Create app.py

app.py

@@ -0,0 +1,147 @@
+import spaces
+
+import os
+import gradio as gr
+import torch
+from threading import Thread
+
+from typing import Union
+from pathlib import Path
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    PreTrainedModel,
+    PreTrainedTokenizer,
+    PreTrainedTokenizerFast,
+    StoppingCriteria,
+    StoppingCriteriaList,
+    TextIteratorStreamer
+)
+
+ModelType = Union[PreTrainedModel]
+TokenizerType = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
+
+MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/glm-4-9b-chat')
+TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
+
+
+def _resolve_path(path: Union[str, Path]) -> Path:
+    return Path(path).expanduser().resolve()
+
+
+def load_model_and_tokenizer(
+        model_dir: Union[str, Path], trust_remote_code: bool = True
+) -> tuple[ModelType, TokenizerType]:
+    model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=trust_remote_code, device_map='auto')
+    tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=trust_remote_code, use_fast=False)
+    return model, tokenizer
+
+
+model, tokenizer = load_model_and_tokenizer(MODEL_PATH, trust_remote_code=True)
+
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = model.config.eos_token_id
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                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
+
+@spaces.GPU
+def predict(history, max_length, top_p, temperature):
+    stop = StopOnTokens()
+    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})
+
+    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)
+    generate_kwargs = {
+        "input_ids": model_inputs,
+        "streamer": streamer,
+        "max_new_tokens": max_length,
+        "do_sample": True,
+        "top_p": top_p,
+        "temperature": temperature,
+        "stopping_criteria": StoppingCriteriaList([stop]),
+        "repetition_penalty": 1.2,
+        "eos_token_id": model.config.eos_token_id,
+    }
+    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
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">GLM-4-9B Gradio Simple Chat Demo</h1>""")
+    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=8192, step=1.0, label="Maximum length", interactive=True)
+            top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True)
+            temperature = gr.Slider(0.01, 1, value=0.6, step=0.01, label="Temperature", interactive=True)
+
+
+    def user(query, history):
+        return "", history + [[parse_text(query), ""]]
+
+
+    submitBtn.click(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().launch()
+