Create app.py

app.py

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+import os
+
+import gradio as gr
+from text_generation import Client
+from conversation import get_default_conv_template
+from transformers import AutoTokenizer
+from pymongo import MongoClient
+
+DB_NAME = os.getenv("MONGO_DBNAME", "facebook/blenderbot-400M-distill")
+USER = os.getenv("MONGO_USER")
+PASSWORD = os.getenv("MONGO_PASSWORD")
+
+uri = f"mongodb+srv://{USER}:{PASSWORD}@{DB_NAME}.kvwjiok.mongodb.net/?retryWrites=true&w=majority"
+mongo_client = MongoClient(uri)
+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>
+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-LLaMa v1.0 (This demo)**: Optimized for Taiwanese Culture
+- **Taiwan-LLaMa v0.9**: Partial instruction set
+- **Taiwan-LLaMa 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!
+"""
+
+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 = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. You are built by NTU Miulab by Yen-Ting Lin for research purpose."
+
+endpoint_url = os.environ.get("ENDPOINT_URL", "http://127.0.0.1:8080")
+client = Client(endpoint_url, timeout=120)
+eos_token = "</s>"
+MAX_MAX_NEW_TOKENS = 1024
+DEFAULT_MAX_NEW_TOKENS = 1024
+
+max_prompt_length = 4096 - MAX_MAX_NEW_TOKENS - 10
+
+model_name = "yentinglin/Taiwan-LLaMa-v1.0"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+
+    chatbot = gr.Chatbot()
+    with gr.Row():
+        msg = gr.Textbox(
+            container=False,
+            show_label=False,
+            placeholder='Type a message...',
+            scale=10,
+        )
+        submit_button = gr.Button('Submit',
+                                  variant='primary',
+                                  scale=1,
+                                  min_width=0)
+
+    with gr.Row():
+        retry_button = gr.Button('🔄  Retry', variant='secondary')
+        undo_button = gr.Button('↩️ Undo', variant='secondary')
+        clear = gr.Button('🗑️  Clear', variant='secondary')
+
+    saved_input = gr.State()
+
+    with gr.Accordion(label='Advanced options', open=False):
+        system_prompt = gr.Textbox(label='System prompt',
+                                   value=DEFAULT_SYSTEM_PROMPT,
+                                   lines=6)
+        max_new_tokens = gr.Slider(
+            label='Max new tokens',
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        )
+        temperature = gr.Slider(
+            label='Temperature',
+            minimum=0.1,
+            maximum=1.0,
+            step=0.1,
+            value=0.7,
+        )
+        top_p = gr.Slider(
+            label='Top-p (nucleus sampling)',
+            minimum=0.05,
+            maximum=1.0,
+            step=0.05,
+            value=0.9,
+        )
+        top_k = gr.Slider(
+            label='Top-k',
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
+        )
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+
+    def bot(history, max_new_tokens, temperature, top_p, top_k, system_prompt):
+        conv = get_default_conv_template("vicuna").copy()
+        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}  # map human to USER and gpt to ASSISTANT
+        conv.system = system_prompt
+        for user, bot in history:
+            conv.append_message(roles['human'], user)
+            conv.append_message(roles["gpt"], bot)
+        msg = conv.get_prompt()
+        prompt_tokens = tokenizer.encode(msg)
+        length_of_prompt = len(prompt_tokens)
+        if length_of_prompt > max_prompt_length:
+            msg = tokenizer.decode(prompt_tokens[-max_prompt_length + 1:])
+
+        history[-1][1] = ""
+        for response in client.generate_stream(
+                msg,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                top_k=top_k,
+        ):
+            if not response.token.special:
+                character = response.token.text
+                history[-1][1] += character
+                yield history
+
+        # After generating the response, store the conversation history in MongoDB
+        conversation_document = {
+            "model_name": model_name,
+            "history": history,
+            "system_prompt": system_prompt,
+            "max_new_tokens": max_new_tokens,
+            "temperature": temperature,
+            "top_p": top_p,
+            "top_k": top_k,
+        }
+        conversations_collection.insert_one(conversation_document)
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        fn=bot,
+        inputs=[
+            chatbot,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+            system_prompt,
+        ],
+        outputs=chatbot
+    )
+    submit_button.click(
+        user, [msg, chatbot], [msg, chatbot], queue=False
+    ).then(
+        fn=bot,
+        inputs=[
+            chatbot,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+            system_prompt,
+        ],
+        outputs=chatbot
+    )
+
+
+    def delete_prev_fn(
+            history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
+        try:
+            message, _ = history.pop()
+        except IndexError:
+            message = ''
+        return history, message or ''
+
+
+    def display_input(message: str,
+                      history: list[tuple[str, str]]) -> list[tuple[str, str]]:
+        history.append((message, ''))
+        return history
+
+    retry_button.click(
+        fn=delete_prev_fn,
+        inputs=chatbot,
+        outputs=[chatbot, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=display_input,
+        inputs=[saved_input, chatbot],
+        outputs=chatbot,
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=bot,
+        inputs=[
+            chatbot,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+            system_prompt,
+        ],
+        outputs=chatbot,
+    )
+
+    undo_button.click(
+        fn=delete_prev_fn,
+        inputs=chatbot,
+        outputs=[chatbot, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=lambda x: x,
+        inputs=[saved_input],
+        outputs=msg,
+        api_name=False,
+        queue=False,
+    )
+
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+    gr.Markdown(LICENSE)
+
+demo.queue(concurrency_count=4, max_size=128)
+demo.launch()