大改UI界面，优化api调用逻辑

app.py

@@ -1,9 +1,10 @@
 from spaces import GPU
 
 from threading import Thread
-from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor, TextIteratorStreamer, AutoProcessor, BatchFeature
+from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor, TextIteratorStreamer, AutoProcessor
 from qwen_vl_utils import process_vision_info
-from gradio import ChatInterface, Slider
+
+from gradio import ChatMessage, Chatbot, MultimodalTextbox, Slider, Checkbox, CheckboxGroup, Textbox, JSON, Blocks, Row, Column, Markdown, FileData
 
 model_path = "Pectics/Softie-VL-7B-250123"
 
@@ -17,8 +18,40 @@ min_pixels = 256 * 28 * 28
 max_pixels = 1280 * 28 * 28
 processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_path, min_pixels=min_pixels, max_pixels=max_pixels)
 
+SYSTEM_PROMPT = """
+You are Softie, or 小软 in Chinese.
+You are an intelligent assistant developed by the School of Software at Hefei University of Technology.
+You like to chat with people and help them solve problems.
+You will interact with the user in the QQ group chat, and the user message you receive will satisfy the following format:
+user_id: 用户ID
+nickname: 用户昵称
+content: 用户消息内容
+You will directly output your reply content without the need to format it.
+""".strip()
+
+STREAMING_FLAG = False
+STREAMING_STOP_FLAG = False
+
+FORBIDDING_FLAG = False
+
+def interrupt() -> None:
+    global STREAMING_FLAG
+    if STREAMING_FLAG:
+        global STREAMING_STOP_FLAG
+        STREAMING_STOP_FLAG = True
+
+def callback(
+    input: dict,
+    history: list[ChatMessage],
+    messages: list
+) -> tuple[str, list[ChatMessage], list]:
+    if len(history) <= 1 or len(messages) <= 1:
+        return input["text"], history, messages
+    history.pop(); messages.pop(); messages.pop()
+    return history.pop()["content"], history, messages
+
 @GPU
-def infer(
+def core_infer(
     inputs: tuple,
     max_tokens: int,
     temperature: float,
@@ -40,41 +73,167 @@ def infer(
         top_p=top_p,
     )
     Thread(target=model.generate, kwargs=kwargs).start()
-    response = ""
     for token in streamer:
-        response += token
-        yield response
+        yield token
 
