File size: 12,268 Bytes
3ff622a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d651bcc
 
 
 
 
 
 
 
 
 
 
3ff622a
 
 
 
a6e127d
 
 
 
 
 
 
3ff622a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bf162bb
fa443f7
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
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350

## required lib, required "pip install"
# import transformers
# import accelerate
import openai
import llama_index
import torch
import cryptography
import cryptography.fernet
## interface libs, required "pip install"
import gradio
import huggingface_hub
import huggingface_hub.hf_api
## standard libs, no need to install
import json
import requests
import time
import os
import random
import re
import sys
import psutil
import threading
import socket
# import PIL
# import pandas
import matplotlib
class HFace_Pluto(object):
  #
  # initialize the object
  def __init__(self, name="Pluto",*args, **kwargs):
    super(HFace_Pluto, self).__init__(*args, **kwargs)
    self.author = "Duc Haba"
    self.name = name
    self._ph()
    self._pp("Hello from class", str(self.__class__) + " Class: " + str(self.__class__.__name__))
    self._pp("Code name", self.name)
    self._pp("Author is", self.author)
    self._ph()
    #
    # define class var for stable division
    self._device = 'cuda'
    self._steps = [3,8,21,55,89,144]
    self._guidances = [1.1,3.0,5.0,8.0,13.0,21.0]
    self._xkeyfile = '.xoxo'
    self._models = [] 
    self._seed = 667 # sum of walnut in ascii (or Angle 667)
    self._width = 512
    self._height = 512
    self._step = 50
    self._guidances = 7.5
    self._llama_query_engine = None
    self._llama_index_doc = None
    #self._generator = torch.Generator(device='cuda')
    self.pipes = []
    self.prompts = []
    self.images = []
    self.seeds = []
    self.fname_id = 0
    self.dname_img = "img_colab/"
    self._huggingface_key="gAAAAABkgtmOIjpnjwXFWmgh1j2et2kMjHUze-ym6h3BieAp34Sqkqv3EVYvRinETvpw-kXu7RSRl5_9FqrYe-7unfakMvMkU8nHrfB3hBSC76ZTXwkVSzlN0RfBNs9NL8BGjaSJ8mz8"
    self._gpt_key="'gAAAAABkgtoTOLPegnxNIAfBfAda17h5HIHTS_65bobO3SdDlJam07AHGrcolvk9c6IWNJtTTxaCb8_JtWnLz0Y5h9doyfL-nJZggeQ6kLtaD4XwZYcG-AtYNNGCnJzVt9AaysPDnu-KWVhnJSe-DyH0oOO33doE0g=='"
    self._fkey="=cvsOPRcWD6JONmdr4Sh6-PqF6nT1InYh965mI8f_sef"
    self._color_primary = '#2780e3' #blue
    self._color_secondary = '#373a3c' #dark gray
    self._color_success = '#3fb618' #green
    self._color_info = '#9954bb' #purple
    self._color_warning = '#ff7518' #orange
    self._color_danger = '#ff0039' #red
    self._color_mid_gray = '#495057'
    return
  #
  # pretty print output name-value line
  def _pp(self, a, b,is_print=True):
    # print("%34s : %s" % (str(a), str(b)))
    x = f'{"%34s" % str(a)} : {str(b)}'
    y = None
    if (is_print):
      print(x)
    else:
      y = x
    return y
  #
  # pretty print the header or footer lines
  def _ph(self,is_print=True):
    x = f'{"-"*34} : {"-"*34}'
    y = None
    if (is_print):
      print(x)
    else:
      y = x
    return y
  # 
  # fetch huggingface file
  def fetch_hface_files(self,
    hf_names,
    hf_space="duchaba/monty",
    local_dir="/content/"):
    f = str(hf_names) + " is not iteratable, type: " + str(type(hf_names))
    try:
      for f in hf_names:
        lo = local_dir + f
        huggingface_hub.hf_hub_download(repo_id=hf_space, filename=f,
          use_auth_token=True,repo_type=huggingface_hub.REPO_TYPE_SPACE,
          force_filename=lo)
    except:
      self._pp("*Error", f)
    return
  #
  #
  def push_hface_files(self,
    hf_names,
    hf_space="duchaba/skin_cancer_diagnose",
    local_dir="/content/"):
    f = str(hf_names) + " is not iteratable, type: " + str(type(hf_names))
    try:
      for f in hf_names:
        lo = local_dir + f
        huggingface_hub.upload_file(
          path_or_fileobj=lo, 
          path_in_repo=f, 
          repo_id=hf_space,
          repo_type=huggingface_hub.REPO_TYPE_SPACE)
    except Exception as e:
      self._pp("*Error", e)
    return
  #
  def push_hface_folder(self, hf_folder, hf_space_id, hf_dest_folder=None):
    api = huggingface_hub.HfApi()
    api.upload_folder(folder_path=hf_folder,
      repo_id=hf_space_id,
