NexQuant/rdagent/log/ui/app.py

import argparse
import textwrap
from collections import defaultdict
from datetime import datetime, timezone
from importlib.resources import files as rfiles
from pathlib import Path
from typing import Callable, Type

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
from plotly.subplots import make_subplots
from streamlit import session_state as state
from streamlit_theme import st_theme

from rdagent.components.coder.factor_coder.CoSTEER.evaluators import (
    FactorSingleFeedback,
)
from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace, FactorTask
from rdagent.components.coder.model_coder.CoSTEER.evaluators import ModelCoderFeedback
from rdagent.components.coder.model_coder.model import ModelFBWorkspace, ModelTask
from rdagent.core.proposal import Hypothesis, HypothesisFeedback
from rdagent.core.scenario import Scenario
from rdagent.log.base import Message
from rdagent.log.storage import FileStorage
from rdagent.log.ui.qlib_report_figure import report_figure
from rdagent.scenarios.data_mining.experiment.model_experiment import DMModelScenario
from rdagent.scenarios.general_model.scenario import GeneralModelScenario
from rdagent.scenarios.kaggle.experiment.scenario import KGScenario
from rdagent.scenarios.qlib.experiment.factor_experiment import QlibFactorScenario
from rdagent.scenarios.qlib.experiment.factor_from_report_experiment import (
    QlibFactorFromReportScenario,
)
from rdagent.scenarios.qlib.experiment.model_experiment import (
    QlibModelExperiment,
    QlibModelScenario,
)

st.set_page_config(layout="wide", page_title="RD-Agent", page_icon="🎓", initial_sidebar_state="expanded")


# 获取log_path参数
parser = argparse.ArgumentParser(description="RD-Agent Streamlit App")
parser.add_argument("--log_dir", type=str, help="Path to the log directory")
parser.add_argument("--debug", action="store_true", help="Enable debug mode")
args = parser.parse_args()
if args.log_dir:
    main_log_path = Path(args.log_dir)
    if not main_log_path.exists():
        st.error(f"Log dir `{main_log_path}` does not exist!")
        st.stop()
else:
    main_log_path = None


QLIB_SELECTED_METRICS = [
    "IC",
    "1day.excess_return_without_cost.annualized_return",
    "1day.excess_return_without_cost.information_ratio",
    "1day.excess_return_without_cost.max_drawdown",
]

SIMILAR_SCENARIOS = (QlibModelScenario, DMModelScenario, QlibFactorScenario, QlibFactorFromReportScenario, KGScenario)

if "log_path" not in state:
    if main_log_path:
        state.log_path = next(main_log_path.iterdir()).relative_to(main_log_path)
    else:
        state.log_path = None
        st.toast(":red[**Please Set Log Path!**]", icon="⚠️")

if "scenario" not in state:
    state.scenario = None

if "fs" not in state:
    state.fs = None

if "msgs" not in state:
    state.msgs = defaultdict(lambda: defaultdict(list))

if "last_msg" not in state:
    state.last_msg = None

if "current_tags" not in state:
    state.current_tags = []

if "lround" not in state:
    state.lround = 0  # RD Loop Round

if "erounds" not in state:
    state.erounds = defaultdict(int)  # Evolving Rounds in each RD Loop

if "e_decisions" not in state:
    state.e_decisions = defaultdict(lambda: defaultdict(tuple))

# Summary Info
if "hypotheses" not in state:
    # Hypotheses in each RD Loop
    state.hypotheses = defaultdict(None)

if "h_decisions" not in state:
    state.h_decisions = defaultdict(bool)

if "metric_series" not in state:
    state.metric_series = []

# Factor Task Baseline
if "alpha158_metrics" not in state:
    state.alpha158_metrics = None


def should_display(msg: Message):
    for t in state.excluded_tags:
        if t in msg.tag.split("."):
            return False

    if type(msg.content).__name__ in state.excluded_types:
        return False

    return True


def get_msgs_until(end_func: Callable[[Message], bool] = lambda _: True):
    if state.fs:
        while True:
            try:
                msg = next(state.fs)
                if should_display(msg):
                    tags = msg.tag.split(".")
                    if "r" not in state.current_tags and "r" in tags:
                        state.lround += 1
                    if "evolving code" not in state.current_tags and "evolving code" in tags:
                        state.erounds[state.lround] += 1

                    state.current_tags = tags
                    state.last_msg = msg

                    # Update Summary Info
                    if "model runner result" in tags or "factor runner result" in tags or "runner result" in tags:
                        # factor baseline exp metrics
                        if isinstance(state.scenario, QlibFactorScenario) and state.alpha158_metrics is None:
                            sms = msg.content.based_experiments[0].result.loc[QLIB_SELECTED_METRICS]
                            sms.name = "alpha158"
                            state.alpha158_metrics = sms

