Spaces:
Runtime error
Runtime error
Update app.py
Browse files
app.py
CHANGED
@@ -1,64 +1,41 @@
|
|
1 |
import requests
|
2 |
import json
|
3 |
import gradio as gr
|
4 |
-
# from concurrent.futures import ThreadPoolExecutor
|
5 |
import pdfplumber
|
6 |
import pandas as pd
|
7 |
import time
|
8 |
from cnocr import CnOcr
|
9 |
from sentence_transformers import SentenceTransformer, models, util
|
10 |
-
|
11 |
-
|
12 |
-
|
13 |
-
|
14 |
-
|
15 |
-
chat_url = 'https://souljoy-my-api.hf.space/
|
16 |
headers = {
|
17 |
'Content-Type': 'application/json',
|
18 |
-
}
|
19 |
-
|
20 |
-
|
21 |
-
all_max_len = 3000
|
22 |
-
|
23 |
-
|
24 |
-
def get_emb(text):
|
25 |
-
emb_url = 'https://souljoy-my-api.hf.space/embeddings'
|
26 |
-
data = {"content": text}
|
27 |
-
try:
|
28 |
-
result = requests.post(url=emb_url,
|
29 |
-
data=json.dumps(data),
|
30 |
-
headers=headers
|
31 |
-
)
|
32 |
-
return result.json()['data'][0]['embedding']
|
33 |
-
except Exception as e:
|
34 |
-
print('data', data, 'result json', result.json())
|
35 |
|
36 |
|
37 |
-
def doc_emb(doc:
|
38 |
-
texts = doc.split('\n')
|
39 |
-
|
40 |
-
emb_list = embedder.encode(texts)
|
41 |
-
# for text in texts:
|
42 |
-
# futures.append(thread_pool_executor.submit(get_emb, text))
|
43 |
-
# for f in futures:
|
44 |
-
# emb_list.append(f.result())
|
45 |
print('\n'.join(texts))
|
46 |
return texts, emb_list, gr.Textbox.update(visible=True), gr.Button.update(visible=True), gr.Markdown.update(
|
47 |
value="""操作说明 step 3:PDF解析提交成功! 🙋 可以开始对话啦~"""), gr.Chatbot.update(visible=True)
|
48 |
|
49 |
|
50 |
-
def get_response(msg, bot, doc_text_list, doc_embeddings):
|
51 |
-
# future = thread_pool_executor.submit(get_emb, msg)
|
52 |
now_len = len(msg)
|
53 |
-
req_json = {'question': msg}
|
54 |
his_bg = -1
|
55 |
for i in range(len(bot) - 1, -1, -1):
|
56 |
if now_len + len(bot[i][0]) + len(bot[i][1]) > history_max_len:
|
57 |
break
|
58 |
now_len += len(bot[i][0]) + len(bot[i][1])
|
59 |
his_bg = i
|
60 |
-
|
61 |
-
# query_embedding = future.result()
|
62 |
query_embedding = embedder.encode([msg])
|
63 |
cos_scores = util.cos_sim(query_embedding, doc_embeddings)[0]
|
64 |
score_index = [[score, index] for score, index in zip(cos_scores, [i for i in range(len(cos_scores))])]
|
@@ -72,24 +49,33 @@ def get_response(msg, bot, doc_text_list, doc_embeddings):
|
|
72 |
index_set.add(s_i[1])
|
73 |
now_len += len(doc)
|
74 |
# 可能段落截断错误,所以把上下段也加入进来
|
75 |
-
if s_i[1] > 0 and s_i[1] -1 not in index_set:
|
76 |
-
doc = doc_text_list[s_i[1]-1]
|
77 |
if now_len + len(doc) > all_max_len:
|
78 |
break
|
79 |
-
index_set.add(s_i[1]-1)
|
80 |
now_len += len(doc)
|
81 |
if s_i[1] + 1 < len(doc_text_list) and s_i[1] + 1 not in index_set:
|
82 |
-
doc = doc_text_list[s_i[1]+1]
|
83 |
if now_len + len(doc) > all_max_len:
|
84 |
break
|
85 |
-
index_set.add(s_i[1]+1)
|
86 |
now_len += len(doc)
|
87 |
|
88 |
index_list = list(index_set)
|
89 |
index_list.sort()
|
90 |
for i in index_list:
|
91 |
sub_doc_list.append(doc_text_list[i])
|
92 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
93 |
data = {"content": json.dumps(req_json)}
|
94 |
print('data:\n', req_json)
|
95 |
result = requests.post(url=chat_url,
|
@@ -146,21 +132,23 @@ def up_file(files):
|
|
146 |
with gr.Blocks() as demo:
|
147 |
with gr.Row():
|
148 |
with gr.Column():
|
149 |
-
|
150 |
-
|
151 |
-
|
152 |
-
|
153 |
-
|
|
|
|
|
154 |
with gr.Column():
|
155 |
-
md = gr.Markdown("""操作说明 step 1:点击左侧区域,上传PDF,进行解析""")
|
156 |
-
chat_bot = gr.Chatbot(visible=False)
|
157 |
-
msg_txt = gr.Textbox(label='消息框', placeholder='输入消息,点击发送', visible=False)
|
158 |
-
|
|
|
159 |
|
160 |
file.change(up_file, [file], [txt, doc_bu, md])
|
161 |
doc_bu.click(doc_emb, [txt], [doc_text_state, doc_emb_state, msg_txt, chat_bu, md, chat_bot])
|
162 |
-
chat_bu.click(get_response, [msg_txt, chat_bot, doc_text_state, doc_emb_state], [chat_bot])
|
163 |
|
164 |
if __name__ == "__main__":
|
165 |
demo.queue().launch()
|
166 |
-
# demo.queue().launch(share=False, server_name='172.22.2.54', server_port=9191)
|
|
|
1 |
import requests
|
2 |
import json
|
3 |
import gradio as gr
|
|
|
4 |
import pdfplumber
|
5 |
import pandas as pd
|
6 |
import time
|
7 |
from cnocr import CnOcr
|
8 |
from sentence_transformers import SentenceTransformer, models, util
|
9 |
+
|
10 |
+
word_embedding_model = models.Transformer('uer/sbert-base-chinese-nli', do_lower_case=True) # BERT模型
|
11 |
+
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode='cls') # 取cls向量作为句向量
|
12 |
+
embedder = SentenceTransformer(modules=[word_embedding_model, pooling_model]) # 定义模型
|
13 |
+
ocr = CnOcr() # 初始化ocr模型
|
14 |
+
chat_url = 'https://souljoy-my-api.hf.space/chatgpt' # 你的url
|
15 |
headers = {
|
16 |
'Content-Type': 'application/json',
|
17 |
+
} # 你的headers
|
18 |
+
history_max_len = 500 # 机器人记忆的最大长度
|
19 |
+
all_max_len = 3000 # 输入的最大长度
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
20 |
|
21 |
|
22 |
+
def doc_emb(doc): # 文档向量化
|
23 |
+
texts = doc.split('\n') # 按行切分
|
24 |
+
emb_list = embedder.encode(texts) # 句向量化
|
|
|
|
|
|
|
|
|
|
|
25 |
print('\n'.join(texts))
|
26 |
return texts, emb_list, gr.Textbox.update(visible=True), gr.Button.update(visible=True), gr.Markdown.update(
|
27 |
value="""操作说明 step 3:PDF解析提交成功! 🙋 可以开始对话啦~"""), gr.Chatbot.update(visible=True)
|
28 |
|
29 |
|
30 |
+
def get_response(open_ai_key, msg, bot, doc_text_list, doc_embeddings):
|
|
|
31 |
now_len = len(msg)
|
|
|
32 |
his_bg = -1
|
33 |
for i in range(len(bot) - 1, -1, -1):
|
34 |
if now_len + len(bot[i][0]) + len(bot[i][1]) > history_max_len:
|
35 |
break
|
36 |
now_len += len(bot[i][0]) + len(bot[i][1])
|
37 |
his_bg = i
|
38 |
+
history = [] if his_bg == -1 else bot[his_bg:]
|
|
|
39 |
