search

gradio-app / gradio

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
http://www.gradio.app
Apache License 2.0
31.01k stars 2.31k forks source link

KeyError: 0 when using Tab #8709

Closed mohammednuruddin closed 1 week ago

mohammednuruddin commented 1 week ago

Describe the bug

I am building a chatbot with different tabs, one tab has a normal rag char and the other tab has a multimodal llm. When I run the multimodal llm alone without including the chatinterface for the rag, i dont get the error but when I add the ChatInterface for the rag bot, I get an error when trying to use the multimodal llm.

Have you searched existing issues? 🔎

Reproduction

#!/usr/bin/env python
# encoding: utf-8
device = "cuda"

ERROR_MSG = "Error, please retry"
model_name = 'MiniCPM-Llama3-V 2.5'

def predict_rag(message, history):
    history.append((message, ""))

    # Convert history to the required format for the conversational model
    history_langchain_format = []
    for human, ai in history:
        history_langchain_format.append(HumanMessage(content=human))
        if ai:
            history_langchain_format.append(AIMessage(content=ai))

    response = conversational_rag_chain.stream({"input": message},
                                               config={
        "configurable": {"session_id": session_id}
    },)

    partial_response = ""
    for chunk in response:
        if answer_chunk := chunk.get("answer"):
            partial_response += answer_chunk
            # say(answer_chunk)
            # print(f"{answer_chunk}", end="")
            yield partial_response

    # Update the last entry with the AI's response
    history[-1] = (message, response)

    return

def create_component(params, comp='Slider'):
    if comp == 'Slider':
        return gr.Slider(
            minimum=params['minimum'],
            maximum=params['maximum'],
            value=params['value'],
            step=params['step'],
            interactive=params['interactive'],
            label=params['label']
        )
    elif comp == 'Radio':
        return gr.Radio(
            choices=params['choices'],
            value=params['value'],
            interactive=params['interactive'],
            label=params['label']
        )
    elif comp == 'Button':
        return gr.Button(
            value=params['value'],
            interactive=True
        )

# @spaces.GPU(duration=120)
def chat(img, msgs, ctx, params=None, vision_hidden_states=None):
    default_params = {"stream": False, "sampling": False, "num_beams":3, "repetition_penalty": 1.2, "max_new_tokens": 1024}
    if params is None:
        params = default_params
    if img is None:
        yield "Error, invalid image, please upload a new image"
    else:
        try:
            image = img.convert('RGB')
            answer = model.chat(
                image=image,
                msgs=msgs,
                tokenizer=tokenizer,
                **params
            )
            for char in answer:
                yield char
        except Exception as err:
            print(err)
            traceback.print_exc()
            yield ERROR_MSG

def upload_img(image, _chatbot, _app_session):
    image = Image.fromarray(image)

    _app_session['sts']=None
    _app_session['ctx']=[]
    _app_session['img']=image 
    _chatbot.append(('', 'Image uploaded successfully, you can talk to me now'))
    return _chatbot, _app_session

def respond(_chat_bot, _app_cfg):
    _question = _chat_bot[-1][0]
    print(':', _question)
    if _app_cfg.get('ctx', None) is None:
        _chat_bot[-1][1] = 'Please upload an image to start'
        yield (_chat_bot, _app_cfg)
    else:
        _context = _app_cfg['ctx'].copy()
        if _context:
            _context.append({"role": "user", "content": _question})
        else:
            _context = [{"role": "user", "content": _question}]
        params = {
                'sampling': True,
                'stream': True,
                'top_p': 0.8,
                'top_k': 100,
                'temperature': 0.7,
                'repetition_penalty': 1.05,
                "max_new_tokens": 896 
            }

        gen = chat(_app_cfg['img'], _context, None, params)
        _chat_bot[-1][1] = ""
        for _char in gen:
            _chat_bot[-1][1] += _char
            _context[-1]["content"] += _char
            yield (_chat_bot, _app_cfg)

def request(_question, _chat_bot, _app_cfg):
    _chat_bot.append((_question, None))
    return '', _chat_bot, _app_cfg

def regenerate_button_clicked(_question, _chat_bot, _app_cfg):
    if len(_chat_bot) <= 1:
        _chat_bot.append(('Regenerate', 'No question for regeneration.'))
        return '', _chat_bot, _app_cfg
    elif _chat_bot[-1][0] == 'Regenerate':
        return '', _chat_bot, _app_cfg
    else:
        _question = _chat_bot[-1][0]
        _chat_bot = _chat_bot[:-1]
        _app_cfg['ctx'] = _app_cfg['ctx'][:-2]
    return request(_question, _chat_bot, _app_cfg)

def clear_button_clicked(_question, _chat_bot, _app_cfg, _bt_pic):
    _chat_bot.clear()
    _app_cfg['sts'] = None
    _app_cfg['ctx'] = None
    _app_cfg['img'] = None
    _bt_pic = None
    return '', _chat_bot, _app_cfg, _bt_pic

css = """
#chatbot {
    flex-grow: 2 !important;  /* Changed size by increasing flex-grow */
    overflow: auto !important;
}
#col { height: calc(90vh - 2px - 16px) !important; }
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("## Your NSMQ Assistant")
    # with gr.Tab("Prep", ):
    #     with gr.Row():
    #         with gr.Column(scale=1,elem_id="col"):
    #             chat = gr.ChatInterface(
    #                 fn=rag,
    #                 chatbot=gr.Chatbot(elem_id="chatbot",
    #                                 render=False),

