NEARai / app.py
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import gradio as gr
from huggingface_hub import InferenceClient
import openai
# Initialize Hugging Face client
hf_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# OpenAI GPT-4 API key
openai.api_key = "sk-proj-vA5kIjVnIcoxmcsgTEfBT3BlbkFJ2aD0n6zIUcYzpcu48QHK"
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
model_choice,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
if model_choice == "Hugging Face Model":
for message in hf_client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
elif model_choice == "OpenAI GPT-4":
response_openai = openai.ChatCompletion.create(
model="gpt-4",
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True
)
for message in response_openai:
response += message['choices'][0]['delta'].get('content', '')
yield response
# Create the Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
gr.Radio(
choices=["Hugging Face Model", "OpenAI GPT-4"],
value="Hugging Face Model",
label="Choose Model"
)
],
title="GPT-4 vs Hugging Face Model Comparison",
description="Compare responses between a Hugging Face model and OpenAI's GPT-4."
)
if __name__ == "__main__":
demo.launch()