query_embedding = embedder.encode([msg])
|
40 |
cos_scores = util.cos_sim(query_embedding, doc_embeddings)[0]
|
41 |
score_index = [[score, index] for score, index in zip(cos_scores, [i for i in range(len(cos_scores))])]
|
|
|
49 |
index_set.add(s_i[1])
|
50 |
now_len += len(doc)
|
51 |
# 可能段落截断错误,所以把上下段也加入进来
|
52 |
+
if s_i[1] > 0 and s_i[1] - 1 not in index_set:
|
53 |
+
doc = doc_text_list[s_i[1] - 1]
|
54 |
if now_len + len(doc) > all_max_len:
|
55 |
break
|
56 |
+
index_set.add(s_i[1] - 1)
|
57 |
now_len += len(doc)
|
58 |
if s_i[1] + 1 < len(doc_text_list) and s_i[1] + 1 not in index_set:
|
59 |
+
doc = doc_text_list[s_i[1] + 1]
|
60 |
if now_len + len(doc) > all_max_len:
|
61 |
break
|
62 |
+
index_set.add(s_i[1] + 1)
|
63 |
now_len += len(doc)
|
64 |
|
65 |
index_list = list(index_set)
|
66 |
index_list.sort()
|
67 |
for i in index_list:
|
68 |
sub_doc_list.append(doc_text_list[i])
|
69 |
+
document = '' if len(sub_doc_list) == 0 else '\n'.join(sub_doc_list)
|
70 |
+
messages = [{
|
71 |
+
"role": "system",
|
72 |
+
"content": "你是一个有用的助手,可以使用文章内容准确地回答问题。使用提供的文章来生成你的答案,但避免逐字复制文章。尽可能使用自己的话。准确、有用、简洁、清晰。"
|
73 |
+
}, {"role": "system", "content": "文章内容:\n" + document}]
|
74 |
+
for his in history:
|
75 |
+
messages.append({"role": "user", "content": his[0]})
|
76 |
+
messages.append({"role": "assistant", "content": his[1]})
|
77 |
+
messages.append({"role": "user", "content": msg})
|
78 |
+
req_json = {'messages': messages, 'key': open_ai_key, 'model': "gpt-3.5-turbo"}
|
79 |
data = {"content": json.dumps(req_json)}
|
80 |
print('data:\n', req_json)
|
81 |
result = requests.post(url=chat_url,
|
|
|
132 |
with gr.Blocks() as demo:
|
133 |
with gr.Row():
|
134 |
with gr.Column():
|
135 |
+
open_ai_key = gr.Textbox(label='OpenAI API Key', placeholder='输入你的OpenAI API Key') # 你的OpenAI API Key
|
136 |
+
file = gr.File(file_types=['.pdf'], label='点击上传PDF,进行解析(支持多文档、表格、OCR)',
|
137 |
+
file_count='multiple') # 支持多文档、表格、OCR
|
138 |
+
doc_bu = gr.Button(value='开始PDF解析', visible=False) # 开始PDF解析
|
139 |
+
txt = gr.Textbox(label='PDF解析结果', visible=False) # PDF解析结果
|
140 |
+
doc_text_state = gr.State([]) # 存储PDF解析结果
|
141 |
+
doc_emb_state = gr.State([]) # 存储PDF解析结果的embedding
|
142 |
with gr.Column():
|
143 |
+
md = gr.Markdown("""操作说明 step 1:点击左侧区域,上传PDF,进行解析""") # 操作说明
|
144 |
+
chat_bot = gr.Chatbot(visible=False) # 聊天机器人
|
145 |
+
msg_txt = gr.Textbox(label='消息框', placeholder='输入消息,点击发送', visible=False) # 消息框
|
146 |
+
with gr.Row():
|
147 |
+
chat_bu = gr.Button(value='发送', visible=False)
|
148 |
|
149 |
file.change(up_file, [file], [txt, doc_bu, md])
|
150 |
doc_bu.click(doc_emb, [txt], [doc_text_state, doc_emb_state, msg_txt, chat_bu, md, chat_bot])
|
151 |
+
chat_bu.click(get_response, [open_ai_key, msg_txt, chat_bot, doc_text_state, doc_emb_state], [chat_bot])
|
152 |
|
153 |
if __name__ == "__main__":
|
154 |
demo.queue().launch()
|
|