    #             )

    with gr.Tab("Fast QA"):
        # pass
        with gr.Column(elem_id="col"):
            chat = gr.ChatInterface(predict_rag, fill_height=True)
            # chat = gr.chat_bot()
        # with gr.Column(elem_id='col'):
        #     image_input = gr.Image()
        #     image_output = gr.Image()
        # image_button = gr.Button("Flip")

    with gr.Tab("Chat with images"):
        with gr.Row():
            with gr.Column(scale=1, min_width=300):
                bt_pic = gr.Image(label="Upload an image to start")
                regenerate = create_component({'value': 'Regenerate'}, comp='Button')
                clear = create_component({'value': 'Clear'}, comp='Button')
            with gr.Column(scale=3, min_width=500):
                app_session = gr.State({'sts':None,'ctx':None,'img':None})
                chat_bot = gr.Chatbot(label=f"Chat with {model_name}")
                txt_message = gr.Textbox(label="Input text")

                clear.click(
                    clear_button_clicked,
                    [txt_message, chat_bot, app_session, bt_pic],
                    [txt_message, chat_bot, app_session, bt_pic],
                    queue=False
                )
                txt_message.submit(
                    request, 
                    [txt_message, chat_bot, app_session],
                    [txt_message, chat_bot, app_session],
                    queue=False
                ).then(
                    respond,
                    [chat_bot, app_session],
                    [chat_bot, app_session]
                )
                regenerate.click(
                    regenerate_button_clicked,
                    [txt_message, chat_bot, app_session],
                    [txt_message, chat_bot, app_session],
                    queue=False
                ).then(
                    respond,
                    [chat_bot, app_session],
                    [chat_bot, app_session]
                )
                bt_pic.upload(lambda: None, None, chat_bot, queue=False).then(upload_img, inputs=[bt_pic,chat_bot,app_session], outputs=[chat_bot,app_session])

# launch
# demo.launch(share=True, debug=True, show_api=False, server_port=8080)
# demo.queue()
demo.launch(share=True, debug=True,)

Screenshot

image

Logs

Traceback (most recent call last):
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/queueing.py", line 541, in process_events
    response = await route_utils.call_process_api(
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/route_utils.py", line 276, in call_process_api
    output = await app.get_blocks().process_api(
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/blocks.py", line 1928, in process_api
    result = await self.call_function(
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/blocks.py", line 1526, in call_function
    prediction = await utils.async_iteration(iterator)
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/utils.py", line 657, in async_iteration
    return await iterator.__anext__()
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/utils.py", line 650, in __anext__
    return await anyio.to_thread.run_sync(
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
    return await get_async_backend().run_sync_in_worker_thread(
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2177, in run_sync_in_worker_thread
    return await future
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 859, in run
    result = context.run(func, *args)
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/utils.py", line 633, in run_sync_iterator_async
    return next(iterator)
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/utils.py", line 816, in gen_wrapper
    response = next(iterator)
  File "/tmp/ipykernel_9635/3196440666.py", line 116, in respond
    gen = chat(_app_cfg['img'], _context, None, params)
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/blocks.py", line 1429, in __call__
    if not (self.is_callable(fn_index)):
  File "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/gradio/blocks.py", line 1394, in is_callable
    block_fn = self.fns[fn_index]
KeyError: 0

System Info

Gradio Environment Information:
------------------------------
Operating System: Windows
gradio version: 4.36.1
gradio_client version: 1.0.1

------------------------------------------------
gradio dependencies in your environment:

aiofiles: 23.2.1
altair: 5.2.0
fastapi: 0.110.0
ffmpy: 0.3.2
gradio-client==1.0.1 is not installed.
httpx: 0.27.0
huggingface-hub: 0.23.3
importlib-resources: 6.3.0
jinja2: 3.1.3
markupsafe: 2.1.5
matplotlib: 3.8.3
numpy: 1.26.4
orjson: 3.9.15
packaging: 23.2
pandas: 2.0.3
pillow: 10.2.0
pydantic: 2.6.3
pydub: 0.25.1
python-multipart: 0.0.9
pyyaml: 6.0.1
ruff: 0.3.2
semantic-version: 2.10.0
tomlkit==0.12.0 is not installed.
typer: 0.12.3
typing-extensions: 4.9.0
urllib3: 2.0.7
uvicorn: 0.28.0
authlib; extra == 'oauth' is not installed.
itsdangerous; extra == 'oauth' is not installed.

gradio_client dependencies in your environment:

fsspec: 2024.2.0
httpx: 0.27.0
huggingface-hub: 0.23.3
packaging: 23.2
typing-extensions: 4.9.0
websockets: 11.0.3

Severity

Blocking usage of gradio

abidlabs commented 1 week ago

Hi @mohammednuruddin can you please provide a minimal code example that we can use to reproduce the issue above? The current code example has quite a lot going on and includes external dependencies. See: https://stackoverflow.com/help/minimal-reproducible-example

abidlabs commented 1 week ago

Going to close this for now, but happy to reopen once we have a simpler, standalone repro!

mohammednuruddin commented 1 week ago

I solved it. Thanks.