                        # common metrics
                        if msg.content.result is None:
                            if isinstance(state.scenario, DMModelScenario):
                                state.metric_series.append(
                                    pd.Series([None], index=["AUROC"], name=f"Round {state.lround}")
                                )
                        else:
                            sms = msg.content.result
                            if isinstance(state.scenario, DMModelScenario):
                                sms.index = ["AUROC"]
                            elif isinstance(
                                state.scenario, (QlibModelScenario, QlibFactorFromReportScenario, QlibFactorScenario)
                            ):
                                sms = sms.loc[QLIB_SELECTED_METRICS]

                            sms.name = f"Round {state.lround}"
                            state.metric_series.append(sms)
                    elif "hypothesis generation" in tags:
                        state.hypotheses[state.lround] = msg.content
                    elif "ef" in tags and "feedback" in tags:
                        state.h_decisions[state.lround] = msg.content.decision
                    elif "d" in tags:
                        if "evolving code" in tags:
                            msg.content = [i for i in msg.content if i]
                        if "evolving feedback" in tags:
                            total_len = len(msg.content)
                            msg.content = [i for i in msg.content if i]
                            none_num = total_len - len(msg.content)
                            if len(msg.content) != len(state.msgs[state.lround]["d.evolving code"][-1].content):
                                st.toast(":red[**Evolving Feedback Length Error!**]", icon="‼️")
                            right_num = 0
                            for wsf in msg.content:
                                if wsf.final_decision:
                                    right_num += 1
                            wrong_num = len(msg.content) - right_num
                            state.e_decisions[state.lround][state.erounds[state.lround]] = (
                                right_num,
                                wrong_num,
                                none_num,
                            )

                    state.msgs[state.lround][msg.tag].append(msg)
                    # Stop Getting Logs
                    if end_func(msg):
                        break
            except StopIteration:
                st.toast(":red[**No More Logs to Show!**]", icon="🛑")
                break


def refresh(same_trace: bool = False):
    if state.log_path is None:
        st.toast(":red[**Please Set Log Path!**]", icon="⚠️")
        return

    if main_log_path:
        state.fs = FileStorage(main_log_path / state.log_path).iter_msg()
    else:
        state.fs = FileStorage(state.log_path).iter_msg()

    # detect scenario
    if not same_trace:
        get_msgs_until(lambda m: not isinstance(m.content, str))
        if state.last_msg is None or not isinstance(state.last_msg.content, Scenario):
            st.toast(":red[**No Scenario Info detected**]", icon="❗")
            state.scenario = None
        else:
            state.scenario = state.last_msg.content
            st.toast(f":green[**Scenario Info detected**] *{type(state.scenario).__name__}*", icon="✅")

    state.msgs = defaultdict(lambda: defaultdict(list))
    state.lround = 0
    state.erounds = defaultdict(int)
    state.e_decisions = defaultdict(lambda: defaultdict(tuple))
    state.hypotheses = defaultdict(None)
    state.h_decisions = defaultdict(bool)
    state.metric_series = []
    state.last_msg = None
    state.current_tags = []
    state.alpha158_metrics = None


