remove demo

app.py

@@ -1,219 +1,16 @@
-import logging
-import os
-import sys
-import time
-from threading import Thread
-
 import gradio as gr
-import torch
-from transformers import AutoProcessor, LlamaTokenizer, StoppingCriteria, TextIteratorStreamer
-
-os.system("git clone https://github.com/turingmotors/heron && cd heron && pip install -e .")
-
-sys.path.insert(0, "./heron")
-from heron.models.video_blip import VideoBlipForConditionalGeneration, VideoBlipProcessor
-
-logger = logging.getLogger(__name__)
-
-title_markdown = """
-# Heronチャットデモ
-
-- モデル: [turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0](https://huggingface.co/turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0)
-- 学習コード: [Heron](https://github.com/turingmotors/heron)
-"""
-
-
-# This class is copied from llava: https://github.com/haotian-liu/LLaVA/blob/main/llava/mm_utils.py#L51-L74
-class KeywordsStoppingCriteria(StoppingCriteria):
-    def __init__(self, keywords, tokenizer, input_ids):
-        self.keywords = keywords
-        self.keyword_ids = []
-        for keyword in keywords:
-            cur_keyword_ids = tokenizer(keyword).input_ids
-            if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
-                cur_keyword_ids = cur_keyword_ids[1:]
-            self.keyword_ids.append(torch.tensor(cur_keyword_ids))
-        self.tokenizer = tokenizer
-        self.start_len = input_ids.shape[1]
-
-    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)"  # TODO
-        offset = min(output_ids.shape[1] - self.start_len, 3)
-        self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
-        for keyword_id in self.keyword_ids:
-            if output_ids[0, -keyword_id.shape[0] :] == keyword_id:
-                return True
-        outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
-        for keyword in self.keywords:
-            if keyword in outputs:
-                return True
-        return False
-
-
-def preprocess(history, image):
-    text = ""
-    for one_history in history:
-        text += f"##human: {one_history[0]}\n##gpt: "
-    # do preprocessing
-    inputs = processor(
-        text=text,
-        images=image,
-        return_tensors="pt",
-        truncation=True,
-    )
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    inputs["pixel_values"] = inputs["pixel_values"].to(device, torch.float16)
-    return inputs
-
-
-def add_text(textbox, history):
-    history = history + [(textbox, None)]
-    return "", history
-
-
-def stream_bot(imagebox, history):
-    # do preprocessing
-    inputs = preprocess(history, imagebox)
-
-    # streamer = TextStreamer(processor.tokenizer, skip_prompt=True, skip_special_tokens=True)
-    streamer = TextIteratorStreamer(
-        processor.tokenizer,
-        skip_prompt=True,
-        skip_special_tokens=True,
-        do_sample=False,
-        temperature=0.2,
-        no_repeat_ngram_size=2,
-    )
-    stopping_criteria = KeywordsStoppingCriteria(
-        [EOS_WORDS], processor.tokenizer, inputs["input_ids"]
-    )
 
-    inputs.update(
-        dict(
-            streamer=streamer,
-            max_new_tokens=max_length,
-            stopping_criteria=[stopping_criteria],
-            no_repeat_ngram_size=2,
-            eos_token_id=[processor.tokenizer.pad_token_id],
-        )
-    )
-    thread = Thread(target=model.generate, kwargs=inputs)
-    thread.start()
-
-    history[-1][1] = ""
-    for new_text in streamer:
-        history[-1][1] += new_text
-        history[-1][1] = history[-1][1].replace(EOS_WORDS, "")
-        time.sleep(0.05)
-        yield history
-
-
-def regenerate(history):
-    history[-1] = (history[-1][0], None)
-    return history
-
-
-def clear_history():
-    return [], "", None
 
+title_markdown = "デモはこちら(https://9255-35-232-109-220.ngrok-free.app/)に移転しました。"
 
 def build_demo():
-    textbox = gr.Textbox(
-        show_label=False, placeholder="Enter text and press ENTER", visible=True, container=False
-    )
+
     with gr.Blocks(title="Heron", theme=gr.themes.Base()) as demo:
         gr.Markdown(title_markdown)
-        with gr.Row():
-            with gr.Column(scale=6):
-                imagebox = gr.Image(type="pil")
-
-                # gr.Examples(
-                #     examples=[
-                #         [
-                #             "./images/bus_kyoto.png",
-                #             "この道路を運転する時には何に気をつけるべきですか？",
-                #         ],
-                #         [
-                #             "./images/bear.png",
-                #             "この画像には何が写っていますか？",
-                #         ],
-                #         [
-                #             "./images/water_bus.png",
-                #             "画像には何が写っていますか？",
-                #         ],
-                #         [
-                #             "./images/extreme_ironing.jpg",
-                #             "この画像の面白い点は何ですか？",
-                #         ],
-                #         [
-                #             "./images/heron.png",
-                #             "この画像はどういう点が面白いですか？",
-                #         ],
-                #     ],
-                #     inputs=[imagebox, textbox],
-                # )
-
-            with gr.Column(scale=6):
-                chatbot = gr.Chatbot(
-                    elem_id="chatbot",
-                    label="Heron Chatbot",
-                    visible=True,
-                    height=550,
-                    avatar_images=("./images/user_icon.png", "./images/heron.png"),
-                )
-                with gr.Row():
-                    with gr.Column(scale=8):
-                        textbox.render()
-                    with gr.Column(scale=1, min_width=60):
-                        submit_btn = gr.Button(value="Submit", visible=True)
-                with gr.Row():
-                    regenerate_btn = gr.Button(value="Regenerate", visible=True)
-                    clear_btn = gr.Button(value="Clear history", visible=True)
-
-        regenerate_btn.click(regenerate, chatbot, chatbot).then(
-            stream_bot,
-            [imagebox, chatbot],
-            [chatbot],
-        )
-        clear_btn.click(clear_history, None, [chatbot, textbox, imagebox])
-
-        textbox.submit(add_text, [textbox, chatbot], [textbox, chatbot], queue=False).then(
-            stream_bot,
-            [imagebox, chatbot],
-            [chatbot],
-        )
-        submit_btn.click(add_text, [textbox, chatbot], [textbox, chatbot], queue=False).then(
-            stream_bot,
-            [imagebox, chatbot],
-            [chatbot],
-        )
 
     return demo
 
 
 if __name__ == "__main__":
-    EOS_WORDS = "##"
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(f"torch.cuda.is_available(): {torch.cuda.is_available()}")
-    max_length = 512
-    MODEL_NAME = "turing-motors/heron-chat-blip-ja-stablelm-base-7b-v0"
-
-    # prepare a pretrained model
-    model = VideoBlipForConditionalGeneration.from_pretrained(
-        MODEL_NAME, torch_dtype=torch.float16, ignore_mismatched_sizes=True
-    )
-
-    model = model.half()
-    model.eval()
-    model.to(device)
-
-    # prepare a processor
-    processor = VideoBlipProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
-    tokenizer = LlamaTokenizer.from_pretrained(
-        "novelai/nerdstash-tokenizer-v1", additional_special_tokens=["▁▁"]
-    )
-    processor.tokenizer = tokenizer
-
     demo = build_demo()
     demo.queue(max_size=10).launch()

requirements.txt

@@ -1,8 +1 @@
-accelerate
-protobuf
-sentencepiece
-torch
-pillow
-transformers==4.33.1
-#accelerate==0.22.0
-deepspeed==0.10.2
+