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import gradio as gr
from huggingface_hub import InferenceClient
from llama_cpp import Llama
# Initialize the InferenceClient
client = InferenceClient()
llm = Llama.from_pretrained(
repo_id="bartowski/Reasoning-Llama-1b-v0.1-GGUF",
filename="Reasoning-Llama-1b-v0.1-f16.gguf",
)
# Fixed system message
FIXED_SYSTEM_MESSAGE = "You are an artifial inteligence created by the ACC(Algorithmic Computer-generated Consciousness). Act like a toddler."
def respond(
message,
history: list[tuple[str, str]],
user_system_message, # User-configurable system message
max_tokens,
temperature,
top_p,
):
# Combine the fixed and user-provided system messages
combined_system_message = f"{FIXED_SYSTEM_MESSAGE} {user_system_message}"
# Construct the messages list
messages = [{"role": "system", "content": combined_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 = ""
# Use the client to get the chat completion
for message in 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
# Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="", label="System Message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Maximum response length"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Creativity"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Neural Activity",
),
],
)
if __name__ == "__main__":
demo.launch()
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