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.ipynb_checkpoints/win_run_app-checkpoint.py

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+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/')
+            shutil.rmtree(os.environ['H2OGPT_MODEL_BASE'])
+            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()