Update app.py

app.py

@@ -1,16 +1,174 @@
+from typing import List, Union
+from pathlib import Path
 import gradio as gr
-from transformers import pipeline
+import torch
+import argparse
+from threading import Thread
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer,
+)
+import warnings
+import spaces
+import os
 
-# 加载模型
-pipe = pipeline("text-generation", model="IndexTeam/Index-1.9B-Character", trust_remote_code=True)
+warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
 
-# 定义Gradio接口
-def generate_text(prompt):
-    messages = [
-        {"role": "user", "content": prompt},
-    ]
-    result = pipe(messages)
-    return result[0]['generated_text']
+MODEL_PATH = os.environ.get('MODEL_PATH', 'IndexTeam/Index-1.9B-Character')
+TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
 
-iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
-iface.launch()
+tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, 
+                                             torch_dtype=torch.bfloat16, 
+                                             device_map="auto", 
+                                             trust_remote_code=True)
+
+def _resolve_path(path: Union[str, Path]) -> Path:
+    return Path(path).expanduser().resolve()
+
+@spaces.GPU
+def hf_gen(dialog: List, top_k, top_p, temperature, repetition_penalty, max_dec_len):
+    """
+    Generate model output with Huggingface API
+    Args:
+        dialog (List): List of dialog messages.
+        top_k (int): The number of highest probability vocabulary tokens to keep for top-k-filtering.
+        top_p (float): Only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
+        temperature (float): Strictly positive float value used to modulate the logits distribution.
+        repetition_penalty (float): The parameter for repetition penalty.
+        max_dec_len (int): The maximum numbers of tokens to generate.
+    Yields:
+        str: Real-time generation results of HF model.
+    """
+    inputs = tokenizer.apply_chat_template(dialog, tokenize=False, add_generation_prompt=False)
+    enc = tokenizer(inputs, return_tensors="pt").to("cuda")
+    streamer = TextIteratorStreamer(tokenizer, **tokenizer.init_kwargs)
+    generation_kwargs = dict(
+        enc,
+        do_sample=True,
+        top_k=int(top_k),
+        top_p=float(top_p),
+        temperature=float(temperature),
+        repetition_penalty=float(repetition_penalty),
+        max_new_tokens=int(max_dec_len),
+        pad_token_id=tokenizer.eos_token_id,
+        streamer=streamer,
+    )
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    answer = ""
+    for new_text in streamer:
+        answer += new_text
+        yield answer[len(inputs):]
+
+@spaces.GPU
+def generate(chat_history: List, query, top_k, top_p, temperature, repetition_penalty, max_dec_len, system_message):
+    """
+    Generate after hitting "submit" button
+    Args:
+        chat_history (List): List that stores all QA records.
+        query (str): Query of current round.
+        top_p (float): Only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
+        temperature (float): Strictly positive float value used to modulate the logits distribution.
+        max_dec_len (int): The maximum numbers of tokens to generate.
+    Yields:
+        List: Updated chat_history with the current round QA.
+    """
+    assert query != "", "Input must not be empty!!!"
+    # apply chat template
+    model_input = []
+    if system_message:
+        model_input.append({
+            "role": "system",
+            "content": system_message
+        })
+    for q, a in chat_history:
+        model_input.append({"role": "user", "content": q})
+        model_input.append({"role": "assistant", "content": a})
+    model_input.append({"role": "user", "content": query})
+    # yield model generation
+    chat_history.append([query, ""])
+    for answer in hf_gen(model_input, top_k, top_p, temperature, repetition_penalty, max_dec_len):
+        chat_history[-1][1] = answer.strip(tokenizer.eos_token)
+        yield gr.update(value=""), chat_history
+
+@spaces.GPU
+def regenerate(chat_history: List, top_k, top_p, temperature, repetition_penalty, max_dec_len, system_message):
+    """
+    Re-generate the answer of last round's query
+    Args:
+        chat_history (List): List that stores all QA records.
+        top_p (float): Only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
+        temperature (float): Strictly positive float value used to modulate the logits distribution.
+        max_dec_len (int): The maximum numbers of tokens to generate.
+    Yields:
+        List: Updated chat_history.
+    """
+    assert len(chat_history) >= 1, "History is empty. Nothing to regenerate!!"
+    # apply chat template
+    model_input = []
+    if system_message:
+        model_input.append({
+            "role": "system",
+            "content": system_message
+        })
+    for q, a in chat_history[:-1]:
+        model_input.append({"role": "user", "content": q})
+        model_input.append({"role": "assistant", "content": a})
+    model_input.append({"role": "user", "content": chat_history[-1][0]})
+    # yield model generation
+    for answer in hf_gen(model_input, top_k, top_p, temperature, repetition_penalty, max_dec_len):
+        chat_history[-1][1] = answer.strip(tokenizer.eos_token)
+        yield gr.update(value=""), chat_history
+
+def clear_history():
+    """
+    Clear all chat history
+    Returns:
+        List: Empty chat history
+    """
+    torch.cuda.empty_cache()
+    return []
+
+def reverse_last_round(chat_history):
+    """
+    Reverse last round QA and keep the chat history before
+    Args:
+        chat_history (List): List that stores all QA records.
+    Returns:
+        List: Updated chat_history without the last round.
+    """
+    assert len(chat_history) >= 1, "History is empty. Nothing to reverse!!"
+    return chat_history[:-1]
+
+# launch gradio demo
+with gr.Blocks(theme="soft") as demo:
+    gr.Markdown("""# Index-1.9B-Character Gradio Demo""")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            top_k = gr.Slider(1, 10, value=5, step=1, label="top_k")
+            top_p = gr.Slider(0, 1, value=0.8, step=0.1, label="top_p")
+            temperature = gr.Slider(0.1, 2.0, value=0.3, step=0.1, label="temperature")
+            repetition_penalty = gr.Slider(0.1, 2.0, value=1.1, step=0.1, label="repetition_penalty")
+            max_dec_len = gr.Slider(1, 4096, value=1024, step=1, label="max_dec_len")
+            with gr.Row():
+                system_message = gr.Textbox(label="System Message", placeholder="Input your system message", value="你是由哔哩哔哩自主研发的大语言模型，名为“Index-1.9B-Character”。你能够根据用户传入的信息，帮助用户完成指定的任务，并生成恰当的、符合要求的回复。")
+        with gr.Column(scale=10):
+            chatbot = gr.Chatbot(bubble_full_width=False, height=500, label='Index-1.9B-Character')
+            user_input = gr.Textbox(label="User", placeholder="Input your query here!", lines=8)
+            with gr.Row():
+                submit = gr.Button("🚀 Submit")
+                clear = gr.Button("🧹 Clear")
+                regen = gr.Button("🔄 Regenerate")
+                reverse = gr.Button("⬅️ Reverse")
+    
+    submit.click(generate, inputs=[chatbot, user_input, top_k, top_p, temperature, repetition_penalty, max_dec_len, system_message],
+                 outputs=[user_input, chatbot])
+    regen.click(regenerate, inputs=[chatbot, top_k, top_p, temperature, repetition_penalty, max_dec_len, system_message],
+                outputs=[user_input, chatbot])
+    clear.click(clear_history, inputs=[], outputs=[chatbot])
+    reverse.click(reverse_last_round, inputs=[chatbot], outputs=[chatbot])
+
+demo.queue().launch()