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Running
on
Zero
Running
on
Zero
File size: 2,507 Bytes
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import os
import gradio as gr
from huggingface_hub import login, InferenceClient
# Authenticate with Hugging Face API
api_key = os.getenv("TOKEN")
login(api_key)
# Initialize clients for different models
llama_client = InferenceClient("meta-llama/Llama-3.1-70B-Instruct")
gpt_client = InferenceClient("openai/gpt-4")
# Define the response function
def respond(
message,
history: list[dict],
system_message,
max_tokens,
temperature,
top_p,
selected_models,
):
messages = [{"role": "system", "content": system_message}] + history
messages.append({"role": "user", "content": message})
responses = {}
if "Llama" in selected_models:
llama_response = ""
for token in llama_client.chat_completion(
messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p
):
delta = token.choices[0].delta.content
llama_response += delta
responses["Llama"] = llama_response
if "GPT" in selected_models:
gpt_response = ""
for token in gpt_client.chat_completion(
messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p
):
delta = token.choices[0].delta.content
gpt_response += delta
responses["GPT"] = gpt_response
return responses
# Build Gradio app
def create_demo():
with gr.Blocks() as demo:
gr.Markdown("# AI Model Comparison Tool 🌟")
gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(
value="You are a helpful assistant providing answers for technical and customer support queries.",
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.CheckboxGroup(
["Llama", "GPT"],
label="Select models to compare",
value=["Llama"]
),
],
)
return demo
if __name__ == "__main__":
demo = create_demo()
demo.launch()
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