Update app.py

app.py

@@ -16,60 +16,13 @@ db = mongo_client[DB_NAME]
 conversations_collection = db['conversations']
 
 DESCRIPTION = """
-# Language Models for Taiwanese Culture
-
-<p align="center">
-✍️ <a href="https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2" target="_blank">Online Demo</a>  
-•
-🤗 <a href="https://huggingface.co/yentinglin" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/yentinglin56" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/pdf/2305.13711.pdf" target="_blank">[Paper Coming Soon]</a>  
-• 👨️ <a href="https://github.com/MiuLab/Taiwan-LLaMa/tree/main" target="_blank">Github Repo</a> 
-    <br/><br/>
-    <img src="https://www.csie.ntu.edu.tw/~miulab/taiwan-llama/logo-v2.png" width="100"> <br/>
-</p>
-
-# 🌟 Checkout New [Taiwan-LLM UI](http://www.twllm.com) 🌟
-
-
-Taiwan-LLaMa is a fine-tuned model specifically designed for traditional mandarin applications. It is built upon the LLaMa 2 architecture and includes a pretraining phase with over 5 billion tokens and fine-tuning with over 490k multi-turn conversational data in Traditional Mandarin.
-
-## Key Features
-
-1. **Traditional Mandarin Support**: The model is fine-tuned to understand and generate text in Traditional Mandarin, making it suitable for Taiwanese culture and related applications.
-
-2. **Instruction-Tuned**: Further fine-tuned on conversational data to offer context-aware and instruction-following responses.
-
-3. **Performance on Vicuna Benchmark**: Taiwan-LLaMa's relative performance on Vicuna Benchmark is measured against models like GPT-4 and ChatGPT. It's particularly optimized for Taiwanese culture.
-
-4. **Flexible Customization**: Advanced options for controlling the model's behavior like system prompt, temperature, top-p, and top-k are available in the demo.
-
-## Model Versions
-
-Different versions of Taiwan-LLaMa are available:
-
-- **Taiwan-LLM v2.0 (This demo)**: Cleaner pretraining, Better post-training
-- **Taiwan-LLM v1.0**: Optimized for Taiwanese Culture
-- **Taiwan-LLM v0.9**: Partial instruction set
-- **Taiwan-LLM v0.0**: No Traditional Mandarin pretraining
-
-The models can be accessed from the provided links in the Hugging Face repository.
-
-Try out the demo to interact with Taiwan-LLaMa and experience its capabilities in handling Traditional Mandarin!
+# Breeze
 """
 
 LICENSE = """
-## Licenses
-
-- Code is licensed under Apache 2.0 License.
-- Models are licensed under the LLAMA 2 Community License.
-- By using this model, you agree to the terms and conditions specified in the license.
-- By using this demo, you agree to share your input utterances with us to improve the model.
-
-## Acknowledgements
-
-Taiwan-LLaMa project acknowledges the efforts of the [Meta LLaMa team](https://github.com/facebookresearch/llama) and [Vicuna team](https://github.com/lm-sys/FastChat) in democratizing large language models.
 """
 
-DEFAULT_SYSTEM_PROMPT = "你是人工智慧助理，以下是用戶和人工智能助理之間的對話。你要對用戶的問題提供有用、安全、詳細和禮貌的回答。 您是由國立臺灣大學的林彥廷博士生為研究目的而建造的。"
+DEFAULT_SYSTEM_PROMPT = "You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan."
 
 endpoint_url = os.environ.get("ENDPOINT_URL", "http://127.0.0.1:8080")
 client = Client(endpoint_url, timeout=120)