Update app.py

app.py

@@ -1,42 +1,5 @@
-import os
-from threading import Thread
-from typing import Iterator
-
 import gradio as gr
-import spaces
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-
-MAX_MAX_NEW_TOKENS = 1024
-DEFAULT_MAX_NEW_TOKENS = 256
-MAX_INPUT_TOKEN_LENGTH = 512
-
-DESCRIPTION = """\
-# Buzz-3B-Small
-This Space demonstrates Buzz-3b-small-v0.6.3.
-"""
-
-LICENSE = """
-<p/>
----
-This demo uses Buzz-3b-small-v0.6.3. Please check the model card for details.
-"""
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo works better on GPU.</p>"
-
-model_id = "H-D-T/Buzz-3b-small-v0.6.3"
-
-if torch.cuda.is_available():
-    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True)
-else:
-    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cpu", trust_remote_code=True, low_cpu_mem_usage=True)
-    
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-if tokenizer.pad_token == None:
-    tokenizer.pad_token = tokenizer.eos_token
-    tokenizer.pad_token_id = tokenizer.eos_token_id
-    model.config.pad_token_id = tokenizer.eos_token_id
+from transformers import pipeline, Conversation, AutoTokenizer
 
 # Define the special tokens
 bos_token = "<|begin_of_text|>"
@@ -44,132 +7,43 @@ eos_token = "<|eot_id|>"
 start_header_id = "<|start_header_id|>"
 end_header_id = "<|end_header_id|>"
 
-def format_chat_history(chat_history: list[tuple[str, str]], add_generation_prompt=False) -> str:
-    """
-    Formats the chat history according to the model's chat template.
-    """
-    chat_template = f"""
-    {{% if not add_generation_prompt is defined %}}{{% set add_generation_prompt = false %}}{{% endif %}}
-    {{% set loop_messages = messages %}}
-    {{% for message in loop_messages %}}
-        {{% set content = '{start_header_id}' + message['role'] + '{end_header_id}\\n\\n' + message['content'].strip() + '{eos_token}' %}}
-        {{% if loop.index0 == 0 %}}{{% set content = bos_token + content %}}{{% endif %}}
-        {{ content }}
-    {{% endfor %}}
-    {{% if add_generation_prompt %}}{{ '{start_header_id}assistant{end_header_id}\\n\\n' }}{{% else %}}{{ eos_token }}{{% endif %}}
-    """
-    chat_context = ""
+# Load the conversational pipeline and tokenizer
+model_id = "H-D-T/Buzz-3b-small-v0.6.3"
+chatbot = pipeline("conversational", model=model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def format_conversation(chat_history):
+    formatted_history = ""
     for i, (user, assistant) in enumerate(chat_history):
-        user_msg = start_header_id + "user" + end_header_id + "\n\n" + user.strip() + eos_token
-        assistant_msg = start_header_id + "assistant" + end_header_id + "\n\n" + assistant.strip() + eos_token
+        user_msg = f"{start_header_id}user{end_header_id}\n\n{user.strip()}{eos_token}"
+        assistant_msg = f"{start_header_id}assistant{end_header_id}\n\n{assistant.strip()}{eos_token}"
         if i == 0:
             user_msg = bos_token + user_msg
-        chat_context += user_msg + assistant_msg
-
-    if add_generation_prompt:
-        chat_context += start_header_id + "assistant" + end_header_id + "\n\n"
-    else:
-        chat_context += eos_token
-
-    return chat_context
+        formatted_history += user_msg + assistant_msg
+    return formatted_history
 
-@spaces.GPU
-def generate(
-    message: str,
-    chat_history: list[tuple[str, str]],
-    max_new_tokens: int = 1024,
-    temperature: float = 0.6,
-    top_p: float = 0.9,
-    top_k: int = 50,
-    repetition_penalty: float = 1.4,
-) -> Iterator[str]:
-    
+def predict(message, chat_history):
     chat_history.append(("user", message))
-    chat_context = format_chat_history(chat_history, add_generation_prompt=True)
-    input_ids = tokenizer([chat_context], return_tensors="pt").input_ids
-    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
-        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
-        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
-    input_ids = input_ids.to(model.device)
-
-    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
-    generate_kwargs = dict(
-        {"input_ids": input_ids},
-        streamer=streamer,
-        max_new_tokens=max_new_tokens,
-        do_sample=True,
-        top_p=top_p,
-        top_k=top_k,
-        temperature=temperature,
-        num_beams=1,
-        pad_token_id = tokenizer.eos_token_id,
-        repetition_penalty=repetition_penalty,
-        no_repeat_ngram_size=5,
-        early_stopping=False,
-    )
-    t = Thread(target=model.generate, kwargs=generate_kwargs)
-    t.start()
-
-    outputs = []
-    for text in streamer:
-        outputs.append(text)
-        yield "".join(outputs)
-
-
-chat_interface = gr.ChatInterface(
-    fn=generate,
-    additional_inputs=[
-        gr.Slider(
-            label="Max new tokens",
-            minimum=1,
-            maximum=MAX_MAX_NEW_TOKENS,
-            step=1,
-            value=DEFAULT_MAX_NEW_TOKENS,
-        ),
-        gr.Slider(
-            label="Temperature",
-            minimum=0.1,
-            maximum=4.0,
-            step=0.1,
-            value=0.6,
-        ),
-        gr.Slider(
-            label="Top-p (nucleus sampling)",
-            minimum=0.05,
-            maximum=1.0,
-            step=0.05,
-            value=0.9,
-        ),
-        gr.Slider(
-            label="Top-k",
-            minimum=1,
-            maximum=1000,
-            step=1,
-            value=50,
-        ),
-        gr.Slider(
-            label="Repetition penalty",
-            minimum=1.0,
-            maximum=2.0,
-            step=0.05,
-            value=1.4,
-        ),
-    ],
-    stop_btn=None,
-    examples=[
-        ["A recipe for a chocolate cake:"],
-        ["Can you explain briefly to me what is the Python programming language?"],
-        ["Explain the plot of Cinderella in a sentence."],
-        ["Question: What is the capital of France?\nAnswer:"],
-        ["Question: I am very tired, what should I do?\nAnswer:"],
-    ],
-)
+    formatted_history = format_conversation(chat_history)
+    conversation = Conversation(formatted_history)
+    conversation = chatbot(conversation)
+    response = conversation.generated_responses[-1]
+    chat_history.append(("assistant", response))
+    return "", chat_history
 
 with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
-    chat_interface.render()
-    gr.Markdown(LICENSE)
+    gr.Markdown("# Buzz-3B-Small Conversational Demo")
+    with gr.Chatbot() as chatbot_ui:
+        chatbot_ui.append({"role": "assistant", "content": "Hi, how can I help you today?"})
+    with gr.Row():
+        with gr.Column():
+            textbox = gr.Textbox(label="Your message:")
+        with gr.Column():
+            submit_btn = gr.Button("Send")
+
+    chat_history = gr.State([])
+
+    submit_btn.click(predict, inputs=[textbox, chat_history], outputs=[textbox, chat_history])
 
 if __name__ == "__main__":
     demo.queue(max_size=20).launch()