init

Dockerfile

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+FROM python:3.10
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["funix", "main.py", "--host", "0.0.0.0", "--port", "7860", "--no-browser"]

README.md

@@ -1,10 +1,18 @@
 ---
-title: Japanese Lm
-emoji: 🏆
-colorFrom: purple
-colorTo: green
+title: Japanese LM
 sdk: docker
+app_port: 7860
+python_version: 3.10
+models:
+    - ku-nlp/gpt2-medium-japanese-char
+    - TURX/chj-gpt2
+    - TURX/wakagpt
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Japanese Language Models
+---
+
+Final Project, STAT 453 Spring 2024, University of Wisconsin-Madison
+
+Author: Ruixuan Tu ([email protected], https://turx.asia)

main.py

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+# To run: funix main.py
+
+from transformers import AutoTokenizer
+from transformers import AutoModelForCausalLM
+import typing
+from funix import funix
+from funix.hint import HTML
+
+low_memory = True  # Set to True to run on mobile devices
+
+ku_gpt_tokenizer = AutoTokenizer.from_pretrained("ku-nlp/gpt2-medium-japanese-char")
+chj_gpt_tokenizer = AutoTokenizer.from_pretrained("TURX/chj-gpt2")
+wakagpt_tokenizer = AutoTokenizer.from_pretrained("TURX/wakagpt")
+ku_gpt_model = AutoModelForCausalLM.from_pretrained("ku-nlp/gpt2-medium-japanese-char")
+chj_gpt_model = AutoModelForCausalLM.from_pretrained("TURX/chj-gpt2")
+wakagpt_model = AutoModelForCausalLM.from_pretrained("TURX/wakagpt")
+
+print("Models loaded successfully.")
+
+model_name_map = {
+    "Kyoto University GPT-2 (Modern)": "ku-gpt2",
+    "CHJ GPT-2 (Classical)": "chj-gpt2",
+    "Waka GPT": "wakagpt",
+}
+
+waka_type_map = {
+    "kana": "[仮名]",
+    "original": "[原文]",
+    "aligned": "[整形]",
+}
+
+
+@funix(
+    title=" Home",
+    description="""
+<h1>Japanese Language Models</h1><hr>
+Final Project, STAT 453 Spring 2024, University of Wisconsin-Madison<br>
+Author: Ruixuan Tu ([email protected], https://turx.asia)<hr>
+Navigate the apps using the left sidebar.
+"""
+)
+def home():
+    return
+
+
+@funix(disable=True)
+def __generate(tokenizer: AutoTokenizer, model: AutoModelForCausalLM, prompt: str,
+               do_sample: bool, num_beams: int, num_beam_groups: int, max_new_tokens: int, temperature: float, top_k: int, top_p: float, repetition_penalty: float, num_return_sequences: int
+               ) -> str:
+    global low_memory
+    inputs = tokenizer(prompt, return_tensors="pt").input_ids
+    outputs = model.generate(inputs, low_memory=low_memory, do_sample=do_sample, num_beams=num_beams, num_beam_groups=num_beam_groups, max_new_tokens=max_new_tokens, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, num_return_sequences=num_return_sequences)
+    return tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+
+@funix(
+    title="Custom Prompt Japanese GPT-2", 
+    description="""
+<h1>Japanese GPT-2</h1><hr>
+Let a GPT-2 model to complete a Japanese sentence for you.
+""",
+    argument_labels={
+        "prompt": "Prompt in Japanese",
+        "model_type": "Model Type",
+        "max_new_tokens": "Max New Tokens to Generate",
+        "do_sample": "Do Sample",
+        "num_beams": "Number of Beams",
+        "num_beam_groups": "Number of Beam Groups",
+        "max_new_tokens": "Max New Tokens",
+        "temperature": "Temperature",
+        "top_k": "Top K",
+        "top_p": "Top P",
+        "repetition_penalty": "Repetition Penalty",
+        "num_return_sequences": "Number of Sequences to Return",
+    },
+    widgets={
+        "num_beams": "slider[1,10,1]",
+        "num_beam_groups": "slider[1,5,1]",
+        "max_new_tokens": "slider[1,512,1]",
+        "temperature": "slider[0.0,1.0,0.01]",
+        "top_k": "slider[1,100,0.1]",
+        "top_p": "slider[0.0,1.0,0.01]",
+        "repetition_penalty": "slider[1.0,2.0,0.01]",
+        "num_return_sequences": "slider[1,5,1]",
+    }
+)
+def prompt(prompt: str = "こんにちは。", model_type: typing.Literal["Kyoto University GPT-2 (Modern)", "CHJ GPT-2 (Classical)", "Waka GPT"] = "Kyoto University GPT-2 (Modern)",
+        do_sample: bool = True, num_beams: int = 1, num_beam_groups: int = 1, max_new_tokens: int = 100, temperature: float = 0.7, top_k: int = 50, top_p: float = 0.95, repetition_penalty: float = 1.0, num_return_sequences: int = 1
+        ) -> HTML:
+    model_name = model_name_map[model_type]
+    if model_name == "ku-gpt2":
+        tokenizer = ku_gpt_tokenizer
+        model = ku_gpt_model
