mirror of
https://github.com/meta-llama/llama-stack.git
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239 lines
8.6 KiB
Python
239 lines
8.6 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import json
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import pandas as pd
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import streamlit as st
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from modules.api import llama_stack_api
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def native_evaluation_page():
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st.set_page_config(page_title="Evaluations (Generation + Scoring)", page_icon="🦙")
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st.title("📊 Evaluations (Generation + Scoring)")
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# Create tabs
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task_tab, candidate_tab, params_tab, run_tab = st.tabs(
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[
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"(1) Select Eval Task",
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"(2) Define Eval Candidate",
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"(3) Define Scoring Parameters",
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"(4) Run Evaluation",
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]
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)
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with task_tab:
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# Select Eval Tasks
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eval_tasks = llama_stack_api.client.eval_tasks.list()
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eval_tasks = {et.identifier: et for et in eval_tasks}
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eval_tasks_names = list(eval_tasks.keys())
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selected_eval_task = st.selectbox(
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"Choose an eval task.",
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options=eval_tasks_names,
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help="Choose an eval task. Each eval task is parameterized by a dataset, and list of scoring functions.",
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)
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st.json(eval_tasks[selected_eval_task], expanded=True)
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with candidate_tab:
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# Define Eval Candidate
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candidate_type = st.radio("Candidate Type", ["model", "agent"])
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available_models = llama_stack_api.client.models.list()
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available_models = [model.identifier for model in available_models]
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selected_model = st.selectbox(
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"Choose a model",
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available_models,
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index=0,
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)
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# Sampling Parameters
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st.markdown("##### Sampling Parameters")
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strategy = st.selectbox(
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"Strategy",
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["greedy", "top_p", "top_k"],
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index=0,
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)
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temperature = st.slider(
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"Temperature",
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min_value=0.0,
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max_value=1.0,
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value=0.0,
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step=0.1,
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help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable",
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)
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top_p = st.slider(
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"Top P",
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min_value=0.0,
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max_value=1.0,
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value=0.95,
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step=0.1,
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)
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max_tokens = st.slider(
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"Max Tokens",
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min_value=0,
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max_value=4096,
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value=512,
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step=1,
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help="The maximum number of tokens to generate",
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)
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repetition_penalty = st.slider(
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"Repetition Penalty",
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min_value=1.0,
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max_value=2.0,
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value=1.0,
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step=0.1,
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help="Controls the likelihood for generating the same word or phrase multiple times in the same sentence or paragraph. 1 implies no penalty, 2 will strongly discourage model to repeat words or phrases.",
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)
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if candidate_type == "model":
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eval_candidate = {
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"type": "model",
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"model": selected_model,
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"sampling_params": {
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"strategy": strategy,
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"temperature": temperature,
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"top_p": top_p,
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"max_tokens": max_tokens,
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"repetition_penalty": repetition_penalty,
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},
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}
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elif candidate_type == "agent":
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system_prompt = st.text_area(
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"System Prompt",
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value="You are a helpful AI assistant.",
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help="Initial instructions given to the AI to set its behavior and context",
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)
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tools_json = st.text_area(
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"Tools Configuration (JSON)",
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value=json.dumps(
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[
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{
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"type": "brave_search",
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"engine": "brave",
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"api_key": "ENTER_BRAVE_API_KEY_HERE",
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}
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]
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),
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help="Enter tool configurations in JSON format. Each tool should have a name, description, and parameters.",
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height=200,
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)
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try:
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tools = json.loads(tools_json)
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except json.JSONDecodeError:
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st.error("Invalid JSON format for tools configuration")
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tools = []
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eval_candidate = {
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"type": "agent",
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"config": {
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"model": selected_model,
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"instructions": system_prompt,
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"tools": tools,
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"tool_choice": "auto",
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"tool_prompt_format": "json",
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"input_shields": [],
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"output_shields": [],
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"enable_session_persistence": False,
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},
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}
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with params_tab:
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st.write("(Optional) Define scoring function parameters here")
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with run_tab:
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# Add info box to explain configurations being used
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st.info(
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"""
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Review the configurations that will be used for this evaluation run, make any necessary changes, and then click the "Run Evaluation" button.
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"""
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)
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dataset_id = eval_tasks[selected_eval_task].dataset_id
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rows = llama_stack_api.client.datasetio.get_rows_paginated(
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dataset_id=dataset_id,
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rows_in_page=-1,
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)
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total_rows = len(rows.rows)
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# Add number of examples control
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num_rows = st.number_input(
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"Number of Examples to Evaluate",
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min_value=1,
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max_value=total_rows,
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value=5,
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help="Number of examples from the dataset to evaluate. ",
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)
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eval_task_config = {
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"type": "benchmark",
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"eval_candidate": eval_candidate,
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"scoring_params": {},
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}
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st.markdown("##### Evaluation Task")
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st.write("Go back to (1) Select Eval Task to make changes to the eval task. ")
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st.json(eval_tasks[selected_eval_task], expanded=True)
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st.markdown("##### Evaluation Task Configuration")
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st.write(
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"Go back to (2) Define Eval Candidate and (3) Define Scoring Parameters to make changes to the configuration. "
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)
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st.json(eval_task_config, expanded=True)
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# Add run button and handle evaluation
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if st.button("Run Evaluation"):
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progress_text = "Running evaluation..."
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progress_bar = st.progress(0, text=progress_text)
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rows = rows.rows
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if num_rows < total_rows:
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rows = rows[:num_rows]
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# Create separate containers for progress text and results
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progress_text_container = st.empty()
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results_container = st.empty()
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output_res = {}
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for i, r in enumerate(rows):
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# Update progress
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progress = i / len(rows)
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progress_bar.progress(progress, text=progress_text)
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# Run evaluation for current row
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eval_res = llama_stack_api.client.eval.evaluate_rows(
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task_id=selected_eval_task,
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input_rows=[r],
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scoring_functions=eval_tasks[selected_eval_task].scoring_functions,
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task_config=eval_task_config,
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)
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for k in r.keys():
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if k not in output_res:
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output_res[k] = []
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output_res[k].append(r[k])
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for k in eval_res.generations[0].keys():
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if k not in output_res:
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output_res[k] = []
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output_res[k].append(eval_res.generations[0][k])
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for scoring_fn in eval_tasks[selected_eval_task].scoring_functions:
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if scoring_fn not in output_res:
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output_res[scoring_fn] = []
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output_res[scoring_fn].append(
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eval_res.scores[scoring_fn].score_rows[0]
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)
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progress_text_container.write(
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f"Expand to see current processed result ({i+1}/{len(rows)})"
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)
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results_container.json(eval_res, expanded=2)
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progress_bar.progress(1.0, text="Evaluation complete!")
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# Display results in dataframe
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if output_res:
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output_df = pd.DataFrame(output_res)
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st.subheader("Evaluation Results")
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st.dataframe(output_df)
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native_evaluation_page()
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