repo_description: Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://gpt-docs.h2o.ai/
repo_topics: ai chatgpt embeddings fedramp generative gpt gpt4all llama2 llm mixtral pdf private privategpt vectorstore
code: import os
  import sys
  import time
  import traceback
  import webbrowser
  
  # uncomment below to ensure CPU install only uses CPU
  # os.environ['CUDA_VISIBLE_DEVICES'] = ''
  
  print('__file__: %s' % __file__)
  path1 = os.path.dirname(os.path.abspath(__file__))
  sys.path.append(path1)
  base_path = os.path.dirname(path1)
  sys.path.append(base_path)
  os.environ['PYTHONPATH'] = path1
  print('path1', path1, flush=True)
  
  os.environ['NLTK_DATA'] = os.path.join(base_path, './nltk_data')
  path_list = [os.environ['PATH'],
                       os.path.join(base_path, 'poppler/Library/bin/'),
                       os.path.join(base_path, 'poppler/Library/lib/'),
                       os.path.join(base_path, 'Tesseract-OCR'),
                       os.path.join(base_path, 'ms-playwright'),
                       os.path.join(base_path, 'ms-playwright/chromium-1076/chrome-win'),
                       os.path.join(base_path, 'ms-playwright/ffmpeg-1009'),
                       os.path.join(base_path, 'ms-playwright/firefox-1422/firefox'),
                       os.path.join(base_path, 'ms-playwright/webkit-1883'),
                       os.path.join(base_path, 'rubberband/')]
  os.environ['PATH'] = ';'.join(path_list)
  print(os.environ['PATH'])
  
  import shutil, errno
  
  
  def copy_tree(src, dst):
      try:
          shutil.copytree(src, dst)
      except OSError as exc: # python >2.5
          if exc.errno in (errno.ENOTDIR, errno.EINVAL):
              shutil.copy(src, dst)
          else: raise
  
  
  def setup_paths():
      for sub in ['src', 'iterators', 'gradio_utils', 'metrics', 'models', '.']:
          path2 = os.path.join(base_path, '..', sub)
          if os.path.isdir(path2):
              if sub == 'models' and os.path.isfile(os.path.join(path2, 'human.jpg')):
                  os.environ['H2OGPT_MODEL_BASE'] = path2
              sys.path.append(path2)
          print(path2, flush=True)
  
          path2 = os.path.join(path1, '..', sub)
          if os.path.isdir(path2):
              if sub == 'models' and os.path.isfile(os.path.join(path2, 'human.jpg')):
                  os.environ['H2OGPT_MODEL_BASE'] = path2
              sys.path.append(path2)
          print(path2, flush=True)
  
      # for app, avoid forbidden for web access
      if os.getenv('H2OGPT_MODEL_BASE'):
          base0 = os.environ['H2OGPT_MODEL_BASE']
          if 'Programs' in os.environ['H2OGPT_MODEL_BASE']:
              os.environ['H2OGPT_MODEL_BASE'] = os.environ['H2OGPT_MODEL_BASE'].replace('Programs', 'Temp/gradio/')
              if os.path.isdir(os.environ['H2OGPT_MODEL_BASE']):
                  shutil.rmtree(os.environ['H2OGPT_MODEL_BASE'], ignore_errors=True)
              if os.path.isfile(os.path.join(base0, 'human.jpg')):
                  copy_tree(base0, os.environ['H2OGPT_MODEL_BASE'])
  
  
  from importlib.metadata import distribution, PackageNotFoundError
  
  try:
      dtorch = distribution('torch')
      assert dtorch is not None
      have_torch = True
      torch_version = dtorch.version
  except (PackageNotFoundError, AssertionError):
      have_torch = False
      torch_version = ''
  
  
  def _main():
      setup_paths()
      os.environ['h2ogpt_block_gradio_exit'] = 'False'
      os.environ['h2ogpt_score_model'] = ''
  
      try:
          from pynvml import nvmlInit, nvmlDeviceGetCount
          nvmlInit()
          deviceCount = nvmlDeviceGetCount()
      except Exception as e:
          print("No GPUs detected by NVML: %s" % str(e))
          deviceCount = 0
  
      need_get_gpu_torch = False
      if have_torch and deviceCount > 0:
          if '+cu' not in torch_version:
              need_get_gpu_torch = True
      elif not have_torch and deviceCount > 0:
          need_get_gpu_torch = True
  
      print("Torch Status: have torch: %s need get gpu torch: %s CVD: %s GPUs: %s" % (have_torch, need_get_gpu_torch, os.getenv('CUDA_VISIBLE_DEVICES'), deviceCount))
  
      auto_install_torch_gpu = False
  
      import sys
      if auto_install_torch_gpu and (not have_torch or need_get_gpu_torch) and sys.platform == "win32":
          print("Installing Torch")
          # for one-click, don't have torch installed, install now
          import subprocess
          import sys
  
          def install(package):
              subprocess.check_call([sys.executable, "-m", "pip", "install", package])
  
          if os.getenv('TORCH_WHEEL'):
              print("Installing Torch from %s" % os.getenv('TORCH_WHEEL'))
              install(os.getenv('TORCH_WHEEL'))
          else:
              if need_get_gpu_torch:
                  wheel_file = "https://h2o-release.s3.amazonaws.com/h2ogpt/torch-2.1.2%2Bcu118-cp310-cp310-win_amd64.whl"
                  print("Installing Torch from %s" % wheel_file)
                  install(wheel_file)
              # assume cpu torch part of install
              #else:
              #   wheel_file = "https://h2o-release.s3.amazonaws.com/h2ogpt/torch-2.1.2-cp310-cp310-win_amd64.whl"
              #    print("Installing Torch from %s" % wheel_file)
              #    install(wheel_file)
          import importlib
          importlib.invalidate_caches()
          import pkg_resources
          importlib.reload(pkg_resources)  # re-load because otherwise cache would be bad
  
      from generate import entrypoint_main as main_h2ogpt
      main_h2ogpt()
  
      server_name = os.getenv('h2ogpt_server_name', os.getenv('H2OGPT_SERVER_NAME', 'localhost'))
      server_port = os.getenv('GRADIO_SERVER_PORT', str(7860))
  
      url = "http://%s:%s" % (server_name, server_port)
      webbrowser.open(url)
  
      while True:
          time.sleep(10000)
  
  
  def main():
      try:
          _main()
      except BaseException as e:
          with open('h2ogpt_exception.log', 'at') as f:
              f.write(traceback.format_exc())
          time.sleep(10)
          raise
      time.sleep(10)
  
  
  if __name__ == "__main__":
      main()
  