initial commit

find the official demo here : https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/

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README.md

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 ---
-title: VLChat OfficialDemo
-emoji: 📚
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 4.7.1
-app_file: app.py
-pinned: false
-license: apache-2.0
+domain:
+- multi-modal
+models:
+- qwen/Qwen-VL-Chat
+deployspec:
+  cpu: 11
+  gpu: 1
+  instance: 1
+  instance_type: ecs.gn6e-c12g1.3xlarge
+  memory: 80000
+license: other
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+#### Clone with HTTP
+```bash
+ git clone https://www.modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo.git
+```

app.py

@@ -0,0 +1,247 @@
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+os.system('pip install tiktoken')
+os.system('pip install "modelscope" --upgrade -f https://pypi.org/project/modelscope/')
+os.system('pip install transformers_stream_generator')
+
+# os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
+from argparse import ArgumentParser
+from pathlib import Path
+
+import copy
+import gradio as gr
+import os
+import re
+import secrets
+import tempfile
+from modelscope import (
+    AutoModelForCausalLM, AutoTokenizer, GenerationConfig, snapshot_download
+)
+
+DEFAULT_CKPT_PATH = 'qwen/Qwen-VL-Chat'
+REVISION = 'v1.0.4'
+BOX_TAG_PATTERN = r"<box>([\s\S]*?)</box>"
+PUNCTUATION = "！？。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏."
+
+
+def _get_args():
+    parser = ArgumentParser()
+    parser.add_argument("-c", "--checkpoint-path", type=str, default=DEFAULT_CKPT_PATH,
+                        help="Checkpoint name or path, default to %(default)r")
+    parser.add_argument("--revision", type=str, default=REVISION)
+    parser.add_argument("--cpu-only", action="store_true", help="Run demo with CPU only")
+
+    parser.add_argument("--share", action="store_true", default=False,
+                        help="Create a publicly shareable link for the interface.")
+    parser.add_argument("--inbrowser", action="store_true", default=False,
+                        help="Automatically launch the interface in a new tab on the default browser.")
+    parser.add_argument("--server-port", type=int, default=8000,
+                        help="Demo server port.")
+    parser.add_argument("--server-name", type=str, default="127.0.0.1",
+                        help="Demo server name.")
+
+    args = parser.parse_args()
+    return args
+
+
+def _load_model_tokenizer(args):
+    model_id = args.checkpoint_path
+    model_dir = snapshot_download(model_id, revision=args.revision)
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_dir, trust_remote_code=True, resume_download=True,
+    )
+
+    if args.cpu_only:
+        device_map = "cpu"
+    else:
+        device_map = "auto"
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_dir,
+        device_map=device_map,
+        trust_remote_code=True,
+        resume_download=True,
+    ).eval()
+    model.generation_config = GenerationConfig.from_pretrained(
+        model_dir, trust_remote_code=True, resume_download=True,
+    )
+
+    return model, tokenizer
+
+
+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("`", r"\`")
+                    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
+
+
+def _launch_demo(args, model, tokenizer):
+    uploaded_file_dir = os.environ.get("GRADIO_TEMP_DIR") or str(
+        Path(tempfile.gettempdir()) / "gradio"
+    )
+
+    def predict(_chatbot, task_history):
+        chat_query = _chatbot[-1][0]
+        query = task_history[-1][0]
+        print("User: " + _parse_text(query))
+        history_cp = copy.deepcopy(task_history)
+        full_response = ""
+
+        history_filter = []
+        pic_idx = 1
+        pre = ""
+        for i, (q, a) in enumerate(history_cp):
+            if isinstance(q, (tuple, list)):
+                q = f'Picture {pic_idx}: <img>{q[0]}</img>'
+                pre += q + '\n'
+                pic_idx += 1
+            else:
+                pre += q
+                history_filter.append((pre, a))
+                pre = ""
+        history, message = history_filter[:-1], history_filter[-1][0]
+        response, history = model.chat(tokenizer, message, history=history)
+        image = tokenizer.draw_bbox_on_latest_picture(response, history)
+        if image is not None:
+            temp_dir = secrets.token_hex(20)
+            temp_dir = Path(uploaded_file_dir) / temp_dir
+            temp_dir.mkdir(exist_ok=True, parents=True)