def evolving_feedback_window(wsf: FactorSingleFeedback | ModelCoderFeedback):
    if isinstance(wsf, FactorSingleFeedback):
        ffc, efc, cfc, vfc = st.tabs(
            ["**Final Feedback🏁**", "Execution Feedback🖥️", "Code Feedback📄", "Value Feedback🔢"]
        )
        with ffc:
            st.markdown(wsf.final_feedback)
        with efc:
            st.code(wsf.execution_feedback, language="log")
        with cfc:
            st.markdown(wsf.code_feedback)
        with vfc:
            st.markdown(wsf.factor_value_feedback)
    elif isinstance(wsf, ModelCoderFeedback):
        ffc, efc, cfc, msfc, vfc = st.tabs(
            [
                "**Final Feedback🏁**",
                "Execution Feedback🖥️",
                "Code Feedback📄",
                "Model Shape Feedback📐",
                "Value Feedback🔢",
            ]
        )
        with ffc:
            st.markdown(wsf.final_feedback)
        with efc:
            st.code(wsf.execution_feedback, language="log")
        with cfc:
            st.markdown(wsf.code_feedback)
        with msfc:
            st.markdown(wsf.shape_feedback)
        with vfc:
            st.markdown(wsf.value_feedback)


def display_hypotheses(hypotheses: dict[int, Hypothesis], decisions: dict[int, bool], success_only: bool = False):
    name_dict = {
        "hypothesis": "RD-Agent proposes the hypothesis⬇️",
        "concise_justification": "because the reason⬇️",
        "concise_observation": "based on the observation⬇️",
        "concise_knowledge": "Knowledge⬇️ gained after practice",
    }
    if success_only:
        shd = {k: v.__dict__ for k, v in hypotheses.items() if decisions[k]}
    else:
        shd = {k: v.__dict__ for k, v in hypotheses.items()}
    df = pd.DataFrame(shd).T

    if "concise_observation" in df.columns and "concise_justification" in df.columns:
        df["concise_observation"], df["concise_justification"] = df["concise_justification"], df["concise_observation"]
        df.rename(
            columns={"concise_observation": "concise_justification", "concise_justification": "concise_observation"},
            inplace=True,
        )
    if "reason" in df.columns:
        df.drop(["reason"], axis=1, inplace=True)
    if "concise_reason" in df.columns:
        df.drop(["concise_reason"], axis=1, inplace=True)

    df.columns = df.columns.map(lambda x: name_dict.get(x, x))

    def style_rows(row):
        if decisions[row.name]:
            return ["color: green;"] * len(row)
        return [""] * len(row)

    def style_columns(col):
        if col.name != name_dict.get("hypothesis", "hypothesis"):
            return ["font-style: italic;"] * len(col)
        return ["font-weight: bold;"] * len(col)

    # st.dataframe(df.style.apply(style_rows, axis=1).apply(style_columns, axis=0))
    st.markdown(df.style.apply(style_rows, axis=1).apply(style_columns, axis=0).to_html(), unsafe_allow_html=True)


def metrics_window(df: pd.DataFrame, R: int, C: int, *, height: int = 300, colors: list[str] = None):
    fig = make_subplots(rows=R, cols=C, subplot_titles=df.columns)

    def hypothesis_hover_text(h: Hypothesis, d: bool = False):
        color = "green" if d else "black"
        text = h.hypothesis
        lines = textwrap.wrap(text, width=60)
        return f"<span style='color: {color};'>{'<br>'.join(lines)}</span>"

    hover_texts = [
        hypothesis_hover_text(state.hypotheses[int(i[6:])], state.h_decisions[int(i[6:])])
        for i in df.index
        if i != "alpha158"
    ]
    if state.alpha158_metrics is not None:
        hover_texts = ["Baseline: alpha158"] + hover_texts
    for ci, col in enumerate(df.columns):
        row = ci // C + 1
        col_num = ci % C + 1
        fig.add_trace(
            go.Scatter(
                x=df.index,
                y=df[col],
                name=col,
                mode="lines+markers",
                connectgaps=True,
                marker=dict(size=10, color=colors[ci]) if colors else dict(size=10),
                hovertext=hover_texts,
                hovertemplate="%{hovertext}<br><br><span style='color: black'>%{x} Value:</span> <span style='color: blue'>%{y}</span><extra></extra>",
            ),
            row=row,
            col=col_num,
        )
    fig.update_layout(showlegend=False, height=height)

    if state.alpha158_metrics is not None:
        for i in range(1, R + 1):  # 行
            for j in range(1, C + 1):  # 列
                fig.update_xaxes(
                    tickvals=[df.index[0]] + list(df.index[1:]),
                    ticktext=[f'<span style="color:blue; font-weight:bold">{df.index[0]}</span>'] + list(df.index[1:]),
                    row=i,
                    col=j,
                )
    st.plotly_chart(fig)


def summary_window():
    if isinstance(state.scenario, SIMILAR_SCENARIOS):
        st.header("Summary📊", divider="rainbow", anchor="_summary")
        if state.lround == 0:
            return
        with st.container():
            # TODO: not fixed height
            with st.container():
                bc, cc = st.columns([2, 2], vertical_alignment="center")
                with bc:
                    st.subheader("Metrics📈", anchor="_metrics")
                with cc:
                    show_true_only = st.toggle("successful hypotheses", value=False)

