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import gradio as gr
import pandas as pd
from transformers import pipeline
from load_models import models_and_tokenizers, models_checkpoints
import spaces
choice = {"ModelA": "", "ModelB": ""}
dff = pd.read_csv("models.csv")
dff.to_html("tab.html")
def refreshfn() -> gr.HTML:
df = pd.read_csv("models.csv")
df.to_html("tab.html")
f = open("tab.html")
content = f.read()
f.close()
t = gr.HTML(content)
return t
def rewrite_csv_ordered_by_winning_rate(csv_path):
# Read the input CSV
df = pd.read_csv(csv_path)
# Sort the DataFrame by WINNING_RATE in descending order
df_sorted = df.sort_values(by="WINNING_RATE", ascending=False)
# Save the sorted DataFrame to a new CSV file
df_sorted.to_csv(csv_path, index=False)
@spaces.GPU(duration=200)
def run_inference(pipeline, prompt):
response = pipeline(prompt)
bot_message = response[0]["generated_text"]
return bot_message
def modelA_button():
global choice
df = pd.read_csv("models.csv")
df.loc[df["MODEL"] == choice["ModelA"], "MATCHES_WON"] += 1
df.loc[df["MODEL"] == choice["ModelA"], "WINNING_RATE"] = df.loc[df["MODEL"] == choice["ModelA"], "MATCHES_WON"]/df.loc[df["MODEL"] == choice["ModelA"], "MATCHES_PLAYED"]
df.to_csv("models.csv")
rewrite_csv_ordered_by_winning_rate("models.csv")
def modelB_button():
global choice
df = pd.read_csv("models.csv")
df.loc[df["MODEL"] == choice["ModelB"], "MATCHES_WON"] += 1
df.loc[df["MODEL"] == choice["ModelB"], "WINNING_RATE"] = df.loc[df["MODEL"] == choice["ModelB"], "MATCHES_WON"]/df.loc[df["MODEL"] == choice["ModelB"], "MATCHES_PLAYED"]
df.to_csv("models.csv")
rewrite_csv_ordered_by_winning_rate("models.csv")
def reply(modelA, modelB, prompt):
global choice
choice["ModelA"] = modelA
choice["ModelB"] = modelB
df = pd.read_csv("models.csv")
df.loc[df["MODEL"] == modelA, "MATCHES_PLAYED"] += 1
df.loc[df["MODEL"] == modelB, "MATCHES_PLAYED"] += 1
df.to_csv("models.csv", index=False)
pipeA = pipeline("text-generation", model=models_and_tokenizers[modelA][0], tokenizer=models_and_tokenizers[modelA][1], max_new_tokens=512, repetition_penalty=1.5, temperature=0.5, device="cuda")
pipeB = pipeline("text-generation", model=models_and_tokenizers[modelB][0], tokenizer=models_and_tokenizers[modelB][1], max_new_tokens=512, repetition_penalty=1.5, temperature=0.5, device="cuda")
responseA = run_inference(pipeA, prompt)
responseB = run_inference(pipeB, prompt)
return responseA, responseB
modelA_dropdown = gr.Dropdown(models_checkpoints, label="Model A", info="Choose the first model for the battle!")
modelB_dropdown = gr.Dropdown(models_checkpoints, label="Model B", info="Choose the second model for the battle!")
prompt_textbox = gr.Textbox(label="Prompt", value="Is pineapple pizza sacrilegious?")
demo0 = gr.Interface(fn=reply, inputs=[modelA_dropdown, modelB_dropdown, prompt_textbox], outputs=[gr.Markdown(label="Model A response"), gr.Markdown(label="Model B response")])
with gr.Blocks() as demo1:
iface = demo0
btnA = gr.Button("Vote for Model A!")
btnB = gr.Button("Vote for Model B!")
btnA.click(modelA_button, inputs=None, outputs=None)
btnB.click(modelB_button, inputs=None, outputs=None)
with gr.Blocks() as demo2:
f = open("tab.html")
content = f.read()
f.close()
t = gr.HTML(content)
btn = gr.Button("Refresh")
btn.click(fn=refreshfn, inputs=None, outputs=t)
demo = gr.TabbedInterface([demo1, demo2], ["Chat Arena", "Leaderboard"])
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |