import hashlib import json import pickle import random import re from collections import defaultdict from datetime import timedelta from pathlib import Path import pandas as pd import streamlit as st from streamlit import session_state as state from rdagent.app.data_science.loop import DataScienceRDLoop from rdagent.log.storage import FileStorage from rdagent.log.ui.utils import load_times from rdagent.log.utils import ( LogColors, extract_evoid, extract_json, extract_loopid_func_name, is_valid_session, ) from rdagent.utils.repo.diff import generate_diff_from_dict if "show_stdout" not in state: state.show_stdout = False if "show_llm_log" not in state: state.show_llm_log = False if "data" not in state: state.data = defaultdict(lambda: defaultdict(dict)) if "llm_data" not in state: state.llm_data = defaultdict(lambda: defaultdict(lambda: defaultdict(list))) if "log_path" not in state: state.log_path = None if "log_folder" not in state: state.log_folder = Path("./log") def convert_defaultdict_to_dict(d): if isinstance(d, defaultdict): d = {k: convert_defaultdict_to_dict(v) for k, v in d.items()} return d @st.cache_data(persist=True) def load_data(log_path: Path): data = defaultdict(lambda: defaultdict(dict)) llm_data = defaultdict(lambda: defaultdict(lambda: defaultdict(list))) for msg in FileStorage(log_path).iter_msg(): if not msg.tag: continue li, fn = extract_loopid_func_name(msg.tag) ei = extract_evoid(msg.tag) if li: li = int(li) if ei is not None: ei = int(ei) if "debug_" in msg.tag: if "debug_tpl" in msg.tag and "filter_" in msg.content["uri"]: continue if ei is not None: llm_data[li][fn][ei].append( { "tag": msg.tag, "obj": msg.content, } ) else: llm_data[li][fn]["no_tag"].append( { "tag": msg.tag, "obj": msg.content, } ) elif "llm" not in msg.tag and "session" not in msg.tag and "batch embedding" not in msg.tag: if msg.tag == "competition": data["competition"] = msg.content continue msg.tag = re.sub(r"\.evo_loop_\d+", "", msg.tag) msg.tag = re.sub(r"Loop_\d+\.[^.]+\.?", "", msg.tag) msg.tag = msg.tag.strip() if ei is not None: if ei not in data[li][fn]: data[li][fn][ei] = {} data[li][fn][ei][msg.tag] = msg.content else: if msg.tag: data[li][fn][msg.tag] = msg.content else: if not isinstance(msg.content, str): data[li][fn]["no_tag"] = msg.content # To be compatible with old version log trace, keep this llm_log_p = log_path / "debug_llm.pkl" if llm_log_p.exists(): try: rd = pickle.loads(llm_log_p.read_bytes()) except: rd = [] for d in rd: t = d["tag"] if "debug_exp_gen" in t: continue if "debug_tpl" in t and "filter_" in d["obj"]["uri"]: continue lid, fn = extract_loopid_func_name(t) ei = extract_evoid(t) if lid: lid = int(lid) if ei is not None: ei = int(ei) if ei is not None: llm_data[lid][fn][ei].append(d) else: llm_data[lid][fn]["no_tag"].append(d) return convert_defaultdict_to_dict(data), convert_defaultdict_to_dict(llm_data) def load_stdout(stdout_path: Path): if stdout_path.exists(): stdout = stdout_path.read_text() else: stdout = f"Please Set: {stdout_path}" return stdout # UI windows def task_win(data): with st.container(border=True): st.markdown(f"**:violet[{data.name}]**") st.markdown(data.description) if hasattr(data, "architecture"): # model task st.markdown( f""" | Model_type | Architecture | hyperparameters | |------------|--------------|-----------------| | {data.model_type} | {data.architecture} | {data.hyperparameters} | """ ) def workspace_win(workspace, cmp_workspace=None, cmp_name="last code."): show_files = {k: v for k, v in workspace.file_dict.items() if "test" not in k} if len(show_files) > 0: if cmp_workspace: diff = generate_diff_from_dict(cmp_workspace.file_dict, show_files, "main.py") with st.expander(f":violet[**Diff with {cmp_name}**]"): st.code("".join(diff), language="diff", wrap_lines=True, line_numbers=True) with st.expander(f"Files in :blue[{replace_ep_path(workspace.workspace_path)}]"): code_tabs = st.tabs(show_files.keys()) for ct, codename in zip(code_tabs, show_files.keys()): with ct: st.code( show_files[codename], language=("python" if codename.endswith(".py") else "markdown"), wrap_lines=True, line_numbers=True, ) if state.show_save_input: st.markdown("### Save All Files to Folder") unique_key = hashlib.md5("".join(show_files.values()).encode()).hexdigest() + str( random.randint(0, 10000) ) target_folder = st.text_input("Enter target folder path:", key=unique_key) if st.button("Save Files", key=f"save_files_button_{unique_key}"): if target_folder.strip() == "": st.warning("Please enter a valid folder path.") else: target_folder_path = Path(target_folder) target_folder_path.mkdir(parents=True, exist_ok=True) for filename, content in workspace.file_dict.items(): save_path = target_folder_path / filename save_path.parent.mkdir(parents=True, exist_ok=True) save_path.write_text(content, encoding="utf-8") st.success(f"All files saved to: {target_folder}") else: st.markdown(f"No files in :blue[{replace_ep_path(workspace.workspace_path)}]") # Helper functions def show_text(text, lang=None): """显示文本代码块""" if lang: st.code(text, language=lang, wrap_lines=True) elif "\n" in text: st.code(text, language="python", wrap_lines=True) else: st.code(text, language="html", wrap_lines=True) def highlight_prompts_uri(uri): """高亮 URI 的格式""" parts = uri.split(":") if len(parts) > 1: return f"**{parts[0]}:**:green[**{parts[1]}**]" return f"**{uri}**" def llm_log_win(llm_d: list): for d in llm_d: if "debug_tpl" in d["tag"]: uri = d["obj"]["uri"] tpl = d["obj"]["template"] cxt = d["obj"]["context"] rd = d["obj"]["rendered"] with st.expander(highlight_prompts_uri(uri), expanded=False, icon="⚙️"): t1, t2, t3 = st.tabs([":green[**Rendered**]", ":blue[**Template**]", ":orange[**Context**]"]) with t1: show_text(rd) with t2: show_text(tpl, lang="django") with t3: st.json(cxt) elif "debug_llm" in d["tag"]: system = d["obj"].get("system", None) user = d["obj"]["user"] resp = d["obj"]["resp"] with st.expander(f"**LLM**", expanded=False, icon="🤖"): t1, t2, t3 = st.tabs([":green[**Response**]", ":blue[**User**]", ":orange[**System**]"]) with t1: try: rdict = json.loads(resp) if "code" in rdict: code = rdict["code"] st.markdown(":red[**Code in response dict:**]") st.code(code, language="python", wrap_lines=True, line_numbers=True) rdict.pop("code") elif "spec" in rdict: spec = rdict["spec"] st.markdown(":red[**Spec in response dict:**]") st.markdown(spec) rdict.pop("spec") else: showed_keys = [] for k, v in rdict.items(): if k.endswith(".py"): st.markdown(f":red[**{k}**]") st.code(v, language="python", wrap_lines=True, line_numbers=True) showed_keys.append(k) for k in showed_keys: rdict.pop(k) st.write(":red[**Other parts (except for the code or spec) in response dict:**]") st.json(rdict) except: try: st.json(resp) except: show_text(resp) with t2: show_text(user) with t3: show_text(system or "No system prompt available") def hypothesis_win(hypo): try: st.code(str(hypo).replace("\n", "\n\n"), wrap_lines=True) except Exception as e: st.write(hypo.