| Name | vipas JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | https://github.com/vipas-engineering/vipas-python-sdk |
| Summary | Python SDK for Vipas AI Platform |
| upload_time | 2024-08-05 09:42:03 |
| maintainer | None |
| docs_url | None |
| author | Vipas Team |
| requires_python | >=3.7 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS |
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# VIPAS AI Platform SDK
The Vipas AI Python SDK provides a simple and intuitive interface to interact with the Vipas AI platform. This SDK allows you to easily make predictions using pre-trained models hosted on the Vipas AI platform.
## Requirements.
Python 3.7+
## Installation & Usage
### pip install
You can install vipas sdk from the pip repository, using the following command:
```sh
pip install vipas
```
(you may need to run `pip` with root permission: `sudo pip install vipas`)
Then import the package:
```python
import vipas
```
## Getting Started
To get started with the Vipas AI Python SDK, you need to create a ModelClient object and use it to make predictions. Below is a step-by-step guide on how to do this.
### Basic Usage
1. Import the necessary modules:
```python
from vipas import model
```
2. Create a ModelClient object:
```python
vps_model_client = model.ModelClient()
```
3. Make a prediction:
```python
model_id = "<MODEL_ID>"
api_response = vps_model_client.predict(model_id=model_id, input_data="<INPUT_DATA>")
```
### Handling Exceptions
The SDK provides specific exceptions to handle different error scenarios:
1. UnauthorizedException: Raised when the API key is invalid or missing.
2. NotFoundException: Raised when the model is not found.
3. BadRequestException: Raised when the input data is invalid.
4. ForbiddenException: Raised when the user does not have permission to access the model.
5. ConnectionException: Raised when there is a connection error.
6. RateLimitException: Raised when the rate limit is exceeded.
7. ClientException: Raised when there is a client error.
### Asynchronous Inference Mode
Asynchronous Inference Mode is a near-real-time inference option that queues incoming requests and processes them asynchronously. This mode is suitable when you need to handle `large payloads` as they arrive or run models with long inference processing times that do not require sub-second latency. `By default, the predict method operates in asynchronous mode`, which will poll the status endpoint until the result is ready. This is ideal for batch processing or tasks where immediate responses are not critical.
#### Asynchronous Inference Mode Example
```python
api_response = vps_model_client.predict(model_id=model_id, input_data="<INPUT_DATA>", async_mode=True)
```
### Real-Time Inference Mode
Real-Time Inference Mode is designed for use cases requiring real-time predictions. In this mode, the predict method processes the request immediately and returns the result without polling the status endpoint. This mode is ideal for applications that need quick, real-time responses and can afford to handle potential timeouts for long-running inferences. It is particularly suitable for interactive applications where users expect immediate feedback.
#### Real-Time Inference Mode Example
```python
api_response = vps_model_client.predict(model_id=model_id, input_data="<INPUT_DATA>", async_mode=False)
```
### Detailed Explanation
#### Asynchronous Inference Mode
##### Description:
This mode allows the system to handle requests by queuing them and processing them as resources become available. It is beneficial for scenarios where the inference task might take longer to process, and an immediate response is not necessary.
##### Behavior:
The system polls the status endpoint to check if the result is ready and returns the result once processing is complete.
##### Ideal For:
Batch processing, large payloads, long-running inference tasks.
##### Default Setting:
By default, async_mode is set to True to support heavier inference requests.
##### Example Usage:
```python
api_response = vps_model_client.predict(model_id=model_id, input_data="<INPUT_DATA>", async_mode=True)
```
#### Real-Time Inference Mode
##### Description:
This mode is intended for use cases that require immediate results. The system processes the request directly and returns the result without polling.
##### Behavior:
The request is processed immediately, and the result is returned. If the inference takes longer than 29 seconds, a 504 Gateway Timeout error is returned.
##### Ideal For:
Applications requiring sub-second latency, interactive applications needing immediate feedback.
##### Example Usage:
```python
api_response = vps_model_client.predict(model_id=model_id, input_data="<INPUT_DATA>", async_mode=False)
```
By understanding and choosing the appropriate mode for your use case, you can optimize the performance and responsiveness of your AI applications on Vipas.AI.