            # hypotheses_c, chart_c = st.columns([2, 3])
            chart_c = st.container()
            hypotheses_c = st.container()

            with hypotheses_c:
                st.subheader("Hypotheses🏅", anchor="_hypotheses")
                display_hypotheses(state.hypotheses, state.h_decisions, show_true_only)

            with chart_c:
                if isinstance(state.scenario, QlibFactorScenario) and state.alpha158_metrics is not None:
                    df = pd.DataFrame([state.alpha158_metrics] + state.metric_series)
                else:
                    df = pd.DataFrame(state.metric_series)
                if show_true_only and len(state.hypotheses) >= len(state.metric_series):
                    if state.alpha158_metrics is not None:
                        selected = ["alpha158"] + [i for i in df.index if state.h_decisions[int(i[6:])]]
                    else:
                        selected = [i for i in df.index if state.h_decisions[int(i[6:])]]
                    df = df.loc[selected]
                if df.shape[0] == 1:
                    st.table(df.iloc[0])
                elif df.shape[0] > 1:
                    if df.shape[1] == 1:
                        fig = px.line(df, x=df.index, y=df.columns, markers=True)
                        fig.update_layout(xaxis_title="Loop Round", yaxis_title=None)
                        st.plotly_chart(fig)
                    else:
                        metrics_window(df, 1, 4, height=300, colors=["red", "blue", "orange", "green"])

    elif isinstance(state.scenario, GeneralModelScenario):
        with st.container(border=True):
            st.subheader("Summary📊", divider="rainbow", anchor="_summary")
            if len(state.msgs[state.lround]["d.evolving code"]) > 0:
                # pass
                ws: list[FactorFBWorkspace | ModelFBWorkspace] = state.msgs[state.lround]["d.evolving code"][-1].content
                # All Tasks

                tab_names = [
                    w.target_task.factor_name if isinstance(w.target_task, FactorTask) else w.target_task.name
                    for w in ws
                ]
                for j in range(len(ws)):
                    if state.msgs[state.lround]["d.evolving feedback"][-1].content[j].final_decision:
                        tab_names[j] += "✔️"
                    else:
                        tab_names[j] += "❌"

                wtabs = st.tabs(tab_names)
                for j, w in enumerate(ws):
                    with wtabs[j]:
                        # Evolving Code
                        for k, v in w.code_dict.items():
                            with st.expander(f":green[`{k}`]", expanded=False):
                                st.code(v, language="python")

                        # Evolving Feedback
                        evolving_feedback_window(state.msgs[state.lround]["d.evolving feedback"][-1].content[j])


def tabs_hint():
    st.markdown(
        "<p style='font-size: small; color: #888888;'>You can navigate through the tabs using ⬅️ ➡️ or by holding Shift and scrolling with the mouse wheel🖱️.</p>",
        unsafe_allow_html=True,
    )


def tasks_window(tasks: list[FactorTask | ModelTask]):
    if isinstance(tasks[0], FactorTask):
        st.markdown("**Factor Tasks🚩**")
        tnames = [f.factor_name for f in tasks]
        if sum(len(tn) for tn in tnames) > 100:
            tabs_hint()
        tabs = st.tabs(tnames)
        for i, ft in enumerate(tasks):
            with tabs[i]:
                # st.markdown(f"**Factor Name**: {ft.factor_name}")
                st.markdown(f"**Description**: {ft.factor_description}")
                st.latex("Formulation")
                st.latex(ft.factor_formulation)

                mks = "| Variable | Description |\n| --- | --- |\n"
                if isinstance(ft.variables, dict):
                    for v, d in ft.variables.items():
                        mks += f"| ${v}$ | {d} |\n"
                    st.markdown(mks)

