first commit Qwen 0.5B
Browse files- app.py +302 -0
- chroma/28c81268-af8d-4e89-9744-aee894bcbde6/data_level0.bin +3 -0
- chroma/28c81268-af8d-4e89-9744-aee894bcbde6/header.bin +3 -0
- chroma/28c81268-af8d-4e89-9744-aee894bcbde6/index_metadata.pickle +3 -0
- chroma/28c81268-af8d-4e89-9744-aee894bcbde6/length.bin +3 -0
- chroma/28c81268-af8d-4e89-9744-aee894bcbde6/link_lists.bin +3 -0
- chroma/c2db2536-5ceb-4ce6-b43c-7b5a8994dc6a/data_level0.bin +3 -0
- chroma/c2db2536-5ceb-4ce6-b43c-7b5a8994dc6a/header.bin +3 -0
- chroma/c2db2536-5ceb-4ce6-b43c-7b5a8994dc6a/index_metadata.pickle +3 -0
- chroma/c2db2536-5ceb-4ce6-b43c-7b5a8994dc6a/length.bin +3 -0
- chroma/c2db2536-5ceb-4ce6-b43c-7b5a8994dc6a/link_lists.bin +3 -0
- data/DSC_0105.jpg +0 -0
- data/riddles_data +0 -0
- gradio_cached_examples/10/log.csv +7 -0
app.py
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1 |
+
# %%
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+
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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+
from langchain.vectorstores import Chroma
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4 |
+
from langchain.embeddings import HuggingFaceEmbeddings
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+
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+
import gradio as gr
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+
import hanzidentifier
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import re
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9 |
+
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import chinese_converter
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+
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# %%
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+
#Load the LLM model and pipeline directly
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+
llm_model_name="Qwen/Qwen1.5-0.5B-Chat"
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+
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+
#pipe = pipeline("text2text-generation", model=model)
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+
model = AutoModelForCausalLM.from_pretrained(
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+
llm_model_name
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+
)
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+
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tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
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+
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# %%
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+
# %%
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+
# loading the vector encoder
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vec_model_name = "shibing624/text2vec-base-chinese"
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+
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+
encode_kwargs = {'normalize_embeddings': False}
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model_kwargs = {'device': 'cpu'}
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+
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huggingface_embeddings= HuggingFaceEmbeddings(
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model_name=vec_model_name,
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+
model_kwargs=model_kwargs,
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encode_kwargs = encode_kwargs
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+
)
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+
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37 |
+
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38 |
+
# %%
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39 |
+
persist_directory = 'chroma/'
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+
vectordb = Chroma(embedding_function=huggingface_embeddings,persist_directory=persist_directory)
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41 |
+
print(vectordb._collection.count())
|
42 |
+
|
43 |
+
# %%
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44 |
+
text_input_label=["谜面","謎面","Riddle"]
|
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+
text_output_label=["谜底","謎底","Answer"]
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+
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clear_label = ["清除","清除","Clear"]
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submit_label = ["提交","提交","Submit"]
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+
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# %%
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+
# helper functions for prompt processing for this LLM
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+
|
53 |
+
def preprocess(text):
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text = text.replace("\n", "\\n").replace("\t", "\\t")
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return text
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+
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def postprocess(text):
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return text.replace("\\n", "\n").replace("\\t", "\t").replace('%20',' ')
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+
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+
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# get answer from LLM with prompt input
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def answer(input_text,context=""):
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prompt = f"{input_text}\n提示:\n{context}\n谜底是什么?请解释。"
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prompt = prompt.strip()
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+
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print(prompt)
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#text = preprocess(text)
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#out_text = pipe(text)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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+
text = tokenizer.apply_chat_template(
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messages,
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+
tokenize=False,
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+
add_generation_prompt=True
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+
)
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model_inputs = tokenizer([text], return_tensors="pt").to(device="cpu")
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79 |
+
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+
generated_ids = model.generate(
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model_inputs.input_ids,
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+