      path_in_repo=hf_dest_folder,
      repo_type="space")
    return
  #
  # Define a function to display available CPU and RAM
  def fetch_system_info(self):
    s=''
    # Get CPU usage as a percentage
    cpu_usage = psutil.cpu_percent()
    # Get available memory in bytes
    mem = psutil.virtual_memory()
    # Convert bytes to gigabytes
    mem_total_gb = mem.total / (1024 ** 3)
    mem_available_gb = mem.available / (1024 ** 3)
    mem_used_gb = mem.used / (1024 ** 3)
    # Print the results
    s += f"CPU usage: {cpu_usage}%\n"
    s += f"Total memory: {mem_total_gb:.2f} GB\n"
    s += f"Available memory: {mem_available_gb:.2f} GB\n"
    # print(f"Used memory: {mem_used_gb:.2f} GB")
    s += f"Memory usage: {mem_used_gb/mem_total_gb:.2f}%\n"
    return s
  # 
  def restart_script_periodically(self):
    while True:
      #random_time = random.randint(540, 600)
      random_time = random.randint(15800, 21600)
      time.sleep(random_time)
      os.execl(sys.executable, sys.executable, *sys.argv)
    return
  #
  def write_file(self,fname, txt):
    f = open(fname, "w")
    f.writelines("\n".join(txt))
    f.close()
    return
  # 
  def fetch_gpu_info(self):
    s=''
    try:
      s += f'Your GPU is the {torch.cuda.get_device_name(0)}\n'
      s += f'GPU ready staus {torch.cuda.is_available()}\n'
      s += f'GPU allocated RAM: {round(torch.cuda.memory_allocated(0)/1024**3,1)} GB\n'
      s += f'GPU reserved RAM {round(torch.cuda.memory_reserved(0)/1024**3,1)} GB\n'
    except Exception as e:
      s += f'**Warning, No GPU: {e}'
    return s
  #
  def _fetch_crypt(self,is_generate=False):
    s=self._fkey[::-1]
    if (is_generate):
      s=open(self._xkeyfile, "rb").read()
    return s
  #
  def _gen_key(self):
    key = cryptography.fernet.Fernet.generate_key()
    with open(self._xkeyfile, "wb") as key_file:
        key_file.write(key)
    return
  #
  def _decrypt_it(self, x):
    y = self._fetch_crypt()
    f = cryptography.fernet.Fernet(y)
    m = f.decrypt(x)
    return m.decode()
  #
  def _encrypt_it(self, x):
    key = self._fetch_crypt()
    p = x.encode()
    f = cryptography.fernet.Fernet(key)
    y = f.encrypt(p)
    return y
  #
  def _login_hface(self):
    huggingface_hub.login(self._decrypt_it(self._huggingface_key), 
      add_to_git_credential=True) # non-blocking login
    self._ph()
    return 
  #
  def _fetch_version(self):
    s = ''
    # print(f"{'torch: 2.0.1':<25} Actual: {torch.__version__}")
    # print(f"{'transformers: 4.29.2':<25} Actual: {transformers.__version__}")
    s += f"{'openai: 0.27.7,':<28} Actual: {openai.__version__}\n"
    s += f"{'huggingface_hub: 0.14.1,':<28} Actual: {huggingface_hub.__version__}\n"
    s += f"{'gradio: 3.32.0,':<28} Actual: {gradio.__version__}\n"
    s += f"{'cryptography: 3.34.0,':<28} Actual: {cryptography.__version__}\n"
    s += f"{'llama_index: 0.6.21.post1,':<28} Actual: {llama_index.__version__}\n"
    return s
  #
  def _fetch_host_ip(self):
    s=''
    hostname = socket.gethostname()
    ip_address = socket.gethostbyname(hostname)
    s += f"Hostname: {hostname}\n"
    s += f"IP Address: {ip_address}\n"
    return s
  #
  def _setup_openai(self,key=None):
    if (key is None):
      key = self._decrypt_it(self._gpt_key)
    #
    openai.api_key = key
    os.environ["OPENAI_API_KEY"] = key
    return
  #
  def _fetch_index_files(self,llama_ix):
    res = []
    x = llama_ix.ref_doc_info
    for val in x.values():
      jdata = json.loads(val.to_json())
      fname = jdata['extra_info']['file_name']
      res.append(fname)
    # remove dublication name
    res = list(set(res))
    return res
# add module/method
#
import functools
def add_method(cls):
  def decorator(func):
    @functools.wraps(func) 
    def wrapper(*args, **kwargs): 
      return func(*args, **kwargs)
    setattr(cls, func.__name__, wrapper)
    return func # returning func means func can still be used normally
  return decorator
#
monty = HFace_Pluto("Monty")
monty._login_hface()
print(monty._fetch_version())
monty._ph()
print(monty.fetch_system_info())
monty._ph()
print(monty.fetch_gpu_info())
monty._ph()
print(monty._fetch_host_ip())
monty._ph()
monty._setup_openai()