    elif isinstance(tasks[0], ModelTask):
        st.markdown("**Model Tasks🚩**")
        tnames = [m.name for m in tasks]
        if sum(len(tn) for tn in tnames) > 100:
            tabs_hint()
        tabs = st.tabs(tnames)
        for i, mt in enumerate(tasks):
            with tabs[i]:
                # st.markdown(f"**Model Name**: {mt.name}")
                st.markdown(f"**Model Type**: {mt.model_type}")
                st.markdown(f"**Description**: {mt.description}")
                st.latex("Formulation")
                st.latex(mt.formulation)

                mks = "| Variable | Description |\n| --- | --- |\n"
                if mt.variables:
                    for v, d in mt.variables.items():
                        mks += f"| ${v}$ | {d} |\n"
                    st.markdown(mks)


def research_window():
    with st.container(border=True):
        title = "Research🔍" if isinstance(state.scenario, SIMILAR_SCENARIOS) else "Research🔍 (reader)"
        st.subheader(title, divider="blue", anchor="_research")
        if isinstance(state.scenario, SIMILAR_SCENARIOS):
            # pdf image
            if pim := state.msgs[round]["r.extract_factors_and_implement.load_pdf_screenshot"]:
                for i in range(min(2, len(pim))):
                    st.image(pim[i].content, use_column_width=True)

            # Hypothesis
            if hg := state.msgs[round]["r.hypothesis generation"]:
                st.markdown("**Hypothesis💡**")  # 🧠
                h: Hypothesis = hg[0].content
                st.markdown(
                    f"""
- **Hypothesis**: {h.hypothesis}
- **Reason**: {h.reason}"""
                )

            if eg := state.msgs[round]["r.experiment generation"]:
                tasks_window(eg[0].content)

        elif isinstance(state.scenario, GeneralModelScenario):
            # pdf image
            c1, c2 = st.columns([2, 3])
            with c1:
                if pim := state.msgs[round]["r.pdf_image"]:
                    for i in range(len(pim)):
                        st.image(pim[i].content, use_column_width=True)

            # loaded model exp
            with c2:
                if mem := state.msgs[round]["d.load_experiment"]:
                    me: QlibModelExperiment = mem[0].content
                    tasks_window(me.sub_tasks)


def feedback_window():
    if isinstance(state.scenario, SIMILAR_SCENARIOS):
        with st.container(border=True):
            st.subheader("Feedback📝", divider="orange", anchor="_feedback")

            if state.lround > 0 and isinstance(
                state.scenario, (QlibModelScenario, QlibFactorScenario, QlibFactorFromReportScenario, KGScenario)
            ):
                with st.expander("**Config⚙️**", expanded=True):
                    st.markdown(state.scenario.experiment_setting, unsafe_allow_html=True)

            if fbr := state.msgs[round]["ef.Quantitative Backtesting Chart"]:
                st.markdown("**Returns📈**")
                fig = report_figure(fbr[0].content)
                st.plotly_chart(fig)
            if fb := state.msgs[round]["ef.feedback"]:
                st.markdown("**Hypothesis Feedback🔍**")
                h: HypothesisFeedback = fb[0].content
                st.markdown(
                    f"""
- **Observations**: {h.observations}
- **Hypothesis Evaluation**: {h.hypothesis_evaluation}
- **New Hypothesis**: {h.new_hypothesis}
- **Decision**: {h.decision}
- **Reason**: {h.reason}"""
                )

            if isinstance(state.scenario, KGScenario):
                if fbe := state.msgs[round]["ef.runner result"]:
                    submission_path = fbe[0].content.experiment_workspace.workspace_path / "submission.csv"
                    st.markdown(
                        f":green[**Exp Workspace**]: {str(fbe[0].content.experiment_workspace.workspace_path.absolute())}"
                    )
                    st.download_button(
                        label="**Download** submission.csv",
                        data=submission_path.read_bytes(),
                        file_name="submission.csv",
                        mime="text/csv",
                    )


def evolving_window():
    title = "Development🛠️" if isinstance(state.scenario, SIMILAR_SCENARIOS) else "Development🛠️ (evolving coder)"
    st.subheader(title, divider="green", anchor="_development")