max_new_tokens=512,
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83 |
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do_sample=False,
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84 |
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top_p=0.0
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85 |
+
)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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+
]
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89 |
+
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90 |
+
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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91 |
+
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92 |
+
#return out_text[0]["generated_text"]
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+
return response
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94 |
+
#return postprocess(out_text[0]["generated_text"])
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95 |
+
|
96 |
+
# helper function for RAG
|
97 |
+
def helper_rag(text):
|
98 |
+
docs_out = vectordb.similarity_search_with_relevance_scores(text,k=1)
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99 |
+
#docs_out = vectordb.max_marginal_relevance_search(text,k=5,fetch_k = 20, lambda_mult = 0.5)
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100 |
+
context = ""
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101 |
+
for doc in docs_out:
|
102 |
+
if doc[1] > 0.7:
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103 |
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context += doc[0].page_content + "\n"
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104 |
+
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105 |
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return context
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106 |
+
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107 |
+
# helper function for prompt
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108 |
+
def helper_text(text_input,radio=None):
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109 |
+
chinese_type = "simplified"
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110 |
+
|
111 |
+
if hanzidentifier.is_traditional(text_input):
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112 |
+
text_input = chinese_converter.to_simplified(text_input)
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113 |
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chinese_type = "traditional"
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114 |
+
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115 |
+
text_input = re.sub(r'hint',"猜",text_input,flags=re.I)
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116 |
+
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117 |
+
if not any(c in text_input for c in ["猜", "打"]):
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118 |
+
warning = "请给一个提示,提示格式,例子:猜一水果,打一字。"
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119 |
+
if chinese_type == "traditional" or radio == "繁體中文":
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120 |
+
warning = chinese_converter.to_traditional(warning)
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+
return warning
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122 |
+
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123 |
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text=f"""猜谜语:\n谜面:{text_input}
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124 |
+
"""
|
125 |
+
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126 |
+
context = helper_rag(text)
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127 |
+
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128 |
+
output = answer(text,context=context)
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129 |
+
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130 |
+
print(output)
|
131 |
+
|
132 |
+
if chinese_type == "traditional":
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output = chinese_converter.to_traditional(output)
|
134 |
+
|
135 |
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#output = re.split(r'\s+',output)
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136 |
+
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137 |
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return output
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138 |
+
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139 |
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#return output[0]
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140 |
+
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141 |
+
|
142 |
+
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143 |
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# Gradio function for configure the language of UI
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+
def change_language(radio,text_input,text_output,markdown,
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markdown_msg1, markdown_msg2):
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146 |
+
if radio == "简体中文":
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index = 0
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148 |
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text_input_update=gr.Textbox.update(value = chinese_converter.to_simplified(text_input), label = text_input_label[index])
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149 |
+
text_output_update=gr.Textbox.update(value = chinese_converter.to_simplified(text_output),label = text_output_label[index])
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150 |
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markdown_update=gr.Markdown.update(value = chinese_converter.to_simplified(markdown))
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151 |
+
markdown_msg1_update=gr.Markdown.update(value = chinese_converter.to_simplified(markdown_msg1))
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152 |
+
markdown_msg2_update=gr.Markdown.update(value = chinese_converter.to_simplified(markdown_msg2))
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153 |
+
elif radio == "繁體中文":
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index = 1
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text_input_update=gr.Textbox.update(value = chinese_converter.to_traditional(text_input),label = text_input_label[index])
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156 |
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text_output_update=gr.Textbox.update(value = chinese_converter.to_traditional(text_output),label = text_output_label[index])
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157 |
+
