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 {model_name} 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 medical assistant.")], additional_inputs_accordion="System prompt", examples=[ ["How to diagnose CHF?"], ["please write an extensive cancer patient story and case study for healthcare providers"], ["Also, please write journey how the cancer was discovered and what symptoms led to patient realization that something might be wrong."], ["Additionally, please write up the intake scenario for her to be seen by a cancer specialist."] ] ).queue().launch(server_name="0.0.0.0")