    # Evolving Status
    if state.erounds[round] > 0:
        st.markdown("**☑️ Evolving Status**")
        es = state.e_decisions[round]
        e_status_mks = "".join(f"| {ei} " for ei in range(1, state.erounds[round] + 1)) + "|\n"
        e_status_mks += "|--" * state.erounds[round] + "|\n"
        for ei, estatus in es.items():
            if not estatus:
                estatus = (0, 0, 0)
            e_status_mks += "| " + "🕙<br>" * estatus[2] + "✔️<br>" * estatus[0] + "❌<br>" * estatus[1] + " "
        e_status_mks += "|\n"
        st.markdown(e_status_mks, unsafe_allow_html=True)

    # Evolving Tabs
    if state.erounds[round] > 0:
        if state.erounds[round] > 1:
            evolving_round = st.radio(
                "**🔄️Evolving Rounds**",
                horizontal=True,
                options=range(1, state.erounds[round] + 1),
                index=state.erounds[round] - 1,
                key="show_eround",
            )
        else:
            evolving_round = 1

        ws: list[FactorFBWorkspace | ModelFBWorkspace] = state.msgs[round]["d.evolving code"][
            evolving_round - 1
        ].content
        # All Tasks

        tab_names = [
            w.target_task.factor_name if isinstance(w.target_task, FactorTask) else w.target_task.name for w in ws
        ]
        if len(state.msgs[round]["d.evolving feedback"]) >= evolving_round:
            for j in range(len(ws)):
                if state.msgs[round]["d.evolving feedback"][evolving_round - 1].content[j].final_decision:
                    tab_names[j] += "✔️"
                else:
                    tab_names[j] += "❌"
        if sum(len(tn) for tn in tab_names) > 100:
            tabs_hint()
        wtabs = st.tabs(tab_names)
        for j, w in enumerate(ws):
            with wtabs[j]:
                # Evolving Code
                for k, v in w.code_dict.items():
                    with st.expander(f":green[`{k}`]", expanded=True):
                        st.code(v, language="python")

                # Evolving Feedback
                if len(state.msgs[round]["d.evolving feedback"]) >= evolving_round:
                    evolving_feedback_window(state.msgs[round]["d.evolving feedback"][evolving_round - 1].content[j])


toc = """
## [Scenario Description📖](#_scenario)
## [Summary📊](#_summary)
- [**Metrics📈**](#_metrics)
- [**Hypotheses🏅**](#_hypotheses)
## [RD-Loops♾️](#_rdloops)
- [**Research🔍**](#_research)
- [**Development🛠️**](#_development)
- [**Feedback📝**](#_feedback)
"""
if isinstance(state.scenario, GeneralModelScenario):
    toc = """
## [Scenario Description📖](#_scenario)
### [Summary📊](#_summary)
### [Research🔍](#_research)
### [Development🛠️](#_development)
"""
# Config Sidebar
with st.sidebar:
    st.markdown("# RD-Agent🤖  [:grey[@GitHub]](https://github.com/microsoft/RD-Agent)")
    st.subheader(":blue[Table of Content]", divider="blue")
    st.markdown(toc)
    st.subheader(":orange[Control Panel]", divider="red")

    with st.container(border=True):
        if main_log_path:
            lc1, lc2 = st.columns([1, 2], vertical_alignment="center")
            with lc1:
                st.markdown(":blue[**Log Path**]")
            with lc2:
                manually = st.toggle("Manual Input")
            if manually:
                st.text_input("log path", key="log_path", on_change=refresh, label_visibility="collapsed")
            else:
                folders = [folder.relative_to(main_log_path) for folder in main_log_path.iterdir() if folder.is_dir()]
                st.selectbox(f"**Select from `{main_log_path}`**", folders, key="log_path", on_change=refresh)
        else:
            st.text_input(":blue[**log path**]", key="log_path", on_change=refresh)

    c1, c2 = st.columns([1, 1], vertical_alignment="center")
    with c1:
        if st.button(":green[**All Loops**]", use_container_width=True):
            if not state.fs:
                refresh()
            get_msgs_until(lambda m: False)
        if st.button("**Reset**", use_container_width=True):
            refresh(same_trace=True)
    with c2:
        if st.button(":green[Next Loop]", use_container_width=True):
            if not state.fs:
                refresh()
            get_msgs_until(lambda m: "ef.feedback" in m.tag)

