Delete demo.py

demo.py

@@ -1,242 +0,0 @@
-"""A simple web interactive chat demo based on gradio."""
-
-from argparse import ArgumentParser
-from threading import Thread
-
-import gradio as gr
-import torch
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    StoppingCriteria,
-    StoppingCriteriaList,
-    TextIteratorStreamer,
-)
-
-
-class StopOnTokens(StoppingCriteria):
-    def __call__(
-        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
-    ) -> bool:
-        stop_ids = (
-            [2, 6, 7, 8],
-        )  # "<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|im_sep|>"
-        for stop_id in stop_ids:
-            if input_ids[0][-1] == stop_id:
-                return True
-        return False
-
-class StoppingCriteriaSub(StoppingCriteria):
-    def __init__(self, stops = [], encounters=1):
-        super().__init__()
-        self.stops = [stop.to("cuda") for stop in stops]
-
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
-        last_token = input_ids[0][-1]
-        for stop in self.stops:
-            if tokenizer.decode(stop) == tokenizer.decode(last_token):
-                return True
-        return False
-
-
-def parse_text(text):
-    lines = text.split("\n")
-    lines = [line for line in lines if line != ""]
-    count = 0
-    for i, line in enumerate(lines):
-        if "```" in line:
-            count += 1
-            items = line.split("`")
-            if count % 2 == 1:
-                lines[i] = f'<pre><code class="language-{items[-1]}">'
-            else:
-                lines[i] = f"<br></code></pre>"
-        else:
-            if i > 0:
-                if count % 2 == 1:
-                    line = line.replace("`", "\`")
-                    line = line.replace("<", "&lt;")
-                    line = line.replace(">", "&gt;")
-                    line = line.replace(" ", "&nbsp;")
-                    line = line.replace("*", "&ast;")
-                    line = line.replace("_", "&lowbar;")
-                    line = line.replace("-", "&#45;")
-                    line = line.replace(".", "&#46;")
-                    line = line.replace("!", "&#33;")
-                    line = line.replace("(", "&#40;")
-                    line = line.replace(")", "&#41;")
-                    line = line.replace("$", "&#36;")
-                lines[i] = "<br>" + line
-    text = "".join(lines)
-    return text
-
-
-def predict(history, max_length, top_p, temperature):
-    stop = StopOnTokens()
-    # messages = [{"role": "system", "content": "You are a helpful assistant"}]
-    messages = [{"role": "system", "content": ""}]
-    # messages = []   
-    for idx, (user_msg, model_msg) in enumerate(history):
-        if idx == len(history) - 1 and not model_msg:
-            messages.append({"role": "user", "content": user_msg})
-            break
-        if user_msg:
-            messages.append({"role": "user", "content": user_msg})
-        if model_msg:
-            messages.append({"role": "assistant", "content": model_msg})
-
-    print("\n\n====conversation====\n", messages)
-    model_inputs = tokenizer.apply_chat_template(
-        messages, add_generation_prompt=True, tokenize=True, return_tensors="pt"
-    ).to(next(model.parameters()).device)
-    streamer = TextIteratorStreamer(
-        tokenizer, timeout=60, skip_prompt=True, skip_special_tokens=True
-    )
-
-    # stop_words = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|im_sep|>"]
-    stop_words = ["</s>"]
-    stop_words_ids = [tokenizer(stop_word, return_tensors='pt', add_special_tokens=False)['input_ids'].squeeze() for stop_word in stop_words]
-    stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
-
-    generate_kwargs = {
-        "input_ids": model_inputs,
-        "streamer": streamer,
-        "max_new_tokens": max_length,
-        "do_sample": True,
-        "top_p": top_p,
-        "temperature": temperature,
-        "stopping_criteria": stopping_criteria,
-        "repetition_penalty": 1.1,
-    }
-    t = Thread(target=model.generate, kwargs=generate_kwargs)
-    t.start()
-
-    for new_token in streamer:
-        if new_token != "":
-            history[-1][1] += new_token
-            yield history
