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| import pandas as pd | |
| import streamlit as st | |
| import json | |
| from langchain_openai 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: | |
| st.warning(f"An error occurred: {str(e)}. Returning default values.") | |
| return "", [], "", [], [], [] | |
| 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: | |
| st.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: | |
| st.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: | |
| st.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: | |
| st.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: | |
| st.error(f"An error occurred: {str(e)}") | |
| def example_directly_selected(): | |
| if 'selected_example_directly_id' in st.session_state: | |
| try: | |
| selected_example_ids = st.session_state.selected_example_directly_id[ | |
| 'selection']['rows'] | |
| # set selected examples to the selected rows if there are any | |
| if selected_example_ids: | |
| selected_examples = st.session_state.examples_directly_dataframe.iloc[selected_example_ids].to_dict( | |
| 'records') | |
| st.session_state.selected_example = pd.DataFrame(selected_examples) # Convert to DataFrame | |
| else: | |
| st.session_state.selected_example = None | |
| except Exception as e: | |
| st.session_state.selected_example = None | |
| def example_from_briefs_selected(): | |
| if 'selected_example_from_briefs_id' in st.session_state: | |
| try: | |
| selected_example_ids = st.session_state.selected_example_from_briefs_id[ | |
| 'selection']['rows'] | |
| # set selected examples to the selected rows if there are any | |
| if selected_example_ids: | |
| selected_examples = st.session_state.examples_from_briefs_dataframe.iloc[selected_example_ids].to_dict( | |
| 'records') | |
| st.session_state.selected_example = pd.DataFrame(selected_examples) # Convert to DataFrame | |
| else: | |
| st.session_state.selected_example = None | |
| except Exception as e: | |
| st.session_state.selected_example = None | |
| def example_selected(): | |
| if 'selected_example_id' in st.session_state: | |
| try: | |
| selected_example_ids = st.session_state.selected_example_id['selection']['rows'] | |
| # set selected examples to the selected rows if there are any | |
| if selected_example_ids: | |
| selected_examples = st.session_state.examples_dataframe.iloc[selected_example_ids].to_dict( | |
| 'records') | |
| st.session_state.selected_example = pd.DataFrame(selected_examples) # Convert to DataFrame | |
| else: | |
| st.session_state.selected_example = None | |
| except Exception as e: | |
| st.session_state.selected_example = None | |
| # Session State | |
| if 'input_data' not in st.session_state: | |
| st.session_state.input_data = pd.DataFrame(columns=["Input", "Output"]) | |
| if 'description_output_text' not in st.session_state: | |
| st.session_state.description_output_text = '' | |
| if 'input_analysis_output_text' not in st.session_state: | |
| st.session_state.input_analysis_output_text = '' | |
| if 'example_briefs_output_text' not in st.session_state: | |
| st.session_state.example_briefs_output_text = '' | |
| if 'examples_from_briefs_dataframe' not in st.session_state: | |
| st.session_state.examples_from_briefs_dataframe = pd.DataFrame(columns=[ | |
| "Input", "Output"]) | |
| if 'examples_directly_dataframe' not in st.session_state: | |
| st.session_state.examples_directly_dataframe = pd.DataFrame( | |
| columns=["Input", "Output"]) | |
| if 'examples_dataframe' not in st.session_state: | |
| st.session_state.examples_dataframe = pd.DataFrame( | |
| columns=["Input", "Output"]) | |
| if 'selected_example' not in st.session_state: | |
| st.session_state.selected_example = None | |
| # if 'input_file' not in st.session_state: | |
| # st.session_state.input_file = None | |
| def update_description_output_text(): | |
| input_json = package_input_data() | |
| st.session_state.description_output_text = generate_description_only( | |
| input_json, model_name, temperature) | |
| def update_input_analysis_output_text(): | |
| st.session_state.input_analysis_output_text = analyze_input( | |
| description_output, model_name, temperature) | |
| def update_example_briefs_output_text(): | |
| st.session_state.example_briefs_output_text = generate_briefs( | |
| description_output, input_analysis_output, generating_batch_size, model_name, temperature) | |
| def update_examples_from_briefs_dataframe(): | |
| input_json = package_input_data() | |
| examples = generate_examples_from_briefs( | |
| description_output, example_briefs_output, input_json, generating_batch_size, model_name, temperature) | |
| st.session_state.examples_from_briefs_dataframe = pd.DataFrame( | |
| examples, columns=["Input", "Output"]) | |
| def update_examples_directly_dataframe(): | |
| input_json = package_input_data() | |
| examples = generate_examples_directly( | |
| description_output, input_json, generating_batch_size, model_name, temperature) | |
| st.session_state.examples_directly_dataframe = pd.DataFrame( | |
| examples, columns=["Input", "Output"]) | |
| def generate_examples_dataframe(): | |
| input_json = package_input_data() | |
| result = process_json(input_json, model_name, | |
| generating_batch_size, temperature) | |
