model config display improv

app/main.py

@@ -5,7 +5,7 @@ from utils.classification import apply_classification
 from utils.validation import generate_classification_model
 from utils.api import get_openai_client
 from utils.tokens import estimate_token_count
-from utils.vis import display_metrics_as_table
+from utils.vis import display_metrics_as_table, display_model_config
 from config.model_params import DEFAULT_PARAMS
 
 st.set_page_config(layout="wide")
@@ -158,8 +158,7 @@ if uploaded_file:
         "max_tokens": DEFAULT_PARAMS["max_tokens"],
     }
 
-    st.sidebar.write('**Model Config**')
-    st.sidebar.json(DEFAULT_PARAMS)
+    display_model_config(DEFAULT_PARAMS)
 
     verbose = st.checkbox("Verbose", value=False)
 

app/utils/vis.py

@@ -1,6 +1,39 @@
 import pandas as pd
 import streamlit as st
 
+def display_model_config(config, title="Model Config"):
+    """
+    Displays the model configuration and available models in a structured table format.
+
+    Args:
+        config (dict): Configuration dictionary containing model, max_tokens, temperature, and available_models.
+        title (str): Title to display in the sidebar.
+    """
+    st.sidebar.subheader(title)
+
+    # Extract main model parameters into a DataFrame
+    model_config_df = pd.DataFrame.from_dict(
+        {
+            "Parameter": ["Model", "Max Tokens", "Temperature"],
+            "Value": [
+                config["model"],
+                config["max_tokens"],
+                config["temperature"],
+            ],
+        }
+    )
+
+    # Display the main parameters as a table
+    st.sidebar.table(model_config_df)
+
+    # Extract and display available models
+    st.sidebar.markdown("### Available Models")
+    available_models_df = pd.DataFrame(
+        {"Available Models": config["available_models"]}
+    )
+    st.sidebar.table(available_models_df)
+
+
 def display_metrics_as_table(metrics):
     """
     Convert evaluation metrics into a readable table and display it.