-
-
-def main(args):
-    with gr.Blocks() as demo:
-#         gr.Markdown(
-#             """\
-# <p align="center"><img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" style="height: 80px"/><p>"""
-#         )
-        # gr.Markdown("""<center><font size=8>Yi-Chat Bot</center>""")
-        gr.Markdown("""<center><font size=8>🦣MAmmoTH2</center>""")
-#         gr.Markdown(
-#             """\
-# <center><font size=3>This WebUI is based on Yi-Chat, developed by 01-AI.</center>"""
-#         )
-        gr.Markdown(
-            """\
-<center><font size=4>
-MAmmoTH2-8x7B-Plus <a style="text-decoration: none" href="https://huggingface.co/TIGER-Lab/MAmmoTH2-8x7B-Plus/">🤗</a> """
-# <a style="text-decoration: none" href="https://www.modelscope.cn/models/01ai/Yi-34B-Chat/summary">🤖</a>&nbsp  
-# &nbsp<a style="text-decoration: none" href="https://github.com/01-ai/Yi">Yi GitHub</a></center>
-
-        )
-
-        chatbot = gr.Chatbot()
-
-        with gr.Row():
-            with gr.Column(scale=4):
-                with gr.Column(scale=12):
-                    user_input = gr.Textbox(
-                        show_label=False,
-                        placeholder="Input...",
-                        lines=10,
-                        container=False,
-                    )
-                with gr.Column(min_width=32, scale=1):
-                    submitBtn = gr.Button("🚀 Submit")
-            with gr.Column(scale=1):
-                emptyBtn = gr.Button("🧹 Clear History")
-                max_length = gr.Slider(
-                    0,
-                    32768,
-                    value=4096,
-                    step=1.0,
-                    label="Maximum length",
-                    interactive=True,
-                )
-                top_p = gr.Slider(
-                    0, 1, value=1.0, step=0.01, label="Top P", interactive=True
-                )
-                temperature = gr.Slider(
-                    0.01, 1, value=0.7, step=0.01, label="Temperature", interactive=True
-                )
-
-        def user(query, history):
-            # return "", history + [[parse_text(query), ""]]
-            return "", history + [[query, ""]]
-
-        submitBtn.click(
-            user, [user_input, chatbot], [user_input, chatbot], queue=False
-        ).then(predict, [chatbot, max_length, top_p, temperature], chatbot)
-        user_input.submit(
-            user, [user_input, chatbot], [user_input, chatbot], queue=False
-        ).then(predict, [chatbot, max_length, top_p, temperature], chatbot)
-        emptyBtn.click(lambda: None, None, chatbot, queue=False)
-
-    demo.queue()
-
-    demo.launch(
-        server_name=args.server_name,
-        server_port=args.server_port,
-        inbrowser=args.inbrowser,
-        share=args.share
-    )
-
-
-if __name__ == "__main__":
-    parser = ArgumentParser()
-    parser.add_argument(
-        "-c",
-        "--checkpoint-path",
-        type=str,
-        default="TIGER-Lab/MAmmoTH2-8B-Plus",
-        help="Checkpoint name or path, default to %(default)r",
-    )
-    parser.add_argument(
-        "--cpu-only", action="store_true", help="Run demo with CPU only"
-    )
-    parser.add_argument(
-        "--share",
-        action="store_true",
-        default=False,
-        help="Create a publicly shareable link for the interface.",
-    )
-    parser.add_argument(
-        "--inbrowser",
-        action="store_true",
-        default=True,
-        help="Automatically launch the interface in a new tab on the default browser.",
-    )
-    parser.add_argument(
-        "--server-port", type=int, default=8110, help="Demo server port."
-    )
-    parser.add_argument(
-        "--server-name", type=str, default="127.0.0.1", help="Demo server name."
-    )
-
-    args = parser.parse_args()
-
-    tokenizer = AutoTokenizer.from_pretrained(
-        args.checkpoint_path, trust_remote_code=True
-    )
-
-    if args.cpu_only:
-        device_map = "cpu"
-    else:
-        device_map = "auto"
-
-    model = AutoModelForCausalLM.from_pretrained(
-        args.checkpoint_path,
-        device_map=device_map,
-        torch_dtype="auto",
-        trust_remote_code=True,
-    ).eval()
-
-    main(args)