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import os
import json
import gradio as gr
from llama_cpp import Llama
# Get environment variables
model_id = os.getenv('MODEL')
quant = os.getenv('QUANT')
chat_template = os.getenv('CHAT_TEMPLATE')
# Interface variables
model_name = model_id.split('/')[1].split('-GGUF')[0]
title = f"🗣️ {model_name}"
description = f"Chat with <a href=\"https://huggingface.co/{model_id}\">{model_name}</a> in GGUF format ({quant})!"
# Initialize the LLM
llm = Llama(model_path="model.gguf",
n_ctx=32768,
n_threads=2,
chat_format=chat_template)
# Function for streaming chat completions
def chat_stream_completion(message, history, system_prompt):
messages_prompts = [{"role": "system", "content": system_prompt}]
for human, assistant in history:
messages_prompts.append({"role": "user", "content": human})
messages_prompts.append({"role": "assistant", "content": assistant})
messages_prompts.append({"role": "user", "content": message})
response = llm.create_chat_completion(
messages=messages_prompts,
stream=True
)
message_repl = ""
for chunk in response:
if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
message_repl = message_repl + chunk['choices'][0]["delta"]["content"]
yield message_repl
# Gradio chat interface
gr.ChatInterface(
fn=chat_stream_completion,
title=title,
description=description,
additional_inputs=[gr.Textbox("You are helpful assistant.")],
additional_inputs_accordion="📝 System prompt",
examples=[
["What is a Large Language Model?"],
["What's 9+2-1?"],
["Write Python code to print the Fibonacci sequence"]
]
).queue().launch(server_name="0.0.0.0") |