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import gradio as gr
from llama_cpp import Llama
# Load the Mistral model
llm = Llama.from_pretrained(
repo_id="bartowski/Mistral-Small-Instruct-2409-GGUF",
filename="Mistral-Small-Instruct-2409-IQ2_M.gguf",
)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
# Add history to messages
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Add the current user message
messages.append({"role": "user", "content": message})
# Generate the response using the Mistral model
response = llm.create_chat_completion(messages=messages)
return response["choices"][0]["message"]["content"] # Adjust based on your model's output format
# Set up Gradio Chat 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)",
),
],
)
if __name__ == "__main__":
demo.launch()
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