### Example Usage for ModelClient using asychronous inference mode
```python
from vipas import model
from vipas.exceptions import UnauthorizedException, NotFoundException, ClientException
from vipas.logger import LoggerClient
logger = LoggerClient(__name__)
def main():
# Create a ModelClient object
vps_model_client = model.ModelClient()
# Make a prediction
try:
model_id = "<MODEL_ID>"
api_response = vps_model_client.predict(model_id=model_id, input_data="<INPUT_DATA>")
logger.info(f"Prediction response: {api_response}")
except UnauthorizedException as err:
logger.error(f"UnauthorizedException: {err}")
except NotFoundException as err:
logger.error(f"NotFoundException: {err}")
except ClientException as err:
logger.error(f"ClientException: {err}")
main()
```
## Logging
The SDK provides a LoggerClient class to handle logging. Here's how you can use it:
### LoggerClient Usage
1. Import the `LoggerClient` class:
```python
from vipas.logger import LoggerClient
```
2. Initialize the `LoggerClient`:
```python
logger = LoggerClient(__name__)
```
3. Log messages at different levels:
```python
logger.debug("This is a debug message")
logger.info("This is an info message")
logger.warning("This is a warning message")
logger.error("This is an error message")
logger.critical("This is a critical message")
```
### Example of LoggerClient
Here is a complete example demonstrating the usage of the LoggerClient:
```python
from vipas.logger import LoggerClient
def main():
logger = LoggerClient(__name__)
logger.info("Starting the main function")
try:
# Example operation
result = 10 / 2
logger.debug(f"Result of division: {result}")
except ZeroDivisionError as e:
logger.error("Error occurred: Division by zero")
except Exception as e:
logger.critical(f"Unexpected error: {str(e)}")
finally:
logger.info("End of the main function")
main()
```
## Author
VIPAS.AI
## License
This project is licensed under the terms of the [vipas.ai license](LICENSE.md).
By following the above guidelines, you can effectively use the VIPAS AI Python SDK to interact with the VIPAS AI platform for making predictions, handling exceptions, and logging activities.
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"description": "# VIPAS AI Platform SDK\nThe Vipas AI Python SDK provides a simple and intuitive interface to interact with the Vipas AI platform. This SDK allows you to easily make predictions using pre-trained models hosted on the Vipas AI platform.\n\n## Requirements.\n\nPython 3.7+\n\n## Installation & Usage\n### pip install\n\nYou can install vipas sdk from the pip repository, using the following command:\n\n```sh\npip install vipas\n```\n(you may need to run `pip` with root permission: `sudo pip install vipas`)\n\nThen import the package:\n```python\nimport vipas\n```\n\n## Getting Started\n\nTo get started with the Vipas AI Python SDK, you need to create a ModelClient object and use it to make predictions. Below is a step-by-step guide on how to do this.\n\n### Basic Usage\n\n1. Import the necessary modules:\n```python\nfrom vipas import model\n```\n\n2. Create a ModelClient object:\n```python\nvps_model_client = model.ModelClient()\n```\n\n3. Make a prediction:\n\n```python\nmodel_id = \"<MODEL_ID>\"\napi_response = vps_model_client.predict(model_id=model_id, input_data=\"<INPUT_DATA>\")\n```\n\n### Handling Exceptions\nThe SDK provides specific exceptions to handle different error scenarios:\n\n1. UnauthorizedException: Raised when the API key is invalid or missing.\n2. NotFoundException: Raised when the model is not found.\n3. BadRequestException: Raised when the input data is invalid.\n4. ForbiddenException: Raised when the user does not have permission to access the model.\n5. ConnectionException: Raised when there is a connection error.\n6. RateLimitException: Raised when the rate limit is exceeded.\n7. ClientException: Raised when there is a client error.\n\n### Asynchronous Inference Mode\nAsynchronous Inference Mode is a near-real-time inference option that queues incoming requests and processes them asynchronously. This mode is suitable when you need to handle `large payloads` as they arrive or run models with long inference processing times that do not require sub-second latency. `By default, the predict method operates in asynchronous mode`, which will poll the status endpoint until the result is ready. This is ideal for batch processing or tasks where immediate responses are not critical.