+            name = f"tmp{secrets.token_hex(5)}.jpg"
+            filename = temp_dir / name
+            image.save(str(filename))
+            _chatbot[-1] = (_parse_text(chat_query), (str(filename),))
+            chat_response = response.replace("<ref>", "")
+            chat_response = chat_response.replace(r"</ref>", "")
+            chat_response = re.sub(BOX_TAG_PATTERN, "", chat_response)
+            if chat_response != "":
+                _chatbot.append((None, chat_response))
+        else:
+            _chatbot[-1] = (_parse_text(chat_query), response)
+        full_response = _parse_text(response)
+
+        task_history[-1] = (query, full_response)
+        print("Qwen-VL-Chat: " + _parse_text(full_response))
+        task_history = task_history[-10:]
+        return _chatbot
+
+    def regenerate(_chatbot, task_history):
+        if not task_history:
+            return _chatbot
+        item = task_history[-1]
+        if item[1] is None:
+            return _chatbot
+        task_history[-1] = (item[0], None)
+        chatbot_item = _chatbot.pop(-1)
+        if chatbot_item[0] is None:
+            _chatbot[-1] = (_chatbot[-1][0], None)
+        else:
+            _chatbot.append((chatbot_item[0], None))
+        return predict(_chatbot, task_history)
+
+    def add_text(history, task_history, text):
+        task_text = text
+        if len(text) >= 2 and text[-1] in PUNCTUATION and text[-2] not in PUNCTUATION:
+            task_text = text[:-1]
+        history = history + [(_parse_text(text), None)]
+        task_history = task_history + [(task_text, None)]
+        return history, task_history, ""
+
+    def add_file(history, task_history, file):
+        history = history + [((file.name,), None)]
+        task_history = task_history + [((file.name,), None)]
+        return history, task_history
+
+    def reset_user_input():
+        return gr.update(value="")
+
+    def reset_state(task_history):
+        task_history.clear()
+        return []
+
+    with gr.Blocks() as demo:
+        gr.Markdown("""\
+<p align="center"><img src="https://modelscope.cn/api/v1/models/qwen/Qwen-VL-Chat/repo?Revision=master&FilePath=assets/logo.jpg&View=true" style="height: 80px"/><p>""")
+        gr.Markdown("""<center><font size=8>Qwen-VL-Chat Bot</center>""")
+        gr.Markdown(
+            """\
+<center><font size=3>This WebUI is based on Qwen-VL-Chat, developed by Alibaba Cloud. \
+(本WebUI基于Qwen-VL-Chat打造，实现聊天机器人功能。)</center>""")
+        gr.Markdown("""\
+<center><font size=4>Qwen-VL <a href="https://modelscope.cn/models/qwen/Qwen-VL/summary">🤖 </a> 
+| <a href="https://huggingface.co/Qwen/Qwen-VL">🤗</a>&nbsp ｜ 
+Qwen-VL-Chat <a href="https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary">🤖 </a> | 
+<a href="https://huggingface.co/Qwen/Qwen-VL-Chat">🤗</a>&nbsp ｜ 
+&nbsp<a href="https://github.com/QwenLM/Qwen-VL">Github</a></center>""")
+
+        chatbot = gr.Chatbot(label='Qwen-VL-Chat', elem_classes="control-height").style(height=500)
+        query = gr.Textbox(lines=2, label='Input')
+        task_history = gr.State([])
+
+        with gr.Row():
+            addfile_btn = gr.UploadButton("📁 Upload (上传文件)", file_types=["image"])
+            submit_btn = gr.Button("🚀 Submit (发送)")
+            regen_btn = gr.Button("🤔️ Regenerate (重试)")
+            empty_bin = gr.Button("🧹 Clear History (清除历史)")
+
+        submit_btn.click(add_text, [chatbot, task_history, query], [chatbot, task_history]).then(
+            predict, [chatbot, task_history], [chatbot], show_progress=True
+        )
+        submit_btn.click(reset_user_input, [], [query])
+        empty_bin.click(reset_state, [task_history], [chatbot], show_progress=True)
+        regen_btn.click(regenerate, [chatbot, task_history], [chatbot], show_progress=True)
+        addfile_btn.upload(add_file, [chatbot, task_history, addfile_btn], [chatbot, task_history], show_progress=True)
+
+        gr.Markdown("""\
+<font size=2>Note: This demo is governed by the original license of Qwen-VL. \
+We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content, \
+including hate speech, violence, pornography, deception, etc. \
+(注：本演示受Qwen-VL的许可协议限制。我们强烈建议，用户不应传播及不应允许他人传播以下内容，\
+包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息。)""")
+
+    demo.queue().launch(
+        share=args.share,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+        server_name=args.server_name,
+    )
+
+
+def main():
+    args = _get_args()
+
+    model, tokenizer = _load_model_tokenizer(args)
+
+    _launch_demo(args, model, tokenizer)
+
+
+if __name__ == '__main__':
+    main()