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import json
import time
import random
import gradio as gr
import pandas as pd
from utils.gpt_processor import QuestionAnswerer
qa_processor = QuestionAnswerer()
current_file = None
context = None
with open("final_result.json", 'r', encoding='UTF-8') as fp:
db = json.load(fp)
def read_examples():
df = pd.read_csv(r'examples.csv')
return [f"{keyword}" for keyword in df['word'].tolist()]
def user(message, history):
#return gr.update(value="", interactive=False), history + [[message, None]]
return "", history + [[message, None]]
def bot(history):
user_message = history[-1][0]
global current_file
global context
#check if user input has "我想了解"
if "我想了解" in user_message:
# get keyword from "「」"
keyword = user_message.split("「")[1].split("」")[0]
# check if keyword is in db
file_list = []
for key in db.keys():
if keyword in db[key]['keywords']:
file_list.append(key)
if len(file_list) == 0:
response = [
[user_message, "Sorry, I can't find any documents about this topic. Please try again."],
]
else:
bot_message = "以下是我所找到的文件："
for file in file_list:
bot_message += "\n" + file
bot_message += "\n\n" + "請複製貼上想要了解的文件，我會給你該文件的摘要"
response = [
[user_message, bot_message],
]
history = response
# history[-1][1] = ""
# for character in bot_message:
# history[-1][1] += character
# time.sleep(random.uniform(0.01, 0.05))
# yield history
return history
# check if user input has a pdf file name
if ".pdf" in user_message or ".docx" in user_message:
current_file = user_message
context = db[current_file]['file_full_content']
# check if file name is in db
if user_message in db.keys():
bot_message = f"文件 {user_message} 的摘要如下："
bot_message += "\n\n" + db[user_message]['summarized_content']
bot_message += "\n\n" + "可以透過詢問來了解更多這個文件的內容"
response = [
[user_message, bot_message],
]
else:
response = [
[user_message, "Sorry, I can't find this file. Please try again."],
]
history[-1] = response[0]
# history[-1][1] = ""
# for character in bot_message:
# history[-1][1] += character
# time.sleep(random.uniform(0.01, 0.05))
# yield history
return history
if context is None:
response = [
[user_message, "請輸入一個文件名稱或是點選下方的範例"],
]
history[-1] = response[0]
return history
if context is not None:
bot_message = qa_processor.answer_question(context, user_message)
response = [
[user_message, bot_message],
]
history[-1] = response[0]
return history
with gr.Blocks() as demo:
history = gr.State([])
user_question = gr.State("")
with gr.Row():
gr.HTML('Junyi Academy Chatbot')
#status_display = gr.Markdown("Success", elem_id="status_display")
with gr.Row(equal_height=True):
with gr.Column(scale=5):
with gr.Row():
chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=12):
user_input = gr.Textbox(
show_label=False,
placeholder="Enter text",
container=False,
)
# with gr.Column(min_width=70, scale=1):
# submit_btn = gr.Button("Send")
with gr.Column(min_width=70, scale=1):
clear_btn = gr.Button("Clear")
response = user_input.submit(user,
[user_input, chatbot],
[user_input, chatbot],
queue=False,
).then(bot, chatbot, chatbot)
response.then(lambda: gr.update(interactive=True), None, [user_input], queue=False)
clear_btn.click(lambda: None, None, chatbot, queue=False)
examples = gr.Examples(examples=read_examples(),
inputs=[user_input])
if __name__ == "__main__":
demo.launch()
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