import gradio as gr
from openai import OpenAI
import os
ACCESS_TOKEN = os.getenv("HF_TOKEN")
print("Access token loaded.")
client = OpenAI(
base_url="https://api-inference.huggingface.co/v1/",
api_key=ACCESS_TOKEN,
)
print("OpenAI client initialized.")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
frequency_penalty,
seed,
custom_model
):
print(f"Received message: {message}")
print(f"History: {history}")
print(f"System message: {system_message}")
print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")
print(f"Selected model (custom_model): {custom_model}")
# Convert seed to None if -1 (meaning random)
if seed == -1:
seed = None
messages = [{"role": "system", "content": system_message}]
print("Initial messages array constructed.")
# Add conversation history to the context
for val in history:
user_part = val[0]
assistant_part = val[1]
if user_part:
messages.append({"role": "user", "content": user_part})
print(f"Added user message to context: {user_part}")
if assistant_part:
messages.append({"role": "assistant", "content": assistant_part})
print(f"Added assistant message to context: {assistant_part}")
# Append the latest user message
messages.append({"role": "user", "content": message})
print("Latest user message appended.")
# If user provided a model, use that; otherwise, fall back to a default model
model_to_use = custom_model.strip() if custom_model.strip() != "" else "meta-llama/Llama-3.3-70B-Instruct"
print(f"Model selected for inference: {model_to_use}")
# Start with an empty string to build the response as tokens stream in
response = ""
print("Sending request to OpenAI API.")
for message_chunk in client.chat.completions.create(
model=model_to_use,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
frequency_penalty=frequency_penalty,
seed=seed,
messages=messages,
):
token_text = message_chunk.choices[0].delta.content
print(f"Received token: {token_text}")
response += token_text
yield response
print("Completed response generation.")
# GRADIO UI
chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", likeable=True, layout="panel")
print("Chatbot interface created.")
system_message_box = gr.Textbox(value="", placeholder="You are a helpful assistant.", label="System Prompt")
max_tokens_slider = gr.Slider(
minimum=1,
maximum=4096,
value=512,
step=1,
label="Max new tokens"
)
temperature_slider = gr.Slider(
minimum=0.1,
maximum=4.0,
value=0.7,
step=0.1,
label="Temperature"
)
top_p_slider = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-P"
)
frequency_penalty_slider = gr.Slider(
minimum=-2.0,
maximum=2.0,
value=0.0,
step=0.1,
label="Frequency Penalty"
)
seed_slider = gr.Slider(
minimum=-1,
maximum=65535,
value=-1,
step=1,
label="Seed (-1 for random)"
)
# The custom_model_box is what the respond function sees as "custom_model"
custom_model_box = gr.Textbox(
value="",
label="Custom Model",
info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
placeholder="meta-llama/Llama-3.3-70B-Instruct"
)
def set_custom_model_from_radio(selected):
"""
This function will get triggered whenever someone picks a model from the 'Featured Models' radio.
We will update the Custom Model text box with that selection automatically.
"""
print(f"Featured model selected: {selected}")
return selected
demo = gr.ChatInterface(
fn=respond,
additional_inputs=[
system_message_box,
max_tokens_slider,
temperature_slider,
top_p_slider,
frequency_penalty_slider,
seed_slider,
custom_model_box,
],
fill_height=True,
chatbot=chatbot,
theme="Nymbo/Nymbo_Theme",
)
print("ChatInterface object created.")
with demo:
with gr.Accordion("Model Selection", open=False):
model_search_box = gr.Textbox(
label="Filter Models",
placeholder="Search for a featured model...",
lines=1
)
print("Model search box created.")
models_list = [
"meta-llama/Llama-3.3-70B-Instruct",
"meta-llama/Llama-3.1-70B-Instruct",
"meta-llama/Llama-3.0-70B-Instruct",
"meta-llama/Llama-3.2-3B-Instruct",
"meta-llama/Llama-3.2-1B-Instruct",
"meta-llama/Llama-3.1-8B-Instruct",
"NousResearch/Hermes-3-Llama-3.1-8B",
"NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
"mistralai/Mistral-Nemo-Instruct-2407",
"mistralai/Mixtral-8x7B-Instruct-v0.1",
"mistralai/Mistral-7B-Instruct-v0.3",
"mistralai/Mistral-7B-Instruct-v0.2",
"Qwen/Qwen2.5-72B-Instruct",
"Qwen/Qwen2.5-3B-Instruct",
"Qwen/Qwen2.5-0.5B-Instruct",
"Qwen/QwQ-32B-Preview",
"Qwen/Qwen2.5-Coder-32B-Instruct",
"microsoft/Phi-3.5-mini-instruct",
"microsoft/Phi-3-mini-128k-instruct",
"microsoft/Phi-3-mini-4k-instruct",
"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"HuggingFaceH4/zephyr-7b-beta",
"HuggingFaceTB/SmolLM2-360M-Instruct",
"tiiuae/falcon-7b-instruct",
"01-ai/Yi-1.5-34B-Chat",
]
print("Models list initialized.")
featured_model_radio = gr.Radio(
label="Select a model below",
choices=models_list,
value="meta-llama/Llama-3.3-70B-Instruct",
interactive=True
)
print("Featured models radio button created.")
def filter_models(search_term):
print(f"Filtering models with search term: {search_term}")
filtered = [m for m in models_list if search_term.lower() in m.lower()]
print(f"Filtered models: {filtered}")
return gr.update(choices=filtered)
model_search_box.change(
fn=filter_models,
inputs=model_search_box,
outputs=featured_model_radio
)
print("Model search box change event linked.")
featured_model_radio.change(
fn=set_custom_model_from_radio,
inputs=featured_model_radio,
outputs=custom_model_box
)
print("Featured model radio button change event linked.")
print("Gradio interface initialized.")
if __name__ == "__main__":
print("Launching the demo application.")
demo.launch()