__dict__) def exp_gen_win(exp_gen_data, llm_data=None): st.header("Exp Gen", divider="blue", anchor="exp-gen") if state.show_llm_log and llm_data is not None: llm_log_win(llm_data["no_tag"]) st.subheader("Hypothesis") hypothesis_win(exp_gen_data["no_tag"].hypothesis) st.subheader("pending_tasks") for tasks in exp_gen_data["no_tag"].pending_tasks_list: task_win(tasks[0]) st.subheader("Exp Workspace") workspace_win(exp_gen_data["no_tag"].experiment_workspace) def evolving_win(data, key, llm_data=None): with st.container(border=True): if len(data) > 1: evo_id = st.slider("Evolving", 0, len(data) - 1, 0, key=key) elif len(data) == 1: evo_id = 0 else: st.markdown("No evolving.") return if evo_id in data: if state.show_llm_log and llm_data is not None: llm_log_win(llm_data[evo_id]) if data[evo_id]["evolving code"][0] is not None: st.subheader("codes") workspace_win( data[evo_id]["evolving code"][0], cmp_workspace=data[evo_id - 1]["evolving code"][0] if evo_id > 0 else None, cmp_name="last evolving code", ) fb = data[evo_id]["evolving feedback"][0] st.subheader("evolving feedback" + ("✅" if bool(fb) else "❌")) f1, f2, f3 = st.tabs(["execution", "return_checking", "code"]) f1.code(fb.execution, wrap_lines=True) f2.code(fb.return_checking, wrap_lines=True) f3.code(fb.code, wrap_lines=True) else: st.write("data[evo_id]['evolving code'][0] is None.") st.write(data[evo_id]) else: st.markdown("No evolving.") def coding_win(data, llm_data: dict | None = None): st.header("Coding", divider="blue", anchor="coding") if llm_data is not None: common_llm_data = llm_data.pop("no_tag", []) evolving_data = {k: v for k, v in data.items() if isinstance(k, int)} task_set = set() for v in evolving_data.values(): for t in v: if "Task" in t.split(".")[0]: task_set.add(t.split(".")[0]) if task_set: # 新版存Task tag的Trace for task in task_set: st.subheader(task) task_data = {k: {a.split(".")[1]: b for a, b in v.items() if task in a} for k, v in evolving_data.items()} evolving_win(task_data, key=task, llm_data=llm_data if llm_data else None) else: # 旧版未存Task tag的Trace evolving_win(evolving_data, key="coding", llm_data=llm_data if llm_data else None) if state.show_llm_log: llm_log_win(common_llm_data) if "no_tag" in data: st.subheader("Exp Workspace (coding final)") workspace_win(data["no_tag"].experiment_workspace) def running_win(data, mle_score, llm_data=None, sota_exp=None): st.header("Running", divider="blue", anchor="running") if llm_data is not None: common_llm_data = llm_data.pop("no_tag", []) evolving_win( {k: v for k, v in data.items() if isinstance(k, int)}, key="running", llm_data=llm_data if llm_data else None ) if state.show_llm_log and llm_data is not None: llm_log_win(common_llm_data) if "no_tag" in data: st.subheader("Exp Workspace (running final)") workspace_win( data["no_tag"].experiment_workspace, cmp_workspace=sota_exp.experiment_workspace if sota_exp else None, cmp_name="last SOTA", ) st.subheader("Result") st.write(data["no_tag"].result) st.subheader("MLE Submission Score" + ("✅" if (isinstance(mle_score, dict) and mle_score["score"]) else "❌")) if isinstance(mle_score, dict): st.json(mle_score) else: st.code(mle_score, wrap_lines=True) def feedback_win(fb_data, llm_data=None): fb_data = fb_data["no_tag"] st.header("Feedback" + ("✅" if bool(fb_data) else "❌"), divider="orange", anchor="feedback") if state.show_llm_log and llm_data is not None: llm_log_win(llm_data["no_tag"]) try: st.code(str(fb_data).replace("\n", "\n\n"), wrap_lines=True) except Exception as e: st.write(fb_data.__dict__) if fb_data.exception is not None: st.markdown(f"**:red[Exception]**: {fb_data.exception}") def sota_win(sota_exp, trace): st.header("SOTA Experiment", divider="rainbow", anchor="sota-exp") if hasattr(trace, "sota_exp_to_submit") and trace.sota_exp_to_submit is not None: st.markdown(":orange[trace.**sota_exp_to_submit**]") sota_exp = trace.sota_exp_to_submit else: st.markdown(":orange[trace.