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import gradio as gr
from gpt4all import GPT4All
from huggingface_hub import hf_hub_download
import subprocess
import asyncio
import os
import stat
title = "Apollo-7B-GGUF Run On CPU"
description = """
🔎 [Apollo-7B](https://huggingface.co/FreedomIntelligence/Apollo-7B) [GGUF format model](https://huggingface.co/FreedomIntelligence/Apollo-7B-GGUF) , 8-bit quantization balanced quality gguf version, running on CPU. Using [GitHub - llama.cpp](https://github.com/ggerganov/llama.cpp) [GitHub - gpt4all](https://github.com/nomic-ai/gpt4all).
🔨 Running on CPU-Basic free hardware. Suggest duplicating this space to run without a queue.
Mistral does not support system prompt symbol (such as ```<<SYS>>```) now, input your system prompt in the first message if you need. Learn more: [Guardrailing Mistral 7B](https://docs.mistral.ai/usage/guardrailing).
"""
"""
[Model From TheBloke/Mistral-6B-Instruct-v0.1-GGUF](https://huggingface.co/FreedomIntelligence/Apollo-6B-GGUF)
[Mistral-instruct-v0.1 System prompt](https://docs.mistral.ai/usage/guardrailing)
"""
model_path = "models"
model_name = "Apollo-6B-q8_0.gguf"
hf_hub_download(repo_id="FreedomIntelligence/Apollo-6B-GGUF", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)
current_dir = os.path.dirname(os.path.realpath(__file__))
main_path = os.path.join(current_dir, 'main')
os.chmod(main_path, os.stat(main_path).st_mode | stat.S_IEXEC)
print("Start the model init process")
model = model = GPT4All(model_name, model_path, allow_download = False, device="cpu")
print("Finish the model init process")
model.config["promptTemplate"] = "{0}"
model.config["systemPrompt"] = "You are a multiligual AI doctor, your name is Apollo."
model._is_chat_session_activated = False
max_new_tokens = 2048
# def generater(message, history, temperature, top_p, top_k):
# prompt = "<s>"
# for user_message, assistant_message in history:
# prompt += model.config["promptTemplate"].format(user_message)
# prompt += assistant_message + "</s>"
# prompt += model.config["promptTemplate"].format(message)
# outputs = []
# for token in model.generate(prompt=prompt, temp=temperature, top_k = top_k, top_p = top_p, max_tokens = max_new_tokens, streaming=True):
# outputs.append(token)
# yield "".join(outputs)
async def generater(message, history, temperature, top_p, top_k):
# 构建prompt
prompt = ""
for user_message, assistant_message in history:
prompt += model.config["promptTemplate"].format(user_message)
prompt += assistant_message
prompt += model.config["promptTemplate"].format(message)
# Debug: 打印最终的prompt以验证其正确性
print(f"Final prompt: {prompt}")
cmd = [
main_path,
"-m",os.path.join(model_path, model_name),
"--prompt", prompt
]
# 使用subprocess.Popen调用./main并流式读取输出
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
)
# 初始占位符输出
yield "Generating response..."
# 异步等待并处理输出
try:
while True:
line = process.stdout.readline()
if not line:
break # 如果没有更多的输出，结束循环
print(f"Generated line: {line.strip()}") # Debug: 打印生成的每行
yield line
except Exception as e:
print(f"Error during generation: {e}")
yield "Sorry, an error occurred while generating the response."
def vote(data: gr.LikeData):
if data.liked:
return
else:
return
chatbot = gr.Chatbot(avatar_images=('resourse/user-icon.png', 'resourse/chatbot-icon.png'),bubble_full_width = False)
additional_inputs=[
gr.Slider(
label="temperature",
value=0.5,
minimum=0.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.",
),
gr.Slider(
label="top_p",
value=1.0,
minimum=0.0,
maximum=1.0,
step=0.01,
interactive=True,
info="0.1 means only the tokens comprising the top 10% probability mass are considered. Suggest set to 1 and use temperature. 1 means 100% and will disable it",
),
gr.Slider(
label="top_k",
value=40,
minimum=0,
maximum=1000,
step=1,
interactive=True,
info="limits candidate tokens to a fixed number after sorting by probability. Setting it higher than the vocabulary size deactivates this limit.",
)
]
iface = gr.ChatInterface(
fn = generater,
title=title,
description = description,
chatbot=chatbot,
additional_inputs=additional_inputs,
examples=[
["枸杞有什么疗效"],
["I've taken several courses of antibiotics for recurring infections, and now they seem less effective. Am I developing antibiotic resistance?"],
]
)
with gr.Blocks(css="resourse/style/custom.css") as demo:
chatbot.like(vote, None, None)
iface.render()
if __name__ == "__main__":
demo.queue(max_size=3).launch()
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