first commit

.gitignore

@@ -0,0 +1,10 @@
+
+.git/
+.idea/
+
+**/flagged/
+**/__pycache__/
+
+cache/
+flagged/
+trained_models/

README.md

@@ -1,11 +1,12 @@
 ---
 title: Generate Similar Question
-emoji: 🐨
-colorFrom: yellow
-colorTo: blue
-sdk: docker
+emoji: 🐠
+colorFrom: purple
+colorTo: green
+sdk: gradio
+sdk_version: 3.41.2
+app_file: main.py
 pinned: false
-license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

examples.json

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+[
+    [
+        "如何开通微信支付？",
+        128, 0.75, 0.35, 1.2, "qgyd2021/similar_question_generation", true
+    ]
+]

main.py

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+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import argparse
+from collections import defaultdict
+import json
+import os
+import platform
+import re
+import string
+from typing import List
+
+from project_settings import project_path
+
+os.environ["HUGGINGFACE_HUB_CACHE"] = (project_path / "cache/huggingface/hub").as_posix()
+
+import gradio as gr
+from threading import Thread
+from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
+from transformers.models.bert.tokenization_bert import BertTokenizer
+from transformers.generation.streamers import TextIteratorStreamer
+import torch
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--max_new_tokens", default=512, type=int)
+    parser.add_argument("--top_p", default=0.9, type=float)
+    parser.add_argument("--temperature", default=0.35, type=float)
+    parser.add_argument("--repetition_penalty", default=1.0, type=float)
+    parser.add_argument('--device', default="cuda" if torch.cuda.is_available() else "cpu", type=str)
+
+    parser.add_argument(
+        "--examples_json_file",
+        default="examples.json",
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def repl1(match):
+    result = "{}{}".format(match.group(1), match.group(2))
+    return result
+
+
+def repl2(match):
+    result = "{}".format(match.group(1))
+    return result
+
+
+def remove_space_between_cn_en(text):
+    splits = re.split(" ", text)
+    if len(splits) < 2:
+        return text
+
+    result = ""
+    for t in splits:
+        if t == "":
+            continue
+        if re.search(f"[a-zA-Z0-9{string.punctuation}]$", result) and re.search("^[a-zA-Z0-9]", t):
+            result += " "
+            result += t
+        else:
+            if not result == "":
+                result += t
+            else:
+                result = t
+
+    if text.endswith(" "):
+        result += " "
+    return result
+
+
+def main():
+    args = get_args()
+
+    description = """
+    ## GPT2 Chat
+    """
+
+    # example json
+    with open(args.examples_json_file, "r", encoding="utf-8") as f:
+        examples = json.load(f)
+
+    if args.device == 'auto':
+        device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    else:
+        device = args.device
+
+    input_text_box = gr.Text(label="text")
+    output_text_box = gr.Text(lines=4, label="generated_content")
+
+    def fn_stream(text: str,
+                  max_new_tokens: int = 200,
+                  top_p: float = 0.85,
+                  temperature: float = 0.35,
+                  repetition_penalty: float = 1.2,
+                  model_name: str = "qgyd2021/lip_service_4chan",
+                  is_chat: bool = True,
+                  ):
+        tokenizer = BertTokenizer.from_pretrained(model_name)
+        model = GPT2LMHeadModel.from_pretrained(model_name)
+        model = model.eval()
+
+        text_encoded = tokenizer.__call__(text, add_special_tokens=False)
+        input_ids_ = text_encoded["input_ids"]
+
+        input_ids = [tokenizer.cls_token_id]
+        input_ids.extend(input_ids_)
+        if is_chat:
+            input_ids.append(tokenizer.sep_token_id)
+
+        input_ids = torch.tensor([input_ids], dtype=torch.long)
+        input_ids = input_ids.to(device)
+
+        streamer = TextIteratorStreamer(tokenizer=tokenizer)
+
+        generation_kwargs = dict(
+            inputs=input_ids,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            top_p=top_p,
+            temperature=temperature,
+            repetition_penalty=repetition_penalty,
+            eos_token_id=tokenizer.sep_token_id if is_chat else None,
+            pad_token_id=tokenizer.pad_token_id,
+            streamer=streamer,
+        )
+        thread = Thread(target=model.generate, kwargs=generation_kwargs)
+        thread.start()
+
+        output: str = ""
+        first_answer = True
+        for output_ in streamer:
+            if first_answer:
+                first_answer = False
+                continue
+
+            output_ = output_.replace("[UNK] ", "")
+            output_ = output_.replace("[UNK]", "")
+            output_ = output_.replace("[CLS] ", "")
+            output_ = output_.replace("[CLS]", "")
+
+            output += output_
+            if output.startswith("[SEP]"):
+                output = output[5:]
+
+            output = output.lstrip(" ,.!?")
+            output = remove_space_between_cn_en(output)
+            # output = re.sub(r"([，。！？\u4e00-\u9fa5]) ([，。！？\u4e00-\u9fa5])", repl1, output)
+            # output = re.sub(r"([，。！？\u4e00-\u9fa5]) ", repl2, output)
+
+            output = output.replace("[SEP] ", "\n")
+            output = output.replace("[SEP]", "\n")
+
+            yield output
+
+    model_name_choices = ["trained_models/lip_service_4chan", "trained_models/chinese_porn_novel"] \
+        if platform.system() == "Windows" else \
+        [
+            "qgyd2021/lip_service_4chan", "qgyd2021/chinese_chitchat",
+            "qgyd2021/chinese_porn_novel", "qgyd2021/few_shot_intent",
+            "qgyd2021/similar_question_generation"
+        ]
+
+    demo = gr.Interface(
+        fn=fn_stream,
+        inputs=[
+            input_text_box,
+            gr.Slider(minimum=0, maximum=512, value=512, step=1, label="max_new_tokens"),
+            gr.Slider(minimum=0, maximum=1, value=0.85, step=0.01, label="top_p"),
+            gr.Slider(minimum=0, maximum=1, value=0.35, step=0.01, label="temperature"),
+            gr.Slider(minimum=0, maximum=2, value=1.2, step=0.01, label="repetition_penalty"),
+            gr.Dropdown(choices=model_name_choices, value=model_name_choices[0], label="model_name"),
+            gr.Checkbox(value=True, label="is_chat")
+        ],
+        outputs=[output_text_box],
+        examples=examples,
+        cache_examples=False,
+        examples_per_page=50,
+        title="GPT2 Chat",
+        description=description,
+    )
+    demo.queue().launch()
+
+    return
+
+
+if __name__ == '__main__':
+    main()

project_settings.py

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+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import os
+from pathlib import Path
+
+
+project_path = os.path.abspath(os.path.dirname(__file__))
+project_path = Path(project_path)
+
+
+if __name__ == '__main__':
+    pass

requirements.txt

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+gradio==3.41.2
+pydantic==1.10.12
+thinc==7.4.6
+spacy==2.3.9
+transformers==4.30.2
+numpy==1.21.4
+tqdm==4.62.3
+torch==1.13.0
+datasets