markdown_update=gr.Markdown.update(value = chinese_converter.to_traditional(markdown))
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158 |
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markdown_msg1_update=gr.Markdown.update(value = chinese_converter.to_traditional(markdown_msg1))
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159 |
+
markdown_msg2_update=gr.Markdown.update(value = chinese_converter.to_traditional(markdown_msg2))
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160 |
+
elif radio == "English":
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161 |
+
index = 2
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162 |
+
text_input_update=gr.Textbox.update(label = text_input_label[index])
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163 |
+
text_output_update=gr.Textbox.update(label = text_output_label[index])
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164 |
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markdown_update=gr.Markdown.update(value = markdown)
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165 |
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markdown_msg1_update=gr.Markdown.update(value = markdown_msg1)
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166 |
+
markdown_msg2_update=gr.Markdown.update(value = markdown_msg2)
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167 |
+
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168 |
+
else:
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169 |
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index = 0
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+
text_input_update=gr.Textbox.update(label = text_input_label[index])
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171 |
+
text_output_update=gr.Textbox.update(label = text_output_label[index])
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172 |
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markdown_update=gr.Markdown.update(value = chinese_converter.to_simplified(markdown))
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173 |
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markdown_msg1_update=gr.Markdown.update(value = chinese_converter.to_simplified(markdown_msg1))
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174 |
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markdown_msg2_update=gr.Markdown.update(value = chinese_converter.to_simplified(markdown_msg2))
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175 |
+
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176 |
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clear_btn_update = gr.ClearButton.update(value = clear_label[index])
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177 |
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submit_btn_update = gr.Button.update(value = submit_label[index])
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178 |
+
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179 |
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return [text_input_update,text_output_update,clear_btn_update,submit_btn_update,markdown_update,
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180 |
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markdown_msg1_update ,markdown_msg2_update]
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181 |
+
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182 |
+
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183 |
+
def clear_text():
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184 |
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text_input_update=gr.Textbox.update(value=None)
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185 |
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text_output_update=gr.Textbox.update(value=None)
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186 |
+
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187 |
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return [text_input_update,text_output_update]
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188 |
+
|
189 |
+
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190 |
+
# %%
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191 |
+
# css = """
|
192 |
+
# #markdown { background-image: url("file/data/DSC_0105.jpg");
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193 |
+
# background-size: cover;
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194 |
+
# }
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195 |
+
# """
|
196 |
+
|
197 |
+
with gr.Blocks() as demo:
|
198 |
+
index = 0
|
199 |
+
example_list = [
|
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["小家伙穿黄袍,花丛中把房造。飞到西来飞到东,人人夸他爱劳动。(猜一动物)"],
|
201 |
+
["一物生来身穿三百多件衣,每天脱一件,年底剩张皮。(猜一物品)"],
|
202 |
+
["A thousand threads, a million strands. Reaching the water, vanishing all at once. (Hint: natural phenomenon)"],
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203 |
+
["无底洞(猜成语)"],
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204 |
+
]
|
205 |
+
radio = gr.Radio(
|
206 |
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["简体中文","繁體中文", "English"],show_label=False,value="简体中文"
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+
)
|
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+
markdown = gr.Markdown(
|
209 |
+
"""
|
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+
# Chinese Lantern Riddles Solver with LLM
|
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+
## 用大语言模型来猜灯谜
|
212 |
+
""",elem_id="markdown")
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213 |
+
with gr.Row():
|
214 |
+
with gr.Column():
|
215 |
+
text_input = gr.Textbox(label=text_input_label[index],
|
216 |
+
value="小家伙穿黄袍,花丛中把房造。飞到西来飞到东,人人夸他爱劳动。(猜一动物)", lines = 2)
|
217 |
+
with gr.Row():
|
218 |
+
clear_btn = gr.ClearButton(value=clear_label[index],components=[text_input])
|
219 |
+
submit_btn = gr.Button(value=submit_label[index], variant = "primary")
|
220 |
+
|
221 |
+
text_output = gr.Textbox(label=text_output_label[index])
|
222 |
+
|
223 |
+
|
224 |
+
examples = gr.Examples(
|
225 |
+
examples=example_list,
|
226 |
+
inputs=text_input,
|
227 |
+
outputs=text_output,
|
228 |
+
fn=helper_text,
|
229 |
+
cache_examples=True,
|
230 |
+
)
|
231 |
+
markdown_msg1 = gr.Markdown(
|
232 |
+
"""
|
233 |
+
灯谜是中华文化特色文娱活动,自北宋盛行。每年逢正月十五元宵节,将谜语贴在花灯上,让大家可一起猜谜。
|
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+
|
235 |
+
Lantern riddle is a traditional Chinese cultural activity. Being popular since the Song Dynasty (960-1276), it \
|
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+
is held in the Lantern Festival (15th day of the first lunar month). \
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+
When people are viewing the flower lanterns, they can guess the riddles on the lanterns together.
