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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"""# <p style="text-align: center;"> Subnet 38 Model Convergence</p>""")
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)
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