**sota_experiment()**]") if sota_exp: st.markdown(f"**SOTA Exp Hypothesis**") hypothesis_win(sota_exp.hypothesis) st.markdown("**Exp Workspace**") workspace_win(sota_exp.experiment_workspace) else: st.markdown("No SOTA experiment.") def main_win(loop_id, llm_data=None): loop_data = state.data[loop_id] exp_gen_win(loop_data["direct_exp_gen"], llm_data["direct_exp_gen"] if llm_data else None) if "coding" in loop_data: coding_win(loop_data["coding"], llm_data["coding"] if llm_data else None) if "running" in loop_data: running_win( loop_data["running"], loop_data.get("mle_score", "no submission to score"), llm_data=llm_data["running"] if llm_data else None, sota_exp=( state.data[loop_id - 1].get("record", {}).get("SOTA experiment", None) if (loop_id - 1) in state.data else None ), ) if "feedback" in loop_data: feedback_win(loop_data["feedback"], llm_data.get("feedback", None) if llm_data else None) if "record" in loop_data and "SOTA experiment" in loop_data["record"]: sota_win(loop_data["record"]["SOTA experiment"], loop_data["record"]["trace"]) def replace_ep_path(p: Path): # 替换workspace path为对应ep机器mount在ep03的path # TODO: FIXME: 使用配置项来处理 match = re.search(r"ep\d+", str(state.log_folder)) if match: ep = match.group(0) return Path( str(p).replace("repos/RD-Agent-Exp", f"repos/batch_ctrl/all_projects/{ep}").replace("/Data", "/data") ) return p def summarize_data(): st.header("Summary", divider="rainbow") with st.container(border=True): df = pd.DataFrame( columns=[ "Component", "Hypothesis", "Reason", "Others", "Running Score (valid)", "Running Score (test)", "Feedback", "e-loops(coding)", "Time", "Exp Gen", "Coding", "Running", "Start Time (UTC+8)", "End Time (UTC+8)", ], index=range(len(state.data) - 1), ) min_id, max_id = get_state_data_range(state.data) for loop in range(min_id, max_id + 1): loop_data = state.data[loop] df.loc[loop, "Component"] = loop_data["direct_exp_gen"]["no_tag"].hypothesis.component df.loc[loop, "Hypothesis"] = loop_data["direct_exp_gen"]["no_tag"].hypothesis.hypothesis df.loc[loop, "Reason"] = loop_data["direct_exp_gen"]["no_tag"].hypothesis.reason df.at[loop, "Others"] = { k: v for k, v in loop_data["direct_exp_gen"]["no_tag"].hypothesis.__dict__.items() if k not in ["component", "hypothesis", "reason"] } if loop in state.times and state.times[loop]: df.loc[loop, "Time"] = str(sum((i.end - i.start for i in state.times[loop]), timedelta())).split(".")[0] exp_gen_time = state.times[loop][0].end - state.times[loop][0].start df.loc[loop, "Exp Gen"] = str(exp_gen_time).split(".")[0] if len(state.times[loop]) > 1: coding_time = state.times[loop][1].end - state.times[loop][1].start df.loc[loop, "Coding"] = str(coding_time).split(".")[0] if len(state.times[loop]) > 2: running_time = state.times[loop][2].end - state.times[loop][2].start df.loc[loop, "Running"] = str(running_time).split(".")[0] df.loc[loop, "Start Time (UTC+8)"] = state.times[loop][0].start + timedelta(hours=8) df.loc[loop, "End Time (UTC+8)"] = state.times[loop][-1].end + timedelta(hours=8) if "running" in loop_data and "no_tag" in loop_data["running"]: try: df.loc[loop, "Running Score (valid)"] = str( round(loop_data["running"]["no_tag"].result.loc["ensemble"].iloc[0], 5) ) except: df.loc[loop, "Running Score (valid)"] = "❌" if "mle_score" not in state.data[loop]: if "mle_score" in loop_data["running"]: mle_score_txt = loop_data["running"]["mle_score"] state.data[loop]["mle_score"] = extract_json(mle_score_txt) if ( state.data[loop]["mle_score"] is not None and state.data[loop]["mle_score"]["score"] is not None ): df.loc[loop, "Running Score (test)"] = str(state.data[loop]["mle_score"]["score"]) else: state.data[loop]["mle_score"] = mle_score_txt df.loc[loop, "Running Score (test)"] = "❌" else: mle_score_path = ( replace_ep_path(loop_data["running"]["no_tag"].experiment_workspace.workspace_path) / "mle_score.txt" ) try: mle_score_txt = mle_score_path.read_text() state.data[loop]["mle_score"] = extract_json(mle_score_txt) if state.data[loop]["mle_score"]["score"] is not None: df.loc[loop, "Running Score (test)"] = str(state.data[loop]["mle_score"]["score"]) else: state.data[loop]["mle_score"] = mle_score_txt df.loc[loop, "Running Score (test)"] = "❌" except Exception as e: state.data[loop]["mle_score"] = str(e) df.loc[loop, "Running Score (test)"] = "❌" else: if isinstance(state.data[loop]["mle_score"], dict): df.loc[loop, "Running Score (test)"] = str(state.data[loop]["mle_score"]["score"]) else: df.loc[loop, "Running Score (test)"] = "❌" else: df.loc[loop, "Running Score (valid)"] = "N/A" df.loc[loop, "Running Score (test)"] = "N/A" if "coding" in loop_data: if len([i for i in loop_data["coding"].keys() if isinstance(i, int)]) == 0: df.loc[loop, "e-loops(coding)"] = 0 else: df.loc[loop, "e-loops(coding)"] = ( max(i for i in loop_data["coding"].keys() if isinstance(i, int)) + 1 ) if "feedback" in loop_data: df.loc[loop, "Feedback"] = "✅" if bool(loop_data["feedback"]["no_tag"]) else "❌" else: df.loc[loop, "Feedback"] = "N/A" st.dataframe(df[df.columns[~df.columns.isin(["Hypothesis", "Reason", "Others"])]]) st.markdown("### Hypotheses Table") st.dataframe( df.iloc[:, :8], row_height=100, column_config={ "Others": st.column_config.JsonColumn(width="medium"), "Reason": st.column_config.TextColumn(width="medium"), "Hypothesis": st.column_config.TextColumn(width="large"), }, ) def comp_stat_func(x: pd.DataFrame): total_num = x.shape[0] valid_num = x[x["Running Score (test)"] != "N/A"].shape[0] success_num = x[x["Feedback"] == "✅"].shape[0] avg_e_loops = x["e-loops(coding)"].mean() return pd.Series( { "Loop Num": total_num, "Valid Loop": valid_num, "Success Loop": success_num, "Valid Rate": round(valid_num / total_num * 100, 2), "Success Rate": round(success_num / total_num * 100, 2), "Avg e-loops(coding)": round(avg_e_loops, 2), } ) st1, st2 = st.columns([1, 1]) # component statistics comp_df = ( df.loc[:, ["Component", "Running Score (test)", "Feedback", "e-loops(coding)"]] .groupby("Component") .apply(comp_stat_func) ) comp_df.loc["Total"] = comp_df.sum() comp_df.loc["Total", "Valid Rate"] = round( comp_df.loc["Total", "Valid Loop"] / comp_df.loc["Total", "Loop Num"] * 100, 2 ) comp_df.loc["Total", "Success Rate"] = round( comp_df.loc["Total", "Success Loop"] / comp_df.loc["Total", "Loop Num"] * 100, 2 ) comp_df["Valid Rate"] = comp_df["Valid Rate"].apply(lambda x: f"{x}%") comp_df["Success Rate"] = comp_df["Success Rate"].apply(lambda x: f"{x}%") comp_df.loc["Total", "Avg e-loops(coding)"] = round(df["e-loops(coding)"].mean(), 2) st2.markdown("### Component Statistics") st2.dataframe(comp_df) # component time statistics time_df = df.loc[:, ["Component", "Time", "Exp Gen", "Coding", "Running"]] time_df = time_df.astype( { "Time": "timedelta64[ns]", "Exp Gen": "timedelta64[ns]", "Coding": "timedelta64[ns]", "Running": "timedelta64[ns]", } ) st1.markdown("### Time Statistics") time_stat_df = time_df.groupby("Component").sum() time_stat_df.loc["Total"] = time_stat_df.sum() time_stat_df.loc[:, "Exp Gen(%)"] = time_stat_df["Exp Gen"] / time_stat_df["Time"] * 100 time_stat_df.loc[:, "Coding(%)"] = time_stat_df["Coding"] / time_stat_df["Time"] * 100 time_stat_df.loc[:, "Running(%)"] = time_stat_df["Running"] / time_stat_df["Time"] * 100 time_stat_df = time_stat_df.map(lambda x: str(x).split(".")