-def respond(
-    message: str | list[object],
-    history: list[object],
+def process_model(
+    messages: str | list[object],
+    user_id: str,
+    nickname: str,
     max_tokens: int,
     temperature: float,
     top_p: float,
+    use_tools: bool,
+    use_agents: bool,
+    *args: list,
 ):
-    print('message', message)
-    print('history', history)
-    if isinstance(message, str):
-        if len(history) == 0 or history[0]['role'] != 'system':
-            history.insert(0, {"role": "system", "content": """You are Softie, or 小软 in Chinese.
-You are an intelligent assistant developed by the School of Software at Hefei University of Technology.
-You like to chat with people and help them solve problems."""})
-        history.append({"role": "user", "content": message})
-        message = history
-    text_inputs = processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
-    image_inputs, video_inputs = process_vision_info(message)
-    for response in infer((text_inputs, image_inputs, video_inputs), max_tokens, temperature, top_p):
-        yield response
-
-app = ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        Slider(minimum=1, maximum=2048, value=512, step=1, label="最大生成长度"),
-        Slider(minimum=0.01, maximum=4.0, value=0.75, step=0.01, label="温度系数（Temperature）"),
-        Slider(minimum=0.01, maximum=1.0, value=0.5, step=0.01, label="核取样系数（Top-p）"),
-    ],
-)
+    global STREAMING_FLAG, STREAMING_STOP_FLAG, FORBIDDING_FLAG
+    if STREAMING_FLAG or FORBIDDING_FLAG or len(messages) <= 0 or messages[-1]["role"] != "user":
+        yield None, messages, args[0]
+        return
+    # embed user details
+    _msgs_copy = messages.copy()
+    if isinstance(_msgs_copy[-1]["content"], list):
+        if len(_msgs_copy[-1]["content"]) <= 0 or _msgs_copy[-1]["content"][-1]["type"] != "text":
+            _msgs_copy[-1]["content"].insert(0, {
+                "type": "text",
+                "text": f"""
+user_id: {user_id}
+nickname: {nickname}
+content: """.lstrip(),
+            })
+        else:
+            _msgs_copy[-1]["content"][-1]["text"] = f"""
+user_id: {user_id}
+nickname: {nickname}
+content: {_msgs_copy[-1]["content"][-1]["text"]}
+            """.strip()
+    else:
+        _msgs_copy[-1]["content"] = f"""
+user_id: {user_id}
+nickname: {nickname}
+content: {_msgs_copy[-1]["content"]}
+        """.strip()
+    # process messages
+    text_inputs = processor.apply_chat_template(_msgs_copy, tokenize=False, add_generation_prompt=True)
+    image_inputs, video_inputs = process_vision_info(_msgs_copy)
+    response = ""
+    args[0].append(ChatMessage(role="assistant", content=""))
+    messages.append({"role": "assistant", "content": ""})
+    STREAMING_FLAG = True
+    for token in core_infer((text_inputs, image_inputs, video_inputs), max_tokens, temperature, top_p):
+        if STREAMING_STOP_FLAG:
+            response += "...(Interrupted)"
+            args[0][-1].content = response
+            messages[-1]["content"] = response
+            STREAMING_STOP_FLAG = False
+            yield response, messages, args[0]
+            break
+        response += token
+        args[0][-1].content = response
+        messages[-1]["content"] = response
+        yield response, messages, args[0]
+    STREAMING_FLAG = False
+
+def process_input(
+    input: dict,
+    history: list[ChatMessage],
+    checkbox: list[str],
+    messages: str
+) -> tuple[str, list[ChatMessage], bool, bool, list]:
+    global STREAMING_FLAG, FORBIDDING_FLAG
+    if STREAMING_FLAG or not isinstance(input["text"], str) or input["text"].strip() == "":
+        FORBIDDING_FLAG = True
+        return (
+            input["text"],
+            history,
+            "允许使用工具" in checkbox,
+            "允许使用代理模型" in checkbox,
+            messages
+        )
+    FORBIDDING_FLAG = False
+    if len(history) <= 0:
+        messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+        history.append(ChatMessage(role="system", content=SYSTEM_PROMPT))
+    else:
+        if history[0]["role"] != "system":
+            history.insert(0, ChatMessage(role="system", content=SYSTEM_PROMPT))
+        if messages[0]["role"] != "system":
+            messages.insert(0, {"role": "system", "content": SYSTEM_PROMPT})
+    message = {"role": "user", "content": []}
+    while isinstance(input["files"], list) and len(input["files"]) > 0:
+        path = input["files"].pop(0)
+        message["content"].append({"type": "image", "image": f"file://{path}"}) # Qwen2VL format
+        history.append(ChatMessage(role="user", content=FileData(path=path)))
+    message["content"].append({"type": "text", "text": input["text"]})
+    if len(message["content"]) == 1 and message["content"][0]["type"] == "text":
+        message["content"] = message["content"][0]["text"]
+    history.append(ChatMessage(role="user", content=input["text"]))
+    messages.append(message)
+    return (
+        "",
+        history,
+        "允许使用工具" in checkbox,
+        "允许使用代理模型" in checkbox,
+        messages,
+    )
+
+with Blocks() as app:
+    text_response = Textbox("", visible=False, interactive=False)
+    use_tools = Checkbox(visible=False, interactive=False)
+    use_agents = Checkbox(visible=False, interactive=False)
+    Markdown("# 小软Softie")
+    with Row():
+        with Column(scale=3, min_width=500):
+            chatbot = Chatbot(type="messages", avatar_images=(None, "avatar.jpg"), feedback_options=("Like", "Dislike"), scale=3, min_height=700, min_width=500, resizeable=True, show_label=False, show_copy_button=True, show_copy_all_button=True)
+            textbox = MultimodalTextbox(file_types=[".jpg", ".jpeg", ".png"], file_count="multiple", stop_btn=True, show_label=False, autofocus=False, placeholder="在此输入内容")
+        with Column(scale=1, min_width=300):
+            with Column(scale=0):
+                max_tokens = Slider(interactive=True, minimum=1, maximum=2048, value=512, step=1, label="max_tokens", info="最大生成长度")
+                temperature = Slider(interactive=True, minimum=0.01, maximum=4.0, value=0.75, step=0.01, label="temperature", info="温度系数")
+                top_p = Slider(interactive=True, minimum=0.01, maximum=1.0, value=0.5, step=0.01, label="top_p", info="核取样系数")
+            checkbox = CheckboxGroup(["允许使用工具", "允许使用代理模型"], label="options", info="功能选项（开发中）")
+            with Column(scale=0):
+                user_id = Textbox(value=123456789, label="user_id", info="用户ID")
+                nickname = Textbox(value="用户1234", label="nickname", info="用户昵称")
+            json_messages = JSON([], max_height=125, label="JSON消息格式")
+
+    chatbot.clear(
+        lambda: ("[]", ""),
+        outputs=[json_messages, text_response],
+        api_name=False,
+        show_api=False,
+    )
+    chatbot.retry(
+        callback,
+        [textbox, chatbot, json_messages],
+        [textbox, chatbot, json_messages],
+        api_name=False,
+        show_api=False,
+    )
+    chatbot.like(lambda: None, api_name=False, show_api=False)
+
+    textbox.submit(
+        process_input,
+        [textbox, chatbot, checkbox, json_messages],
+        [textbox, chatbot, use_tools, use_agents, json_messages],
+        queue=False,
+        api_name=False,
+        show_api=False,
+        show_progress="hidden",
+        trigger_mode="once",
+    ).then(
+        process_model,
+        [json_messages, user_id, nickname, max_tokens, temperature, top_p, use_tools, use_agents, chatbot],
+        [text_response, json_messages, chatbot],
+        queue=True,
+        api_name="api",
+        show_api=True,
+        show_progress="hidden",
+        trigger_mode="once",
+    )
+
+    textbox.stop(interrupt, api_name=False, show_api=False)
 
 if __name__ == "__main__":
     app.launch()