✨ Add validation plot and score for webui

aide/webui/app.py

@@ -158,24 +158,35 @@ def handle_file_upload(self):
         Returns:
             list: List of uploaded or example files.
         """
-        if st.button(
-            "Load Example Experiment", type="primary", use_container_width=True
-        ):
-            st.session_state.example_files = self.load_example_files()
+        # Only show file uploader if no example files are loaded
+        if not st.session_state.get("example_files"):
+            uploaded_files = st.file_uploader(
+                "Upload Data Files",
+                accept_multiple_files=True,
+                type=["csv", "txt", "json", "md"],
+                label_visibility="collapsed",
+            )
+
+            if uploaded_files:
+                st.session_state.pop(
+                    "example_files", None
+                )  # Remove example files if any
+                return uploaded_files
+
+            # Only show example button if no files are uploaded
+            if st.button(
+                "Load Example Experiment", type="primary", use_container_width=True
+            ):
+                st.session_state.example_files = self.load_example_files()
 
         if st.session_state.get("example_files"):
             st.info("Example files loaded! Click 'Run AIDE' to proceed.")
             with st.expander("View Loaded Files", expanded=False):
                 for file in st.session_state.example_files:
                     st.text(f"📄 {file['name']}")
-            uploaded_files = st.session_state.example_files
-        else:
-            uploaded_files = st.file_uploader(
-                "Upload Data Files",
-                accept_multiple_files=True,
-                type=["csv", "txt", "json", "md"],
-            )
-        return uploaded_files
+            return st.session_state.example_files
+
+        return []  # Return empty list if no files are uploaded or loaded
 
     def handle_user_inputs(self):
         """
@@ -187,12 +198,12 @@ def handle_user_inputs(self):
         goal_text = st.text_area(
             "Goal",
             value=st.session_state.get("goal", ""),
-            placeholder="Example: Predict house prices",
+            placeholder="Example: Predict the sales price for each house",
         )
         eval_text = st.text_area(
             "Evaluation Criteria",
             value=st.session_state.get("eval", ""),
-            placeholder="Example: Use RMSE metric",
+            placeholder="Example: Use the RMSE metric between the logarithm of the predicted and observed values.",
         )
         num_steps = st.slider(
             "Number of Steps",
@@ -450,7 +461,16 @@ def render_results_section(self):
         st.header("Results")
         if st.session_state.get("results"):
             results = st.session_state.results
-            tabs = st.tabs(["Tree Visualization", "Best Solution", "Config", "Journal"])
+
+            tabs = st.tabs(
+                [
+                    "Tree Visualization",
+                    "Best Solution",
+                    "Config",
+                    "Journal",
+                    "Validation Plot",
+                ]
+            )
 
             with tabs[0]:
                 self.render_tree_visualization(results)
@@ -460,6 +480,12 @@ def render_results_section(self):
                 self.render_config(results)
             with tabs[3]:
                 self.render_journal(results)
+            with tabs[4]:
+                # Display best score before the plot
+                best_metric = self.get_best_metric(results)
+                if best_metric is not None:
+                    st.metric("Best Validation Score", f"{best_metric:.4f}")
+                self.render_validation_plot(results)
         else:
             st.info("No results to display. Please run an experiment.")
 
@@ -529,6 +555,77 @@ def render_journal(results):
         else:
             st.info("No journal available.")
 
+    @staticmethod
+    def get_best_metric(results):
+        """
+        Extract the best validation metric from results.
+        """
+        try:
+            journal_data = json.loads(results["journal"])
+            metrics = []
+            for node in journal_data:
+                if node["metric"] is not None:
+                    try:
+                        # Convert string metric to float
+                        metric_value = float(node["metric"])
+                        metrics.append(metric_value)
+                    except (ValueError, TypeError):
+                        continue
+            return max(metrics) if metrics else None
+        except (json.JSONDecodeError, KeyError):
+            return None
+
+    @staticmethod
+    def render_validation_plot(results):
+        """
+        Render the validation score plot.
+        """
+        try:
+            journal_data = json.loads(results["journal"])
+            steps = []
+            metrics = []
+
+            for node in journal_data:
+                if node["metric"] is not None and node["metric"].lower() != "none":
+                    try:
+                        metric_value = float(node["metric"])
+                        steps.append(node["step"])
+                        metrics.append(metric_value)
+                    except (ValueError, TypeError):
+                        continue
+
+            if metrics:
+                import plotly.graph_objects as go
+
+                fig = go.Figure()
+                fig.add_trace(
+                    go.Scatter(
+                        x=steps,
+                        y=metrics,
+                        mode="lines+markers",
+                        name="Validation Score",
+                        line=dict(color="#F04370"),
+                        marker=dict(color="#F04370"),
+                    )
+                )
+
+                fig.update_layout(
+                    title="Validation Score Progress",
+                    xaxis_title="Step",
+                    yaxis_title="Validation Score",
+                    template="plotly_white",
+                    hovermode="x unified",
+                    plot_bgcolor="rgba(0,0,0,0)",
+                    paper_bgcolor="rgba(0,0,0,0)",
+                )
+
+                st.plotly_chart(fig, use_container_width=True)
+            else:
+                st.info("No validation metrics available to plot.")
+
+        except (json.JSONDecodeError, KeyError):
+            st.error("Could not parse validation metrics data.")
+
 
 if __name__ == "__main__":
     app = WebUI()

aide/webui/style.css

@@ -1,7 +1,7 @@
 /* Main colors */
 :root {
     --background: #F2F0E7;
-    --background-shaded: #EBE8DD;
+    --background-shaded: #FFFFFF;
     --card: #FFFFFF;
     --primary: #0D0F18;
     --accent: #F04370;