feat(app): UI improvement by adding tables for nodes & properties

app.py

@@ -2,6 +2,7 @@ import copy
 import json
 import os
 import zipfile
+import pandas as pd
 
 import gradio as gr
 import spaces
@@ -140,7 +141,7 @@ def gen_output_from_files_uploaded(filepaths: list[str] = None):
         sql = ""
 
     # Create Summary Table
-    nodes_df, properties_df = {}, {}
+    nodes_df, properties_df = pd.DataFrame(), pd.DataFrame()
     try:
         nodes_df, properties_df = create_summary_tables(model_response_json)
     except Exception as exc:
@@ -153,16 +154,18 @@ def gen_output_from_example_simple():
     model_response = get_example_ai_model_output_simple()
     model_response_json = json.loads(model_response)
     sql, validation = dd_to_sql(model_response_json)
+    nodes_df, properties_df = create_summary_tables(model_response_json)
 
-    return model_response, sql
+    return model_response, sql, nodes_df, properties_df
 
 
 def gen_output_from_example_many():
     model_response = get_example_ai_model_output_many()
     model_response_json = json.loads(model_response)
     sql, validation = dd_to_sql(model_response_json)
+    nodes_df, properties_df = create_summary_tables(model_response_json)
 
-    return model_response, sql
+    return model_response, sql, nodes_df, properties_df
 
 
 def zip_tsvs():
@@ -209,44 +212,47 @@ with gr.Blocks() as demo:
     )
 
     # Define Outputs
+    with gr.Row(equal_height=True):
+        json_out = gr.Code(
+            label="Generated Data Model Output",
+            value=json.dumps([]),
+            language="json",
+            interactive=True,
+            show_label=True,
+            container=True,
+            elem_id="json-output",
+        )
+        sql_out = gr.Textbox(
+            label="SQL Defined Relational Schema",
+            value=[],
+            show_label=True,
+            container=True,
+        )
+
+    with gr.Row():
+        nodes_df_out = gr.Dataframe(label="Generated Node/Table Descriptions")
     with gr.Row():
-        with gr.Column(scale=7):
-            json_out = gr.Code(
-                label="Generated Data Model Output",
-                value=json.dumps([]),
-                language="json",
-                interactive=True,
-                show_label=True,
-                container=True,
-                elem_id="json-output",
-            )
-        with gr.Column(scale=7):
-            sql_out = gr.Textbox(
-                label="SQL Defined Relational Schema",
-                value=[],
-                show_label=True,
-                container=True,
-            )
+        properties_df_out = gr.Dataframe(label="Generated Property Descriptions")
 
     # If files are uploaded, generate prompt and run model
     if model_loaded:
         files.upload(
             fn=gen_output_from_files_uploaded,
             inputs=files,
-            outputs=[json_out, sql_out],
+            outputs=[json_out, sql_out, nodes_df_out, properties_df_out],
         )
 
     gr.Markdown("Run out of FreeGPU or having issues? Try the example outputs!")
     demo_btn2 = gr.Button("Manually Load 'Simple' Example Output from Previous Run")
     demo_btn2.click(
         fn=gen_output_from_example_simple,
-        outputs=[json_out, sql_out],
+        outputs=[json_out, sql_out, nodes_df_out, properties_df_out],
     )
 
     demo_btn = gr.Button("Manually Load 'Many' Example Output from Previous Run")
     demo_btn.click(
         fn=gen_output_from_example_many,
-        outputs=[json_out, sql_out],
+        outputs=[json_out, sql_out, nodes_df_out, properties_df_out],
     )
 
 if __name__ == "__main__":

utils.py

@@ -2,6 +2,7 @@ import csv
 import os
 from io import BytesIO
 from string import Template
+import pandas as pd
 
 import matplotlib.pyplot as plt
 import networkx as nx
@@ -97,16 +98,34 @@ def create_summary_tables(json_response):
     node_descriptions = {}
     node_property_descriptions = {}
 
-    for node in json_response["nodes"]:
-        node_descriptions[node["name"]] = node["description"]
+    for node in json_response.get("nodes", []):
+        node_descriptions[node.get("name", "N/A")] = node.get("description", "N/A")
 
-        properties_dict = {}
-        for prop in node["properties"]:
-            properties_dict[prop["name"]] = prop["description"]
+        for prop in node.get("properties", []):
+            node_property_descriptions[
+                f"{node.get('name', 'N/A')}.{prop.get('name', 'N/A')}"
+            ] = [prop.get("type", "N/A"), prop.get("description", "N/A")]
 
-        node_property_descriptions[node["name"]] = properties_dict
+    node_descriptions_df = pd.DataFrame.from_dict(
+        node_descriptions, orient="index"
+    ).reset_index()
+    node_descriptions_df.rename(
+        columns={"index": "Node Name", 0: "Generated Description"}, inplace=True
+    )
+
+    node_property_descriptions_df = pd.DataFrame.from_dict(
+        node_property_descriptions, orient="index"
+    ).reset_index()
+    node_property_descriptions_df.rename(
+        columns={
+            "index": "Node.Property Name",
+            0: "Generated Data Type",
+            1: "Generated Description",
+        },
+        inplace=True,
+    )
 
-    return node_descriptions, node_property_descriptions
+    return node_descriptions_df, node_property_descriptions_df
 
 
 def get_example_ai_model_output_simple():