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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.
Args:
metrics (dict): Evaluation metrics.
"""
# Create a DataFrame for class-specific metrics
class_metrics = [
{"Class": label, "Precision": data["precision"], "Recall": data["recall"], "F1-Score": data["f1-score"], "Support": data["support"]}
for label, data in metrics.items()
if label not in ["accuracy", "macro avg", "weighted avg"]
]
class_df = pd.DataFrame(class_metrics)
# Create a DataFrame for overall metrics
overall_metrics = [
{"Metric": "Accuracy", "Value": metrics["accuracy"]},
{"Metric": "Macro Avg Precision", "Value": metrics["macro avg"]["precision"]},
{"Metric": "Macro Avg Recall", "Value": metrics["macro avg"]["recall"]},
{"Metric": "Macro Avg F1-Score", "Value": metrics["macro avg"]["f1-score"]},
{"Metric": "Weighted Avg Precision", "Value": metrics["weighted avg"]["precision"]},
{"Metric": "Weighted Avg Recall", "Value": metrics["weighted avg"]["recall"]},
{"Metric": "Weighted Avg F1-Score", "Value": metrics["weighted avg"]["f1-score"]},
]
overall_df = pd.DataFrame(overall_metrics)
# Display the tables
st.subheader("Class-Specific Metrics")
st.dataframe(class_df)
st.subheader("Overall Metrics")
st.dataframe(overall_df)
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