SillyTavern/SillyTavern-extras
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2023-02-28 23:08:28 +02:00
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31
README.md
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# TavernAI - Extras
## What is this
A set of unofficial APIs for various [TavernAI](https://github.com/TavernAI/TavernAI) extensions
## How to run
* Install Python 3.10
* Run `pip install -r requirements.txt`
* Run `python server.py`
## Included functionality
* BART model for text summarization
* BERT model for text classification
* BLIP model for image captioning
### BLIP model for image captioning
`POST /api/caption`
#### **Input**
```
{ "image": "base64 encoded image" }
```
#### **Output**
```
{ "caption": "caption of the posted image" }
```
### BART model for text summarization
Not implemented yet
### BERT model for text classification
Not implemented yet
## Additional options
| Flag | Description |
| -------------- | -------------------------------------------------------------------- |
| `--port` | Specify the port on which the application is hosted. Default: *5100* |
| `--listen` | Hosts the app on the local network |
| `--share` | Shares the app on CloudFlare tunnel |
| `--bart-model` | Load a custom BART model.<br>Expects a HuggingFace model ID.<br>Default: [Qiliang/bart-large-cnn-samsum-ChatGPT_v3](https://huggingface.co/Qiliang/bart-large-cnn-samsum-ChatGPT_v3) |
| `--bert-model` | Load a custom BERT model.<br>Expects a HuggingFace model ID.<br>Default: [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) |
| `--blip-model` | Load a custom BLIP model.<br>Expects a HuggingFace model Id.<br>Default: [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) |

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server.py
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@@ -15,18 +15,15 @@ from io import BytesIO
DEFAULT_BART = 'Qiliang/bart-large-cnn-samsum-ChatGPT_v3'
DEFAULT_BERT = 'bhadresh-savani/distilbert-base-uncased-emotion'
DEFAULT_BLIP = 'Salesforce/blip-image-captioning-base'
UPLOAD_FOLDER = './uploads'
# Script arguments
parser = argparse.ArgumentParser(
prog = 'TavernAI Extras',
description = 'Web API for transformers models')
parser = argparse.ArgumentParser(prog = 'TavernAI Extras', description = 'Web API for transformers models')
parser.add_argument('--port', type=int, help="Specify the port on which the application is hosted")
parser.add_argument('--listen', action='store_true', help="Hosts the app on the local network")
parser.add_argument('--share', action='store_true', help="Shares the app on cloudflare tunnel")
parser.add_argument('--bart-model', help="Customize a BART model to be used by the app")
parser.add_argument('--bert-model', help="Customize a BERT model to be used by the app")
parser.add_argument('--blip-model', help="Customize a BLIP model to be used by the app")
parser.add_argument('--share', action='store_true', help="Shares the app on CloudFlare tunnel")
parser.add_argument('--bart-model', help="Load a custom BART model")
parser.add_argument('--bert-model', help="Load a custom BERT model")
parser.add_argument('--blip-model', help="Load a custom BLIP model")
args = parser.parse_args()
@@ -74,7 +71,6 @@ bert = AutoModelForSequenceClassification.from_pretrained(bert_model)
# Flask init
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['MAX_CONTENT_LENGTH'] = 100 * 1024 * 1024
# AI stuff