+    elif model_name == "chj-gpt2":
+        tokenizer = chj_gpt_tokenizer
+        model = chj_gpt_model
+    elif model_name == "wakagpt":
+        tokenizer = wakagpt_tokenizer
+        model = wakagpt_model
+    else:
+        raise NotImplementedError(f"Unsupported model: {model_name}")
+    generated = __generate(tokenizer, model, prompt, do_sample, num_beams, num_beam_groups, max_new_tokens, temperature, top_k, top_p, repetition_penalty, num_return_sequences)
+    return HTML("".join([f"<p>{i}</p>" for i in generated]))
+
+
+@funix(
+    title="WakaGPT Poem Composer",
+    description="""
+<h1>WakaGPT Poem Composer</h1><hr>
+Generate a Japanese waka poem in 5-7-5-7-7 form using WakaGPT. A sample poem (Kokinshu 169) is provided below:<br>
+    Preface: 秋立つ日よめる<br>
+    Author: 敏行 藤原敏行朝臣 (018)<br>
+    Kana (Kana only with Separator): あききぬと−めにはさやかに−みえねとも−かせのおとにそ−おとろかれぬる<br>
+    Original (Kana + Kanji without Separator): あききぬとめにはさやかに見えねとも風のおとにそおとろかれぬる<br>
+    Aligned (Kana + Kanji with Separator): あききぬと−めにはさやかに−見えねとも−風のおとにそ−おとろかれぬる
+""",
+    argument_labels={
+        "preface": "Preface (Kotobagaki) in Japanese (optional)",
+        "author": "Author Name in Japanese (optional)",
+        "first_line": "First Line of Poem in Japanese (optional)",
+        "type": "Waka Type",
+        "remaining_lines": "Remaining Lines of Poem",
+        "do_sample": "Do Sample",
+        "num_beams": "Number of Beams",
+        "num_beam_groups": "Number of Beam Groups",
+        "temperature": "Temperature",
+        "top_k": "Top K",
+        "top_p": "Top P",
+        "repetition_penalty": "Repetition Penalty",
+        "num_return_sequences": "Number of Sequences to Return (at Maximum)",
+    },
+    widgets={
+        "remaining_lines": "slider[1,5,1]",
+        "num_beams": "slider[1,10,1]",
+        "num_beam_groups": "slider[1,5,1]",
+        "temperature": "slider[0.0,1.0,0.01]",
+        "top_k": "slider[1,100,0.1]",
+        "top_p": "slider[0.0,1.0,0.01]",
+        "repetition_penalty": "slider[1.0,2.0,0.01]",
+        "num_return_sequences": "slider[1,5,1]",
+    }
+)
+def waka(preface: str = "", author: str = "", first_line: str = "あききぬと−めにはさやかに−みえねとも", type: typing.Literal["Kana", "Original", "Aligned"] = "Kana", remaining_lines: int = 2,
+         do_sample: bool = True, num_beams: int = 1, num_beam_groups: int = 1, temperature: float = 0.7, top_k: int = 50, top_p: float = 0.95, repetition_penalty: float = 1.0, num_return_sequences: int = 1
+         ) -> HTML:
+    waka_prompt = ""
+    if preface:
+        waka_prompt += "[詞書] " + preface + "\n"
+    if author:
+        waka_prompt += "[作者] " + author + "\n"
+    token_counts = [5, 7, 5, 7, 7]
+    max_new_tokens = sum(token_counts[-remaining_lines:])
+    first_line = first_line.strip()
+
+    # add separators
+    if type.lower() in ["kana", "aligned"]:
+        if first_line == "":
+            max_new_tokens += 4
+        else:
+            first_line += "−" if first_line[-1] != "−" else first_line
+            max_new_tokens += remaining_lines - 1  # remaining separators
+
+    waka_prompt += waka_type_map[type.lower()] + " " + first_line
+    info = f"""
+    Prompt: {waka_prompt}<br>
+    Max New Tokens: {max_new_tokens}<br>
+    """
+    yield info + "Generating Poem..."
+    generated = __generate(wakagpt_tokenizer, wakagpt_model, waka_prompt, do_sample, num_beams, num_beam_groups, max_new_tokens, temperature, top_k, top_p, repetition_penalty, num_return_sequences)
+
+    removed = 0
+    checked_generated = []
+    if type.lower() in ["kana", "aligned"]:
+        def check(seq):
+            poem = first_line + seq[len(waka_prompt) - 1:]
+            parts = poem.split("−")
+            if len(parts) == 5 and all(len(part) == token_counts[i] for i, part in enumerate(parts)):
+                checked_generated.append(poem)
+            else:
+                nonlocal removed
+                removed += 1
+        for i in generated:
+            check(i)
+    else:
+        checked_generated = [first_line + i[len(waka_prompt) - 1:] for i in generated]
+
+    generated = [f"<p>{i}</p>" for i in checked_generated]
+    yield info + f"Removed Malformed: {removed}<br>Results:<br>{''.join(generated)}"
+
+
+if __name__ == "__main__":
+    print(prompt("こんにちは", "Kyoto University GPT-2 (Modern)", num_beams=5, num_return_sequences=5))

requirements.txt

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+transformers
+torch
+funix