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8d1c3e1 ca4bd3b 8d1c3e1 ca4bd3b 8d1c3e1 ca4bd3b 8d1c3e1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import openai, tiktoken, os
import pandas as pd
from openai.embeddings_utils import get_embedding, cosine_similarity
import gradio as gr
import pickle
from utils import *
openai.api_key = os.getenv("OPENAI_API_KEY")
# Split the text into chunks of 200 Chinese characters
def split_chinese_text_into_chunks(text, chunk_size=200):
chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
return chunks
# Create embeddings
def create_embedding(text1, text2, text3, text4, text5, text6, text7, text8, text9, text10):
# Concatenated texts
concatenated_text = text1 + text2 + text3 + text4 + text5 + text6 + text7 + text8 + text9 + text10
# Get the chunks
chunks = split_chinese_text_into_chunks(concatenated_text)
# Convert chunks into a Pandas DataFrame
df = pd.DataFrame({"text": chunks})
# Removing any row with empty text
df = df[df.text.ne('')]
# Counting the number of tokens for each text
df["n_tokens"] = df.text.apply(lambda x: len(encoding.encode(str(x))))
# filter too long text if any
df = df[df.n_tokens <= max_tokens]
df["embedding"] = df.text.apply(lambda x: get_embedding(x, engine=embedding_model))
print('Start saving pkl file')
with open('tmp.pkl', 'wb') as f:
pickle.dump(df, f)
print('Finish saving pkl file')
return "文章分析完成囉！請到頁面最上方，切換到 Chatbot 頁面進行問答或是文章生成"
def Bot(prompt):
with open('tmp.pkl', 'rb') as f:
df = pickle.load(f)
prompt_embedding = get_embedding(prompt, engine=embedding_model)
df["similarity"] = df.embedding.apply(lambda x: cosine_similarity(x, prompt_embedding))
results = (df.sort_values("similarity", ascending=False))
system = """
你是一個萬能文字助手，你擅長從大量的文章中，辨識出相關主題，並整理成重點摘要。
"""
messages = [{"role": "system", "content": system},]
messages.append({"role": "user", "content": prepare_prompt(prompt, results)})
return answer(messages)
with gr.Blocks() as demo:
gr.Markdown(
"""
# 文章彙整工具
輸入希望快速吸收資訊來源，即可使用問答的方式獲取資訊！
請輸入 Open AI API key 以使用本服務
"""
)
openai_api_key_textbox = gr.Textbox(placeholder="Paste your OpenAI API key (sk-...) and hit Enter",
show_label=False, lines=1, type='password')
openai_api_key_textbox.change(set_openai_api_key,
inputs=[openai_api_key_textbox],
outputs=[])
openai_api_key_textbox.submit(set_openai_api_key,
inputs=[openai_api_key_textbox],
outputs=[])
with gr.Tab("Articles"):
gr.Markdown("請在下列文字匡中輸入待彙整的文章")
input1 = gr.Textbox(label="Articles 1")
input2 = gr.Textbox(label="Articles 2")
input3 = gr.Textbox(label="Articles 3")
input4 = gr.Textbox(label="Articles 4")
input5 = gr.Textbox(label="Articles 5")
input6 = gr.Textbox(label="Articles 6")
input7 = gr.Textbox(label="Articles 7")
input8 = gr.Textbox(label="Articles 8")
input9 = gr.Textbox(label="Articles 9")
input10 = gr.Textbox(label="Articles 10")
output_text = gr.Textbox(label="處理進度")
text_button = gr.Button("開始分析")
text_button.click(fn=create_embedding, inputs=[input1, input2, input3, input4, input5, input6, input7, input8, input9, input10], outputs=output_text, api_name="create_embedding")
with gr.Tab("Chatbot"):
def predict(message, history):
bot_message = Bot(prompt = message)
partial_message = ""
for chunk in bot_message:
if len(chunk['choices'][0]['delta']) != 0:
partial_message = partial_message + chunk['choices'][0]['delta']['content']
yield partial_message
gr.ChatInterface(predict)
demo.queue().launch()
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