Delete app.py

app.py

@@ -1,121 +0,0 @@
-import time
-
-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
-
-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'
-
-import os
-
-import http.client, urllib
-def send_message(message):
-    conn = http.client.HTTPSConnection("api.pushover.net:443")
-    conn.request("POST", "/1/messages.json",
-      urllib.parse.urlencode({
-        "token": "ar2zh3pd82ujvjdmn27kdn5axapsjm",
-        "user": "ucvqife229ek8qiymrircodezw2vm2",
-        "message": message,
-      }), { "Content-type": "application/x-www-form-urlencoded" })
-    conn.getresponse()
-
-
-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))
-    send_message(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)