File size: 3,786 Bytes
a5ba5b0 4a27b83 2a8bf34 4a27b83 2a8bf34 4a27b83 2a8bf34 a5ba5b0 |
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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
# from sklearn.decomposition import PCA
from sklearn.metrics import DistanceMetric
# import matplotlib.pyplot as plt
# import matplotlib as mpl
import warnings
warnings.filterwarnings("ignore")
import gradio as gr
# from transformers.utils.hub import move_cache
#
# move_cache()
# from mailersend import emails
import asyncio
from telegram import Bot
import os
def load_resolv_conf():
with open('/etc/resolv.conf', 'r') as resolv_conf:
return resolv_conf.readlines()
load_resolv_conf()
token = '7370905765:AAHvmlw68cW7RxWzsJE1yxTzgf3xQFiokDo'
chat_id = '7431171535'
pd.set_option('display.max_colwidth', None)
df_resume = pd.read_csv('QA.csv',encoding='latin-1')
# df_resume['role'][df_resume['role'].iloc[-1] == df_resume['role']] = "Other" # relabel random role as "other"
print(df_resume.head())
model = SentenceTransformer("all-MiniLM-L6-v2")
# file_name = 'questions.txt'
async def send_telegram_message(token, chat_id, message):
bot = Bot(token=token)
await bot.send_message(chat_id=chat_id, text=message)
print("Message sent successfully.")
def savevectorstore():
# store embed vectors
embedding_arr = model.encode(df_resume['question'])
print(embedding_arr.shape)
np.save('embeddings.npy', embedding_arr)
# savevectorstore()
# load embed vectors
def rag_chain(question,name):
embedding_arr = np.load('embeddings.npy')
# print(embedding_arr.shape)
# pca = PCA(n_components=2).fit(embedding_arr)
# print(pca.explained_variance_ratio_)
query = question
query_embedding = model.encode(query)
dist = DistanceMetric.get_metric('euclidean') # other distances: manhattan, chebyshev
# compute pair wise distances between query embedding and all resume embeddings
dist_arr = dist.pairwise(embedding_arr, query_embedding.reshape(1, -1)).flatten()
# sort results
idist_arr_sorted = np.argsort(dist_arr)
# print(df_resume['name'].iloc[idist_arr_sorted[:1]].to_string(index=False))
# print(df_resume['name'].iloc[idist_arr_sorted[:1]])
que = f"**Most relevant question.**<br><br>{str(df_resume['question'].iloc[idist_arr_sorted[:1]].to_string(index=False)).replace('||','<br>')}"
# que = df_resume['question'].iloc[idist_arr_sorted[:1]]
ans = f"**Answer ideas.**<br><br>{str(df_resume['answer'].iloc[idist_arr_sorted[:1]].to_string(index=False)).replace('||','<br>')}"
# ans = df_resume['answer'].iloc[idist_arr_sorted[:1]]
# profit = df_resume['profit'].iloc[idist_arr_sorted[:1]].to_string(index=False)
# product = df_resume['product'].iloc[idist_arr_sorted[:1]].to_string(index=False)
# que1 = que
# ans1 = ans
# print(que1)
# print(ans1)
# que2= f'''Most relevant question
#
#
# {que1}'''
# ans2= f'''Answer ideas
#
#
# {ans1}'''
# print(que2)
# print(ans2)
# with open('questions.txt', 'a',encoding='utf-8') as file:
# file.write(f"\n\n{question}\n\n{country}\n\n{whatsapp}\n\n{name}||{sales}||{profit}||{product}")
message = f"{name}\n\n{question}\n\n{ans}"
asyncio.run(send_telegram_message(token, chat_id, message))
return que,ans
# rag_chain('I am very hungry.')
desc = "This is an awesome ML App. I'm really excited to show you"
long_desc = "如果我没有回答你的问题，把问题发给Eric吧。"
search_interface = gr.Interface(
fn=rag_chain,
inputs=[gr.Textbox(label="Question"),gr.Textbox(label="Name")],
outputs=[gr.Markdown(label="Most relevant question"),gr.Markdown(label="Answer ideas")],
title="Ask Eric",
description="Hi,我是数字分身，欢迎提问！",
# theme=gr.themes.Glass
article=long_desc
)
search_interface.launch(share=True,debug=True) |