        if st.button("Next Step", use_container_width=True):
            if not state.fs:
                refresh()
            get_msgs_until(lambda m: "d.evolving feedback" in m.tag)

    with st.popover(":orange[**Config⚙️**]", use_container_width=True):
        st.multiselect("excluded log tags", ["llm_messages"], ["llm_messages"], key="excluded_tags")
        st.multiselect("excluded log types", ["str", "dict", "list"], ["str"], key="excluded_types")

    if args.debug:
        debug = st.toggle("debug", value=False)

        if debug:
            if st.button("Single Step Run", use_container_width=True):
                get_msgs_until()
    else:
        debug = False


# Debug Info Window
if debug:
    with st.expander(":red[**Debug Info**]", expanded=True):
        dcol1, dcol2 = st.columns([1, 3])
        with dcol1:
            st.markdown(
                f"**log path**: {state.log_path}\n\n"
                f"**excluded tags**: {state.excluded_tags}\n\n"
                f"**excluded types**: {state.excluded_types}\n\n"
                f":blue[**message id**]: {sum(sum(len(tmsgs) for tmsgs in rmsgs.values()) for rmsgs in state.msgs.values())}\n\n"
                f":blue[**round**]: {state.lround}\n\n"
                f":blue[**evolving round**]: {state.erounds[state.lround]}\n\n"
            )
        with dcol2:
            if state.last_msg:
                st.write(state.last_msg)
                if isinstance(state.last_msg.content, list):
                    st.write(state.last_msg.content[0])
                elif not isinstance(state.last_msg.content, str):
                    st.write(state.last_msg.content.__dict__)


if state.log_path and state.fs is None:
    refresh()

# Main Window
header_c1, header_c3 = st.columns([1, 6], vertical_alignment="center")
with st.container():
    with header_c1:
        st.image("https://img-prod-cms-rt-microsoft-com.akamaized.net/cms/api/am/imageFileData/RE1Mu3b?ver=5c31")
    with header_c3:
        st.markdown(
            """
        <h1>
            RD-Agent:<br>LLM-based autonomous evolving agents for industrial data-driven R&D
        </h1>
        """,
            unsafe_allow_html=True,
        )

# Project Info
with st.container():
    image_c, scen_c = st.columns([3, 3], vertical_alignment="center")
    with image_c:
        img_path = rfiles("rdagent.log.ui").joinpath("flow.png")
        st.image(str(img_path), use_column_width=True)
    with scen_c:
        st.header("Scenario Description📖", divider="violet", anchor="_scenario")
        if state.scenario is not None:
            theme = st_theme()
            if theme:
                theme = theme.get("base", "light")
            css = f"""
<style>
    a[href="#_rdloops"], a[href="#_research"], a[href="#_development"], a[href="#_feedback"], a[href="#_scenario"], a[href="#_summary"], a[href="#_hypotheses"], a[href="#_metrics"] {{
        color: {"black" if theme == "light" else "white"};
    }}
</style>
"""
            st.markdown(state.scenario.rich_style_description + css, unsafe_allow_html=True)


if state.scenario is not None:
    summary_window()

    # R&D Loops Window
    if isinstance(state.scenario, SIMILAR_SCENARIOS):
        st.header("R&D Loops♾️", divider="rainbow", anchor="_rdloops")
        if len(state.msgs) > 1:
            r_options = list(state.msgs.keys())
            if 0 in r_options:
                r_options.remove(0)
            round = st.radio("**Loops**", horizontal=True, options=r_options, index=state.lround - 1)
        else:
            round = 1
        rf_c, d_c = st.columns([2, 2])
    elif isinstance(state.scenario, GeneralModelScenario):
        rf_c = st.container()
        d_c = st.container()
        round = 1
    else:
        st.error("Unknown Scenario!")
        st.stop()

    with rf_c:
        research_window()
        feedback_window()

    with d_c.container(border=True):
        evolving_window()


st.markdown("<br><br><br>", unsafe_allow_html=True)
st.markdown("#### Disclaimer")
st.markdown(
    "*This content is AI-generated and may not be fully accurate or up-to-date; please verify with a professional for critical matters.*",
    unsafe_allow_html=True,
)