurred: {str(e)}")
def analyze_input(description, model_name, temperature):
try:
model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
input_analysis = generator.analyze_input(description)
return input_analysis
except Exception as e:
raise gr.Error(f"An error occurred: {str(e)}")
def generate_briefs(description, input_analysis, generating_batch_size, model_name, temperature):
try:
model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
briefs = generator.generate_briefs(description, input_analysis, generating_batch_size)
return briefs
except Exception as e:
raise gr.Error(f"An error occurred: {str(e)}")
def generate_examples_from_briefs(description, new_example_briefs, input_str, generating_batch_size, model_name, temperature):
try:
model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
result = generator.generate_examples_from_briefs(description, new_example_briefs, input_str, generating_batch_size)
examples = [[example["input"], example["output"]] for example in result["examples"]]
return examples
except Exception as e:
raise gr.Error(f"An error occurred: {str(e)}")
def generate_examples_directly(description, raw_example, generating_batch_size, model_name, temperature):
try:
model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
result = generator.generate_examples_directly(description, raw_example, generating_batch_size)
examples = [[example["input"], example["output"]] for example in result["examples"]]
return examples
except Exception as e:
raise gr.Error(f"An error occurred: {str(e)}")
def format_selected_example(evt: gr.SelectData, examples):
if evt.index[0] < len(examples):
selected_example = examples.iloc[evt.index[0]] # Use iloc to access by integer position
json_example = json.dumps({"input": selected_example.iloc[0], "output": selected_example.iloc[1]}, indent=2, ensure_ascii=False)
return json_example
return ""
with gr.Blocks(title="Task Description Generator") as demo:
gr.Markdown("# Task Description Generator")
gr.Markdown("Enter a JSON object with 'input' and 'output' fields to generate a task description and additional examples.")
with gr.Row():
with gr.Column(scale=1): # Inputs column
input_json = gr.Textbox(label="Input JSON", lines=10, show_copy_button=True)
model_name = gr.Dropdown(
label="Model Name",
choices=["llama3-70b-8192", "llama3-8b-8192", "llama-3.1-70b-versatile", "llama-3.1-8b-instant", "gemma2-9b-it"],
value="llama3-70b-8192"
)
temperature = gr.Slider(label="Temperature", value=1.0, minimum=0.0, maximum=1.0, step=0.1)
generating_batch_size = gr.Slider(label="Generating Batch Size", value=3, minimum=1, maximum=10, step=1)
with gr.Row():
submit_button = gr.Button("Generate", variant="primary")
generate_description_button = gr.Button("Generate Description", variant="secondary")
with gr.Column(scale=1): # Outputs column
description_output = gr.Textbox(label="Description", lines=5, show_copy_button=True)
with gr.Row():
generate_examples_directly_button = gr.Button("Generate Examples Directly", variant="secondary")
analyze_input_button = gr.Button("Analyze Input", variant="secondary")
examples_directly_output = gr.DataFrame(label="Examples Directly", headers=["Input", "Output"], interactive=False)
input_analysis_output = gr.Textbox(label="Input Analysis", lines=5, show_copy_button=True)
generate_briefs_button = gr.Button("Generate Briefs", variant="secondary")
example_briefs_output = gr.Textbox(label="Example Briefs", lines=5, show_copy_button=True)
generate_examples_from_briefs_button = gr.Button("Generate Examples from Briefs", variant="secondary")
examples_from_briefs_output = gr.DataFrame(label="Examples from Briefs", headers=["Input", "Output"], interactive=False)
examples_output = gr.DataFrame(label="Examples", headers=["Input", "Output"], interactive=False)
new_example_json = gr.Textbox(label="New Example JSON", lines=5, show_copy_button=True)
clear_button = gr.ClearButton([input_json, description_output, input_analysis_output,
example_briefs_output, examples_from_briefs_output,
examples_output, new_example_json])
submit_button.click(
fn=process_json,
inputs=[input_json, model_name, generating_batch_size, temperature],
outputs=[description_output, examples_directly_output, input_analysis_output, example_briefs_output, examples_from_briefs_output, examples_output]
)
generate_description_button.click(
fn=generate_description_only,
inputs=[input_json, model_name, temperature],
outputs=[description_output]
)
generate_examples_directly_button.click(
fn=generate_examples_directly,
inputs=[description_output, input_json, generating_batch_size, model_name, temperature],
outputs=[examples_directly_output]
)
analyze_input_button.click(
fn=analyze_input,
inputs=[description_output, model_name, temperature],
outputs=[input_analysis_output]
)
generate_briefs_button.click(
fn=generate_briefs,
inputs=[description_output, input_analysis_output, generating_batch_size, model_name, temperature],
outputs=[example_briefs_output]
)
generate_examples_from_briefs_button.click(
fn=generate_examples_from_briefs,
inputs=[description_output, example_briefs_output, input_json, generating_batch_size, model_name, temperature],
outputs=[examples_from_briefs_output]
)
examples_directly_output.select(
fn=format_selected_example,
inputs=[examples_directly_output],
outputs=[new_example_json]
)
examples_from_briefs_output.select(
fn=format_selected_example,
inputs=[examples_from_briefs_output],
outputs=[new_example_json]
)
examples_output.select(
fn=format_selected_example,
inputs=[examples_output],
outputs=[new_example_json]
)
gr.Markdown("### Manual Flagging")
with gr.Row():
flag_button = gr.Button("Flag")
flag_reason = gr.Textbox(label="Reason for flagging")
flagging_callback = gr.CSVLogger()
flag_button.click(
lambda *args: flagging_callback.flag(args),
inputs=[input_json, model_name, generating_batch_size, description_output, examples_output, flag_reason],
outputs=[]
)
if __name__ == "__main__":
demo.launch() |