[0] if pd.notnull(x) else "0:00:00") st1.dataframe(time_stat_df) def stdout_win(loop_id: int): stdout = load_stdout(state.log_folder / f"{state.log_path}.stdout") if stdout.startswith("Please Set"): st.toast(stdout, icon="🟡") return start_index = stdout.find(f"Start Loop {loop_id}") end_index = stdout.find(f"Start Loop {loop_id + 1}") loop_stdout = LogColors.remove_ansi_codes(stdout[start_index:end_index]) with st.container(border=True): st.subheader(f"Loop {loop_id} stdout") pattern = f"Start Loop {loop_id}, " + r"Step \d+: \w+" matches = re.finditer(pattern, loop_stdout) step_stdouts = {} for match in matches: step = match.group(0) si = match.start() ei = loop_stdout.find(f"Start Loop {loop_id}", match.end()) step_stdouts[step] = loop_stdout[si:ei].strip() for k, v in step_stdouts.items(): with st.expander(k, expanded=False): st.code(v, language="log", wrap_lines=True) def get_folders_sorted(log_path, sort_by_time=False): """ Cache and return the sorted list of folders, with progress printing. :param log_path: Log path :param sort_by_time: Whether to sort by time, default False (sort by name) """ if not log_path.exists(): st.toast(f"Path {log_path} does not exist!") return [] with st.spinner("Loading folder list..."): folders = [folder for folder in log_path.iterdir() if is_valid_session(folder)] if sort_by_time: folders = sorted(folders, key=lambda folder: folder.stat().st_mtime, reverse=True) else: folders = sorted(folders, key=lambda folder: folder.name) st.write(f"Found {len(folders)} folders") return [folder.name for folder in folders] # UI - Sidebar with st.sidebar: # TODO: 只是临时的功能 if any("log.srv" in folder for folder in state.log_folders): day_map = {"srv": "最近(srv)", "srv2": "上一批(srv2)", "srv3": "上上批(srv3)"} day_srv = st.radio("选择批次", ["srv", "srv2", "srv3"], format_func=lambda x: day_map[x], horizontal=True) if day_srv == "srv": state.log_folders = [re.sub(r"log\.srv\d*", "log.srv", folder) for folder in state.log_folders] elif day_srv == "srv2": state.log_folders = [re.sub(r"log\.srv\d*", "log.srv2", folder) for folder in state.log_folders] elif day_srv == "srv3": state.log_folders = [re.sub(r"log\.srv\d*", "log.srv3", folder) for folder in state.log_folders] if "log_folder" in st.query_params: state.log_folder = Path(st.query_params["log_folder"]) state.log_folders = [str(state.log_folder)] else: state.log_folder = Path( st.radio( f"Select :blue[**one log folder**]", state.log_folders, format_func=lambda x: x[x.rfind("amlt") + 5 :].split("/")[0] if "amlt" in x else x, ) ) if not state.log_folder.exists(): st.warning(f"Path {state.log_folder} does not exist!") else: folders = get_folders_sorted(state.log_folder, sort_by_time=False) if "selection" in st.query_params: default_index = ( folders.index(st.query_params["selection"]) if st.query_params["selection"] in folders else 0 ) else: default_index = 0 state.log_path = st.selectbox( f"Select from :blue[**{state.log_folder.absolute()}**]", folders, index=default_index ) if st.button("Refresh Data"): if state.log_path is None: st.toast("Please select a log path first!", icon="🟡") st.stop() state.times = load_times(state.log_folder / state.log_path) state.data, state.llm_data = load_data(state.log_folder / state.log_path) st.rerun() st.toggle("**Show LLM Log**", key="show_llm_log") st.toggle("*Show stdout*", key="show_stdout") st.toggle("*Show save workspace feature*", key="show_save_input") st.markdown( f""" - [Exp Gen](#exp-gen) - [Coding](#coding) - [Running](#running) - [Feedback](#feedback) - [SOTA Experiment](#sota-exp) """ ) def get_state_data_range(state_data): # we have a "competition" key in state_data # like dict_keys(['competition', 10, 11, 12, 13, 14]) keys = [k for k in state_data.keys() if isinstance(k, int)] return min(keys), max(keys) # UI - Main if "competition" in state.data: st.title(state.data["competition"]) st.markdown(f"[share_link](/ds_trace?log_folder={state.log_folder}&selection={state.log_path})") summarize_data() min_id, max_id = get_state_data_range(state.data) if max_id > min_id: loop_id = st.slider("Loop", min_id, max_id, min_id) else: loop_id = min_id if state.show_stdout: stdout_win(loop_id) main_win(loop_id, state.llm_data[loop_id] if loop_id in state.llm_data else None)