demo changes

rdagent/log/ui/app.py

@@ -195,21 +195,28 @@ def evolving_feedback_window(wsf: FactorSingleFeedback | ModelCoderFeedback):
             st.markdown(wsf.value_feedback)
 
 
-def display_hypotheses(hypotheses: dict[int, Hypothesis], decisions: dict[int, bool]):
-    shd = {k: v.__dict__ for k, v in hypotheses.items()}
+def display_hypotheses(hypotheses: dict[int, Hypothesis], decisions: dict[int, bool], success_only: bool = False):
+    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 "reason" in df.columns:
+        df.drop(["reason"], axis=1, inplace=True)
+    df.columns = df.columns.map(lambda x: x.replace("_", " ").capitalize())
 
-    def highlight_rows(row):
+    def style_rows(row):
         if decisions[row.name]:
-            return ['color: green; font-weight: bold;'] * len(row)
-
-    def background_color_columns(col):
-        if col.name == 'hypothesis':
-            return ['background-color: lightgrey'] * len(col)
+            return ['color: green;'] * len(row)
+        return [''] * len(row)
 
-    st.dataframe(df.style.apply(highlight_rows, axis=1).apply(background_color_columns, axis=0))
+    def style_columns(col):
+        if col.name != 'Hypothesis':
+            return ['font-style: italic;'] * len(col)
+        return ['font-weight: bold;'] * len(col)
 
-    st.markdown(df.style.apply(highlight_rows, axis=1).apply(background_color_columns, axis=0).to_html(), unsafe_allow_html=True)
+    # 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):
@@ -219,7 +226,9 @@ def hypothesis_hover_text(h: Hypothesis, d: bool = False):
         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]
+    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
@@ -256,52 +265,41 @@ def summary_window():
         with st.container():
             # TODO: not fixed height
             with st.container():
-                ac,bc,cc = st.columns([2,1,2], vertical_alignment="center")
-                with ac:
-                    st.subheader("Hypotheses🏅", anchor="_hypotheses")
+                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])
-            with hypotheses_c:
-                with st.container(height=700):
-                    h_strs = []
-                    for id, h in state.hypotheses.items():
-                        if state.h_decisions[id]:
-                            h_strs.append(f"{id}. :green[**{h.hypothesis}**]\n\t>:green-background[*{h.__dict__.get('concise_reason', '')}*]")
-                        else:
-                            h_strs.append(f"{id}. {h.hypothesis}\n\t>*{h.__dict__.get('concise_reason', '')}*")
+            # hypotheses_c, chart_c = st.columns([2, 3])
+            chart_c = st.container()
+            hypotheses_c = st.container()
 
-                        if hasattr(h, "concise_observation"):
-                            h_strs[-1] += f"\n\n\t>:blue[**Observation**]: {h.concise_observation}"
-                            h_strs[-1] += f"\n\n\t>:blue[**Justification**]: {h.concise_justification}"
-                            h_strs[-1] += f"\n\n\t>:blue[**Knowledge**]: {h.concise_knowledge}"
-                    st.markdown("\n".join(h_strs))
+            with hypotheses_c:
+                st.subheader("Hypotheses🏅", anchor="_hypotheses")
+                display_hypotheses(state.hypotheses, state.h_decisions, show_true_only)
 
             with chart_c:
-                with st.container(height=700):
-                    if state.log_type == "qlib_factor":
-                        df = pd.DataFrame([state.alpha158_metrics] + state.metric_series)
+                if state.log_type == "qlib_factor":
+                    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:
-                        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:
-                            # suhan's scenario
-                            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, 2, 2, height=650, colors=['red', 'blue', 'orange', 'green'])
+                        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:
+                        # suhan's scenario
+                        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 state.log_type == "model_extraction_and_implementation" and len(state.msgs[state.lround]["d.evolving code"]) > 0:
@@ -443,10 +441,23 @@ def tasks_window(tasks: list[FactorTask | ModelTask]):
                 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)
+                    st.write(state.last_msg.content.__dict__)
 
 
 # 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():