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import gradio as gr
from huggingface_hub import InferenceClient
import concurrent.futures
# Available LLM models
LLM_MODELS = {
"Llama-3.3": "meta-llama/Llama-3.3-70B-Instruct",
"QwQ-32B": "Qwen/QwQ-32B-Preview",
"C4AI-Command": "CohereForAI/c4ai-command-r-plus-08-2024",
"Marco-o1": "AIDC-AI/Marco-o1",
"Qwen2.5": "Qwen/Qwen2.5-72B-Instruct",
"Mistral-Nemo": "mistralai/Mistral-Nemo-Instruct-2407",
"Nemotron-70B": "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
}
# Default selected models
DEFAULT_MODELS = [
"meta-llama/Llama-3.3-70B-Instruct",
"CohereForAI/c4ai-command-r-plus-08-2024",
"mistralai/Mistral-Nemo-Instruct-2407"
]
clients = {model: InferenceClient(model) for model in LLM_MODELS.values()}
def process_file(file):
if file is None:
return ""
if file.name.endswith(('.txt', '.md')):
return file.read().decode('utf-8')
return f"Uploaded file: {file.name}"
def respond_single(
client,
message,
history,
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for user, assistant in history:
if user:
messages.append({"role": "user", "content": user})
if assistant:
messages.append({"role": "assistant", "content": assistant})
messages.append({"role": "user", "content": message})
response = ""
try:
for msg in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = msg.choices[0].delta.content
response += token
yield response
except Exception as e:
yield f"Error: {str(e)}"
def respond_all(
message,
file,
history1,
history2,
history3,
selected_models,
system_message,
max_tokens,
temperature,
top_p,
):
if file:
file_content = process_file(file)
message = f"{message}\n\nFile content:\n{file_content}"
while len(selected_models) < 3:
selected_models.append(selected_models[-1])
def generate(client, history):
return respond_single(
client,
message,
history,
system_message,
max_tokens,
temperature,
top_p,
)
return (
generate(clients[selected_models[0]], history1),
generate(clients[selected_models[1]], history2),
generate(clients[selected_models[2]], history3),
)
with gr.Blocks() as demo:
with gr.Row():
model_choices = gr.Checkboxgroup(
choices=list(LLM_MODELS.values()),
value=DEFAULT_MODELS,
label="Select Models (Choose up to 3)",
interactive=True
)
with gr.Row():
with gr.Column():
chat1 = gr.ChatInterface(
lambda message, history: None,
chatbot=gr.Chatbot(height=400, label="Chat 1"),
textbox=False,
)
with gr.Column():
chat2 = gr.ChatInterface(
lambda message, history: None,
chatbot=gr.Chatbot(height=400, label="Chat 2"),
textbox=False,
)
with gr.Column():
chat3 = gr.ChatInterface(
lambda message, history: None,
chatbot=gr.Chatbot(height=400, label="Chat 3"),
textbox=False,
)
with gr.Row():
with gr.Column():
system_message = gr.Textbox(
value="You are a friendly Chatbot.",
label="System message"
)
max_tokens = gr.Slider(
minimum=1,
maximum=2048,
value=512,
step=1,
label="Max new tokens"
)
temperature = gr.Slider(
minimum=0.1,
maximum=4.0,
value=0.7,
step=0.1,
label="Temperature"
)
top_p = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p"
)
with gr.Row():
file_input = gr.File(label="Upload File (optional)")
msg_input = gr.Textbox(
show_label=False,
placeholder="Enter text and press enter",
container=False
)
def submit_message(message, file):
return respond_all(
message,
file,
chat1.chatbot.value,
chat2.chatbot.value,
chat3.chatbot.value,
model_choices.value,
system_message.value,
max_tokens.value,
temperature.value,
top_p.value,
)
msg_input.submit(
submit_message,
[msg_input, file_input],
[chat1.chatbot, chat2.chatbot, chat3.chatbot],
api_name="submit"
)
if __name__ == "__main__":
demo.launch() |