# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""A simple web interactive chat demo based on gradio."""
from argparse import ArgumentParser
from pathlib import Path
import copy
import gradio as gr
import os
import re
import secrets
import tempfile
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig
# from modelscope.hub.api import HubApi
from pydub import AudioSegment
import os
YOUR_ACCESS_TOKEN = os.getenv('YOUR_ACCESS_TOKEN')
# api = HubApi()
# api.login(YOUR_ACCESS_TOKEN)
# DEFAULT_CKPT_PATH = snapshot_download('qwen/Qwen-Audio-Chat')
DEFAULT_CKPT_PATH = "xun/Qwen-Audio-Chat-Int4"
def _get_args():
parser = ArgumentParser()
parser.add_argument("-c", "--checkpoint-path", type=str, default=DEFAULT_CKPT_PATH,
help="Checkpoint name or path, default to %(default)r")
parser.add_argument("--cpu-only", action="store_true", help="Run demo with CPU only")
parser.add_argument("--share", action="store_true", default=False,
help="Create a publicly shareable link for the interface.")
parser.add_argument("--inbrowser", action="store_true", default=False,
help="Automatically launch the interface in a new tab on the default browser.")
parser.add_argument("--server-port", type=int, default=7860,
help="Demo server port.")
parser.add_argument("--server-name", type=str, default="0.0.0.0",
help="Demo server name.")
args = parser.parse_args()
return args
def _load_model_tokenizer(args):
tokenizer = AutoTokenizer.from_pretrained(
args.checkpoint_path, trust_remote_code=True, resume_download=True, token=YOUR_ACCESS_TOKEN
)
if args.cpu_only:
device_map = "cpu"
else:
device_map = "cuda"
model = AutoModelForCausalLM.from_pretrained(
args.checkpoint_path,
device_map=device_map,
trust_remote_code=True,
resume_download=True,
token=YOUR_ACCESS_TOKEN
).eval()
model.generation_config = GenerationConfig.from_pretrained(
args.checkpoint_path, trust_remote_code=True, resume_download=True, token=YOUR_ACCESS_TOKEN
)
return model, tokenizer
def _parse_text(text):
lines = text.split("\n")
lines = [line for line in lines if line != ""]
count = 0
for i, line in enumerate(lines):
if "```" in line:
count += 1
items = line.split("`")
if count % 2 == 1:
lines[i] = f'
'
else:
lines[i] = f"
"
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", r"\`")
line = line.replace("<", "<")
line = line.replace(">", ">")
line = line.replace(" ", " ")
line = line.replace("*", "*")
line = line.replace("_", "_")
line = line.replace("-", "-")
line = line.replace(".", ".")
line = line.replace("!", "!")
line = line.replace("(", "(")
line = line.replace(")", ")")
line = line.replace("$", "$")
lines[i] = " " + line
text = "".join(lines)
return text
def _launch_demo(args, model, tokenizer):
uploaded_file_dir = os.environ.get("GRADIO_TEMP_DIR") or str(
Path(tempfile.gettempdir()) / "gradio"
)
def predict(_chatbot, task_history):
query = task_history[-1][0]
print("User: " + _parse_text(query))
history_cp = copy.deepcopy(task_history)
full_response = ""
history_filter = []
audio_idx = 1
pre = ""
global last_audio
for i, (q, a) in enumerate(history_cp):
if isinstance(q, (tuple, list)):
last_audio = q[0]
q = f'Audio {audio_idx}: '
pre += q + '\n'
audio_idx += 1
else:
pre += q
history_filter.append((pre, a))
pre = ""
history, message = history_filter[:-1], history_filter[-1][0]
response, history = model.chat(tokenizer, message, history=history)
ts_pattern = r"<\|\d{1,2}\.\d+\|>"
all_time_stamps = re.findall(ts_pattern, response)
print(response)
if (len(all_time_stamps) > 0) and (len(all_time_stamps) % 2 ==0) and last_audio:
ts_float = [ float(t.replace("<|","").replace("|>","")) for t in all_time_stamps]
ts_float_pair = [ts_float[i:i + 2] for i in range(0,len(all_time_stamps),2)]
# 读取音频文件
format = os.path.splitext(last_audio)[-1].replace(".","")
audio_file = AudioSegment.from_file(last_audio, format=format)
chat_response_t = response.replace("<|", "").replace("|>", "")
chat_response = chat_response_t
temp_dir = secrets.token_hex(20)
temp_dir = Path(uploaded_file_dir) / temp_dir
temp_dir.mkdir(exist_ok=True, parents=True)
# 截取音频文件
for pair in ts_float_pair:
audio_clip = audio_file[pair[0] * 1000: pair[1] * 1000]
# 保存音频文件
name = f"tmp{secrets.token_hex(5)}.{format}"
filename = temp_dir / name
audio_clip.export(filename, format=format)
_chatbot[-1] = (_parse_text(query), chat_response)
_chatbot.append((None, (str(filename),)))
else:
_chatbot[-1] = (_parse_text(query), response)
full_response = _parse_text(response)
task_history[-1] = (query, full_response)
print("Qwen-Audio-Chat: " + _parse_text(full_response))
return _chatbot
def regenerate(_chatbot, task_history):
if not task_history:
return _chatbot
item = task_history[-1]
if item[1] is None:
return _chatbot
task_history[-1] = (item[0], None)
chatbot_item = _chatbot.pop(-1)
if chatbot_item[0] is None:
_chatbot[-1] = (_chatbot[-1][0], None)
else:
_chatbot.append((chatbot_item[0], None))
return predict(_chatbot, task_history)
def add_text(history, task_history, text):
history = history + [(_parse_text(text), None)]
task_history = task_history + [(text, None)]
return history, task_history, ""
def add_file(history, task_history, file):
history = history + [((file.name,), None)]
task_history = task_history + [((file.name,), None)]
return history, task_history
def add_mic(history, task_history, file):
if file is None:
return history, task_history
os.rename(file, file + '.wav')
print("add_mic file:", file)
print("add_mic history:", history)
print("add_mic task_history:", task_history)
# history = history + [((file.name,), None)]
# task_history = task_history + [((file.name,), None)]
task_history = task_history + [((file + '.wav',), None)]
history = history + [((file + '.wav',), None)]
print("task_history", task_history)
return history, task_history
def reset_user_input():
return gr.update(value="")
def reset_state(task_history):
task_history.clear()
return []
with gr.Blocks() as demo:
gr.Markdown("""
""") ## todo
gr.Markdown("""
Qwen-Audio-Chat Bot
""")
gr.Markdown(
"""\
This WebUI is based on Qwen-Audio-Chat, developed by Alibaba Cloud.