| description, examples_directly, input_analysis, new_example_briefs, examples_from_briefs, examples = result | |
| st.session_state.description_output_text = description | |
| st.session_state.examples_directly_dataframe = pd.DataFrame( | |
| examples_directly, columns=["Input", "Output"]) | |
| st.session_state.input_analysis_output_text = input_analysis | |
| st.session_state.example_briefs_output_text = new_example_briefs | |
| st.session_state.examples_from_briefs_dataframe = pd.DataFrame( | |
| examples_from_briefs, columns=["Input", "Output"]) | |
| st.session_state.examples_dataframe = pd.DataFrame( | |
| examples, columns=["Input", "Output"]) | |
| st.session_state.selected_example = None | |
| def package_input_data(): | |
| data = input_data.to_dict(orient='records') | |
| lowered_data = [{k.lower(): v for k, v in d.items()} for d in data] | |
| return json.dumps(lowered_data, ensure_ascii=False) | |
| def export_input_data_to_json(): | |
| input_data_json = package_input_data() | |
| st.download_button( | |
| label="Download input data as JSON", | |
| data=input_data_json, | |
| file_name="input_data.json", | |
| mime="application/json" | |
| ) | |
| def import_input_data_from_json(): | |
| try: | |
| if 'input_file' in st.session_state and st.session_state.input_file is not None: | |
| data = st.session_state.input_file.getvalue() | |
| data = json.loads(data) | |
| data = [{k.capitalize(): v for k, v in d.items()} for d in data] | |
| st.session_state.input_data = pd.DataFrame(data) | |
| except Exception as e: | |
| st.error(f"Failed to import JSON: {str(e)}") | |
| # Streamlit UI | |
| st.title("LLM Task Example Generator") | |
| st.markdown("Enter input-output pairs in the table below to generate a task description, analysis, and additional examples.") | |
| # Input column | |
| input_data = st.data_editor( | |
| st.session_state.input_data, | |
| num_rows="dynamic", | |
| use_container_width=True, | |
| column_config={ | |
| "Input": st.column_config.TextColumn("Input", width="large"), | |
| "Output": st.column_config.TextColumn("Output", width="large"), | |
| }, | |
| ) | |
| with st.expander("Model Settings"): | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| input_file = st.file_uploader( | |
| label="Import Input Data from JSON", | |
| type="json", | |
| key="input_file", | |
| on_change=import_input_data_from_json | |
| ) | |
| with col2: | |
| export_button = st.button( # Add the export button | |
| "Export Input Data to JSON", on_click=export_input_data_to_json | |
| ) | |
| model_name = st.selectbox( | |
| "Model Name", | |
| ["llama3-70b-8192", "llama3-8b-8192", "llama-3.1-70b-versatile", | |
| "llama-3.1-8b-instant", "gemma2-9b-it"], | |
| index=0 | |
| ) | |
| temperature = st.slider("Temperature", 0.0, 1.0, 1.0, 0.1) | |
| generating_batch_size = st.slider("Generating Batch Size", 1, 10, 3, 1) | |
| submit_button = st.button( | |
| "Generate", type="primary", on_click=generate_examples_dataframe) | |
| with st.expander("Description and Analysis"): | |
| generate_description_button = st.button( | |
| "Generate Description", on_click=update_description_output_text) | |
| description_output = st.text_area( | |
| "Description", value=st.session_state.description_output_text, height=100) | |
| col3, col4 = st.columns(2) | |
| with col3: | |
| generate_examples_directly_button = st.button( | |
| "Generate Examples Directly", on_click=update_examples_directly_dataframe) | |
| with col4: | |
| analyze_input_button = st.button( | |
| "Analyze Input", on_click=update_input_analysis_output_text) | |
| examples_directly_output = st.dataframe(st.session_state.examples_directly_dataframe, use_container_width=True, | |
| selection_mode="multi-row", key="selected_example_directly_id", | |
| on_select=example_directly_selected) | |
| input_analysis_output = st.text_area( | |
| "Input Analysis", value=st.session_state.input_analysis_output_text, height=100) | |
| generate_briefs_button = st.button( | |
| "Generate Briefs", on_click=update_example_briefs_output_text) | |
| example_briefs_output = st.text_area( | |
| "Example Briefs", value=st.session_state.example_briefs_output_text, height=100) | |
| generate_examples_from_briefs_button = st.button( | |
| "Generate Examples from Briefs", on_click=update_examples_from_briefs_dataframe) | |
| examples_from_briefs_output = st.dataframe(st.session_state.examples_from_briefs_dataframe, use_container_width=True, | |
| selection_mode="multi-row", key="selected_example_from_briefs_id", | |
| on_select=example_from_briefs_selected) | |
| examples_output = st.dataframe(st.session_state.examples_dataframe, use_container_width=True, | |
| selection_mode="multi-row", key="selected_example_id", on_select=example_selected) | |
| def append_selected_to_input_data(): | |
| if st.session_state.selected_example is not None: | |
| st.session_state.input_data = pd.concat( | |
| [st.session_state.input_data, st.session_state.selected_example], ignore_index=True) | |
| st.session_state.selected_example = None | |
| def show_sidebar(): | |
| if st.session_state.selected_example is not None: | |
| with st.sidebar: | |
| st.dataframe(st.session_state.selected_example) # Display DataFrame in sidebar | |
| st.button("Append to Input Data", on_click=append_selected_to_input_data) | |
| show_sidebar() | |