@add_method(HFace_Pluto)
def load_llama_index(self,vindex='vector_index',vpath='./index_storage'):
  try:
    storage_context = llama_index.StorageContext.from_defaults(persist_dir=vpath)
    # load index
    self._llama_index_doc = llama_index.load_index_from_storage(storage_context, index_id=vindex)
    print(f'Index doc are: {self._fetch_index_files(self._llama_index_doc)}')
  except Exception as e:
    print('**Error: can not load index, check the index_storage directory or the GPT auth token')
    print('If do not have index tokens then run the .gen_llama_index() function')
    print(f'Exception: {e}')
  return

monty.load_llama_index()

@add_method(HFace_Pluto)
def ask_me(self, p):
  if (self._llama_query_engine is None):
    self._llama_query_engine = self._llama_index_doc.as_query_engine()
  resp = self._llama_query_engine.query(p)
  return resp

in_box = [gradio.Textbox(lines=1, label="Your Humana request", placeholder="Your Humana request...see example if you need help.")
  # ,gradio.Slider(0.0001, .05, value=0.001, step=.0001,label="Your Personalize Safer Value:")
  ]
out_box = [gradio.Textbox(label="Humana response:")
  # ,gradio.Textbox(lines=4, label="Response Raw JSON Data:")
  ]
#

title = "Humana and YML Fine-tune LLM model"
desc = '*Note: This model is fine-tuned by YML using GPT3.5 as the base LLM.'
arti = '''
<ul><li>The documents for fine-tuning are:</li>
<li>'ANOC - Humana Gold Plus SNP-DE (HMO-POS D-SNP).pdf', 'Humana Honor (PPO) - CMS Plan Rating.pdf', \n
'Humana Gold Plus (HMO) - CMS Plan Rating.pdf', 'docushare-app-6_8_2023 3_58_22 AM.pdf', \n
'Humana Honor (PPO) H5216-329 - Summary of Benefits.pdf', 'docushare-app-6_8_2023 4_00_10 AM.pdf', \n 
'Humana Gold Plus SNP-DE (HMO-POS D-SNP) - Summary of Benefits.pdf', 'covered-drugs.pdf', \n
'Humana Gold Plus SNP-DE (HMO-POS D-SNP) - CMS Plan Rating.pdf', 'docushare-app-6_8_2023 3_58_27 AM.pdf', \n
'Humana Honor (PPO) - Evidence of Coverage.pdf', 'docushare-app-6_8_2023 3_59_37 AM.pdf', \n
'HumanaChoice (PPO) - Evidence of Coverage.pdf', 'HumanaChoice H5216-318 (PPO)  - Summary of Benefits.pdf', \n
'docushare-app-6_8_2023 4_00_39 AM.pdf', 'Humana Gold Plus SNP-DE (HMO-POS D-SNP) - Evidence of Coverage.pdf', \n
'pre-authorization required.pdf', 'Humana Gold Plus H0028-014 (HMO) - Summary of Benefits.pdf', 'Step Therapy.pdf', \n
'Pharmacy List.pdf', 'HumanaChoice (PPO) - CMS Plan Rating.pdf', 'Humana Gold Plus (HMO) - Evidence of Coverage.pdf', \n
'docushare-app-6_8_2023 3_59_10 AM.pdf', 'docushare-app-6_8_2023 3_59_48 AM.pdf', 'docushare-app-6_8_2023 3_59_22 AM.pdf'</li>
<li><i>**Note: You can add more documentation. The more documentation the model has the smarter it will be.</i></li>
</ul>
'''
exp = [
  ['How is my plan changing'],
  ['Is my current Rx covered in the new plan?'],
  ['Is my current Doctor still in-network?'],
  ['What are my premiums and deductible amounts?'],
  ['How is my plan rated?'],
  ['What plans am I eligible for?'],
  ['What\'s the next best plan I should consider?'],  
  ['Tell me the Humana Gold Plus plan.'],
  ['Please write a summary in bullet point of the Humana Gold Plus SNP-DE H0028-015 (HMO-POS D-SNP) Annual Notice of Changes for 2023.'],
  ['Compare the Humana monthly premium and maximum out-of-pocket for in-network and out-network.'],
  ['What is the maximum dollar value allowance for in-network over the counter drug?'],
  ['Write a newsletter introducing Humana Gold Plus plan, and target it to senior citizen demographic.'],
  ['Please write a summary about the Humana and Longevity Health Partner so that a teenage can understand.'],
  ['Tell me about the state agency contact information in bullet point.'],
  ['Please tell me more about the Humana Offer Free Counseling about Medicare and Medicaid'],
  ['Write four engaging tweets about the Humana Gold Plus plan.'],
  ['Tell me something funny about Humana.'],
  ['Is Humana is same as human?']
 ]
flag_options = ['Good :-)', 'Need Improvement', 'Wrong Answer']

gradio.Interface(fn=monty.ask_me,
  inputs=in_box,
  outputs=out_box,
  examples=exp,
  title=title,
  description=desc,
  article=arti,
  flagging_options=flag_options).launch(debug=True)