import json import gradio as gr import pandas as pd with open('results.json', 'r') as file: results = json.load(file) models = [key for key in results.keys()] demo = gr.Blocks() from random import randint, random food_rating_data = pd.DataFrame( { "cuisine": [["Italian", "Mexican", "Chinese"][i % 3] for i in range(100)], "rating": [random() * 4 + 0.5 * (i % 3) for i in range(100)], "price": [randint(10, 50) + 4 * (i % 3) for i in range(100)], "wait": [random() for i in range(100)], } ) df = pd.DataFrame.from_dict(results[models[0]]["main-net"], orient = "index").reset_index() df.columns = ["Step", "Loss"] df["Step"] = pd.to_numeric(df["Step"]) df["Test"] = "Main-net" if "baseline" in results[models[0]]: df_baseline = pd.DataFrame.from_dict(results[models[0]]["baseline"], orient = "index").reset_index() df_baseline.columns = ["Step", "Loss"] df_baseline["Step"] = pd.to_numeric(df_baseline["Step"]) df_baseline["Test"] = "Baseline" df = pd.concat([df, df_baseline]) def return_results(model_name): print(model_name) df = pd.DataFrame.from_dict(results[model_name]["main-net"], orient = "index").reset_index() df.columns = ["Step", "Loss"] df["Step"] = pd.to_numeric(df["Step"]) df["Test"] = "Main-net" if "baseline" in results[model_name]: df_baseline = pd.DataFrame.from_dict(results[model_name]["baseline"], orient = "index").reset_index() df_baseline.columns = ["Step", "Loss"] df_baseline["Step"] = pd.to_numeric(df_baseline["Step"]) df_baseline["Test"] = "Baseline" df = pd.concat([df, df_baseline]) return df with demo: with gr.Row(): title = gr.Markdown(value=f"""#

Subnet 38 Model Convergence

""") with gr.Row(): dropdown_1 = gr.Dropdown(choices = models, value = models[0]) button_1 = gr.Button("Submit") with gr.Row(): chart = gr.LinePlot(df, "Step", "Loss", color="Test", x_lim = (0, max(df['Step']))) button_1.click(return_results, dropdown_1, chart) demo.launch(debug=True, server_name="0.0.0.0", server_port=7860)