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import gradio as gr
import os
from huggingface_hub import InferenceClient
from huggingface_hub import hf_hub_download
import chatglm_cpp
def list_files_tree(directory, indent=""):
items = os.listdir(directory)
for i, item in enumerate(items):
prefix = "└── " if i == len(items) - 1 else "├── "
print(indent + prefix + item)
item_path = os.path.join(directory, item)
if os.path.isdir(item_path):
next_indent = indent + (" " if i == len(items) - 1 else "│ ")
list_files_tree(item_path, next_indent)
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
repo_id = "None1145/ChatGLM3-6B-Theresa-GGML"
filename = "ChatGLM3-6B-Theresa-GGML-Q4_0.bin"
hf_hub_download(repo_id=repo_id, filename=filename, local_dir=f"./Models/{repo_id}")
model = f"./Models/{repo_id}/{filename}"
max_length = 8192
pipeline = chatglm_cpp.Pipeline(model, max_length=max_length)
# messages = []
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# global messages
# print(messages)
generation_kwargs = dict(
max_length=max_length,
max_context_length=max_tokens,
do_sample=temperature > 0,
top_k=0,
top_p=top_p,
temperature=temperature,
repetition_penalty=1.0,
stream=True,
)
# if messages == []:
messages = [chatglm_cpp.ChatMessage(role="system", content=system_message)]
print(messages)
# for val in history:
# if val[0]:
# messages.append(chatglm_cpp.ChatMessage(role="user", content=val[0]))
# if val[1]:
# messages.append(chatglm_cpp.ChatMessage(role="assistant", content=val[0]))
messages.append(chatglm_cpp.ChatMessage(role="user", content=message))
response = ""
yield response
chunks = []
for chunk in pipeline.chat(messages, **generation_kwargs):
response += chunk.content
chunks.append(chunk)
yield response
messages.append(chatglm_cpp.ChatMessage(role="assistant", content=response))
print(messages)
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
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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