\n\n\n#### Asynchronous Inference Mode Example\n```python\napi_response = vps_model_client.predict(model_id=model_id, input_data=\"<INPUT_DATA>\", async_mode=True)\n```\n### Real-Time Inference Mode\nReal-Time Inference Mode is designed for use cases requiring real-time predictions. In this mode, the predict method processes the request immediately and returns the result without polling the status endpoint. This mode is ideal for applications that need quick, real-time responses and can afford to handle potential timeouts for long-running inferences. It is particularly suitable for interactive applications where users expect immediate feedback.\n\n#### Real-Time Inference Mode Example\n```python\napi_response = vps_model_client.predict(model_id=model_id, input_data=\"<INPUT_DATA>\", async_mode=False)\n```\n\n### Detailed Explanation\n#### Asynchronous Inference Mode\n##### Description:\nThis mode allows the system to handle requests by queuing them and processing them as resources become available. It is beneficial for scenarios where the inference task might take longer to process, and an immediate response is not necessary.\n\n##### Behavior:\nThe system polls the status endpoint to check if the result is ready and returns the result once processing is complete.\n\n##### Ideal For:\nBatch processing, large payloads, long-running inference tasks.\n\n##### Default Setting:\nBy default, async_mode is set to True to support heavier inference requests.\n\n##### Example Usage:\n\n```python\napi_response = vps_model_client.predict(model_id=model_id, input_data=\"<INPUT_DATA>\", async_mode=True)\n```\n\n#### Real-Time Inference Mode\n##### Description:\nThis mode is intended for use cases that require immediate results. The system processes the request directly and returns the result without polling.\n\n##### Behavior:\nThe request is processed immediately, and the result is returned. If the inference takes longer than 29 seconds, a 504 Gateway Timeout error is returned.\n\n##### Ideal For:\nApplications requiring sub-second latency, interactive applications needing immediate feedback.\n\n##### Example Usage:\n\n```python\napi_response = vps_model_client.predict(model_id=model_id, input_data=\"<INPUT_DATA>\", async_mode=False)\n```\n\nBy understanding and choosing the appropriate mode for your use case, you can optimize the performance and responsiveness of your AI applications on Vipas.AI.\n\n\n### Example Usage for ModelClient using asychronous inference mode\n\n```python\nfrom vipas import model\nfrom vipas.exceptions import UnauthorizedException, NotFoundException, ClientException\nfrom vipas.logger import LoggerClient\n\nlogger = LoggerClient(__name__)\n\ndef main():\n # Create a ModelClient object\n vps_model_client = model.ModelClient()\n\n # Make a prediction\n try:\n model_id = \"<MODEL_ID>\"\n api_response = vps_model_client.predict(model_id=model_id, input_data=\"<INPUT_DATA>\")\n logger.info(f\"Prediction response: {api_response}\")\n except UnauthorizedException as err:\n logger.error(f\"UnauthorizedException: {err}\")\n except NotFoundException as err:\n logger.error(f\"NotFoundException: {err}\")\n except ClientException as err:\n logger.error(f\"ClientException: {err}\")\n\nmain()\n\n```\n\n## Logging\nThe SDK provides a LoggerClient class to handle logging. Here's how you can use it:\n\n### LoggerClient Usage\n\n1. Import the `LoggerClient` class:\n```python\nfrom vipas.logger import LoggerClient\n```\n\n2. Initialize the `LoggerClient`:\n```python\nlogger = LoggerClient(__name__)\n```\n\n3. Log messages at different levels:\n```python\nlogger.debug(\"This is a debug message\")\nlogger.info(\"This is an info message\")\nlogger.warning(\"This is a warning message\")\nlogger.error(\"This is an error message\")\nlogger.critical(\"This is a critical message\")\n\n```\n\n### Example of LoggerClient\nHere is a complete example demonstrating the usage of the LoggerClient:\n\n```python\nfrom vipas.logger import LoggerClient\n\ndef main():\n logger = LoggerClient(__name__)\n \n logger.info(\"Starting the main function\")\n \n try:\n # Example operation\n result = 10 / 2\n logger.debug(f\"Result of division: {result}\")\n except ZeroDivisionError as e:\n logger.error(\"Error occurred: Division by zero\")\n except Exception as e:\n logger.critical(f\"Unexpected error: {str(e)}\")\n finally:\n logger.info(\"End of the main function\")\n\nmain()\n``` \n\n## Author\nVIPAS.AI\n\n## License\nThis project is licensed under the terms of the [vipas.ai license](LICENSE.md).\n\nBy following the above guidelines, you can effectively use the VIPAS AI Python SDK to interact with the VIPAS AI platform for making predictions, handling exceptions, and logging activities.\n\n\n\n\n",
"bugtrack_url": null,
"license": " Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS",
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