|
238 |
+
|
239 |
+
|
240 |
+
"""
|
241 |
+
)
|
242 |
+
|
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+
with gr.Column():
|
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+
markdown_msg2 = gr.Markdown(
|
245 |
+
"""
|
246 |
+
![lantern](file/data/DSC_0105.jpg)
|
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+
|
248 |
+
---
|
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# 声明 Disclaimer
|
250 |
+
|
251 |
+
本应用输出的文本为机器基于模型生成的结果,不代表任何人观点,请谨慎辨别和参考。请在法律允许的范围内使用。
|
252 |
+
|
253 |
+
本应用调用了 [ChatYuan-large-v2](https://huggingface.co/ClueAI/ChatYuan-large-v2) 对话语言大模型,\
|
254 |
+
使用本应用前请务必阅读和同意遵守其[使用授权许可证](https://huggingface.co/ClueAI/ChatYuan-large-v2/blob/main/LICENSE)。
|
255 |
+
|
256 |
+
本应用仅供非商业用途。
|
257 |
+
|
258 |
+
The outputs of this application are machine-generated with a statistical model. \
|
259 |
+
The outputs do not reflect any opinions of any human subjects. You must identify the outputs in caution. \
|
260 |
+
It is your responsbility to decide whether to accept the outputs. You must use the applicaiton in obedience to the Law.
|
261 |
+
|
262 |
+
This application utilizes [ChatYuan-large-v2](https://huggingface.co/ClueAI/ChatYuan-large-v2) \
|
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+
Conversational Large Language Model. Before using this application, you must read and accept to follow \
|
264 |
+
the [LICENSE](https://huggingface.co/ClueAI/ChatYuan-large-v2/blob/main/LICENSE).
|
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+
|
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+
This application is for non-commercial use only.
|
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+
|
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+
---
|
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+
|
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+
# 感谢 Acknowledgement
|
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+
|
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+
本应用调用了 [text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) 生成 text vector embeddings.
|
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+
该模型是以 [apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) 发行。
|
274 |
+
|
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+
This application utilizes [text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) to generate text vector embeddings.
|
276 |
+
The model is released under [apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)。
|
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+
""")
|
278 |
+
|
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+
|
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+
|
281 |
+
|
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+
|
283 |
+
|
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+
submit_btn.click(fn=helper_text, inputs=[text_input,radio], outputs=text_output)
|
285 |
+
|
286 |
+
clear_btn.click(fn=clear_text,outputs=[text_input,text_output])
|
287 |
+
radio.change(fn=change_language,inputs=[radio,text_input,text_output,
|
288 |
+
markdown, markdown_msg1,markdown_msg2],
|
289 |
+
outputs=[text_input,text_output,clear_btn,submit_btn,
|
290 |
+
markdown, markdown_msg1,markdown_msg2])
|
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+
|
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+
#demo = gr.Interface(fn=helper_text, inputs=text_input, outputs=text_output,
|
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+
# flagging_options=["Inappropriate"],allow_flagging="never",
|
294 |
+
# title="aaa",description="aaa",article="aaa")
|
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+
demo.queue(api_open=False)
|
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+
demo.launch(show_api=False)
|
297 |
+
|
298 |
+
|
299 |
+
# %%
|
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+
|
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+
|
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+
|
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|
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|
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size 8148
|
data/DSC_0105.jpg
ADDED
![]() |
data/riddles_data
ADDED
The diff for this file is too large to render.
See raw diff
|
|
gradio_cached_examples/10/log.csv
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
谜底,flag,username,timestamp
|
2 |
+
这个谜面描述了一个小家伙穿着黄色的袍子,在花丛中建造房子。这个小家伙是蜜蜂,因为蜜蜂在花朵上采集花蜜,然后将这些花蜜转化为蜂蜜。所以答案是蜜蜂。,,,2024-02-19 01:00:57.388598
|
3 |
+
谜底是日历。因为日历上的日期会随着时间的推移而改变,所以每过一年,就会有一层新的布料覆盖在上面,这就是所谓的“年轮”。因此,当最后一层布料被剥落时,就只剩下了一张没有了皮的日历。,,,2024-02-19 01:01:02.376888
|
4 |
+
"这个谜面描述了一个自然现象,即“千条线,万条线,掉到水里看不见”。这个现象通常指的是水流在地面上形成的小水滴或小水泡,这些小水滴或小水泡会随着水流的流动而消失不见。
|
5 |
+
|
6 |
+
这个谜底是雨水,因为雨水是由水滴和小水泡组成的,当它们落在地面上时,由于受到重力的作用,就会被冲走。因此,这个谜底就是雨水。",,,2024-02-19 01:01:10.248405
|
7 |
+
这个谜面是通过“无底洞”来比喻一个无法预测或理解的、非常深邃的地方,因此可以推断出成语“深不可测”。,,,2024-02-19 01:01:13.125556
|