Update app.py

app.py

@@ -1,8 +1,9 @@
 import os, copy
+os.environ["RWKV_V7_ON"] = '1'
 os.environ["RWKV_JIT_ON"] = '1'
 os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
-# make sure cuda dir is in the same level as modeling_rwkv.py
-from modeling_rwkv import RWKV
+
+from rwkv.model import RWKV
 
 import gc, re
 import gradio as gr
@@ -18,53 +19,22 @@ nvmlInit()
 gpu_h = nvmlDeviceGetHandleByIndex(0)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-ctx_limit = 2500
-gen_limit = 500
-gen_limit_long = 800
-ENABLE_VISUAL = False
+ctx_limit = 4096
+gen_limit = 1000
 
 ########################## text rwkv ################################################################
 from rwkv.utils import PIPELINE, PIPELINE_ARGS
 
-title_v6 = "RWKV-x060-World-3B-v2.1-20240417-ctx4096"
-model_path_v6 = hf_hub_download(repo_id="BlinkDL/rwkv-6-world", filename=f"{title_v6}.pth")
-# model_path_v6 = '/mnt/e/RWKV-Runner/models/rwkv-final-v6-2.1-3b' # conda activate torch2; cd /mnt/program/_RWKV_/_ref_/_gradio_/RWKV-Gradio-1; python app.py
+title_v6 = "RWKV-x070-World-0.1B-v2.8-20241210-ctx4096"
+model_path_v6 = hf_hub_download(repo_id="BlinkDL/rwkv-7-world", filename=f"{title_v6}.pth")
+# model_path_v6 = f'/mnt/e/RWKV-Runner/models/{title_v6}' # conda activate torch2; cd /mnt/program/git-public/RWKV-Gradio-1; python app.py
 model_v6 = RWKV(model=model_path_v6, strategy='cuda fp16')
 pipeline_v6 = PIPELINE(model_v6, "rwkv_vocab_v20230424")
 
 args = model_v6.args
-eng_name = 'rwkv-x060-eng_single_round_qa-3B-20240516-ctx2048'
-chn_name = 'rwkv-x060-chn_single_round_qa-3B-20240516-ctx2048'
-
-# state_eng_raw = torch.load(f'/mnt/e/RWKV-Runner/models/{eng_name}.pth', map_location=torch.device('cpu'))
-# state_chn_raw = torch.load(f'/mnt/e/RWKV-Runner/models/{chn_name}.pth', map_location=torch.device('cpu'))
-
-eng_file = hf_hub_download(repo_id="BlinkDL/temp-latest-training-models", filename=f"{eng_name}.pth")
-chn_file = hf_hub_download(repo_id="BlinkDL/temp-latest-training-models", filename=f"{chn_name}.pth")
-state_eng_raw = torch.load(eng_file, map_location=torch.device('cpu'))
-state_chn_raw = torch.load(chn_file, map_location=torch.device('cpu'))
-
-state_eng = [None] * args.n_layer * 3
-state_chn = [None] * args.n_layer * 3
-for i in range(args.n_layer):
-    dd = model_v6.strategy[i]
-    dev = dd.device
-    atype = dd.atype    
-    state_eng[i*3+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
-    state_chn[i*3+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
-    state_eng[i*3+1] = state_eng_raw[f'blocks.{i}.att.time_state'].transpose(1,2).to(dtype=torch.float, device=dev).requires_grad_(False).contiguous()
-    state_chn[i*3+1] = state_chn_raw[f'blocks.{i}.att.time_state'].transpose(1,2).to(dtype=torch.float, device=dev).requires_grad_(False).contiguous()
-    state_eng[i*3+2] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
-    state_chn[i*3+2] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
 
 penalty_decay = 0.996
 
-if ENABLE_VISUAL:
-    title = "RWKV-5-World-1B5-v2-20231025-ctx4096"
-    model_path = hf_hub_download(repo_id="BlinkDL/rwkv-5-world", filename=f"{title}.pth")
-    model = RWKV(model=model_path, strategy='cuda fp16')
-    pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
-
 def generate_prompt(instruction, input=""):
     instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
     input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
@@ -98,122 +68,7 @@ def evaluate(
     occurrence = {}
     state = None
     for i in range(int(token_count)):
-        input_ids = pipeline_v6.encode(ctx)[-ctx_limit:] if i == 0 else [token]
-        out, state = model_v6.forward(tokens=input_ids, state=state)
-        for n in occurrence:
-            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
-
-        token = pipeline_v6.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
-        if token in args.token_stop:
-            break
-        all_tokens += [token]
-        for xxx in occurrence:
-            occurrence[xxx] *= penalty_decay
-            
-        ttt = pipeline_v6.decode([token])
-        www = 1
-        if ttt in ' \t0123456789':
-            www = 0
-        #elif ttt in '\r\n,.;?!"\':+-*/=#@$%^&_`~|<>\\()[]{}，。；“”：？！（）【】':
-        #    www = 0.5
-        if token not in occurrence:
-            occurrence[token] = www
-        else:
-            occurrence[token] += www
-            
-        tmp = pipeline_v6.decode(all_tokens[out_last:])
-        if '\ufffd' not in tmp:
-            out_str += tmp
-            yield out_str.strip()
-            out_last = i + 1
-
-    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
-    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
-    del out
-    del state
-    gc.collect()
-    torch.cuda.empty_cache()
-    yield out_str.strip()
 
-def evaluate_eng(
-    ctx,
-    token_count=200,
-    temperature=1.0,
-    top_p=0.7,
-    presencePenalty = 0.1,
-    countPenalty = 0.1,
-):
-    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
-                     alpha_frequency = countPenalty,
-                     alpha_presence = presencePenalty,
-                     token_ban = [], # ban the generation of some tokens
-                     token_stop = [0]) # stop generation whenever you see any token here
-    ctx = qa_prompt(ctx)
-    all_tokens = []
-    out_last = 0
-    out_str = ''
-    occurrence = {}
-    state = copy.deepcopy(state_eng)
-    for i in range(int(token_count)):
-        input_ids = pipeline_v6.encode(ctx)[-ctx_limit:] if i == 0 else [token]
-        out, state = model_v6.forward(tokens=input_ids, state=state)
-        for n in occurrence:
-            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
-
-        token = pipeline_v6.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
-        if token in args.token_stop:
-            break
-        all_tokens += [token]
-        for xxx in occurrence:
-            occurrence[xxx] *= penalty_decay
-            
-        ttt = pipeline_v6.decode([token])
-        www = 1
-        if ttt in ' \t0123456789':
-            www = 0
-        #elif ttt in '\r\n,.;?!"\':+-*/=#@$%^&_`~|<>\\()[]{}，。；“”：？！（）【】':
-        #    www = 0.5
-        if token not in occurrence:
-            occurrence[token] = www
-        else:
-            occurrence[token] += www
-            
-        tmp = pipeline_v6.decode(all_tokens[out_last:])
-        if '\ufffd' not in tmp:
-            out_str += tmp
-            yield out_str.strip()
-            out_last = i + 1
-
-    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
-    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
-    del out
-    del state
-    gc.collect()
-    torch.cuda.empty_cache()
-    yield out_str.strip()
-
-def evaluate_chn(
-    ctx,
-    token_count=200,
-    temperature=1.0,
-    top_p=0.7,
-    presencePenalty = 0.1,
-    countPenalty = 0.1,
-):
-    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
-                     alpha_frequency = countPenalty,
-                     alpha_presence = presencePenalty,
-                     token_ban = [], # ban the generation of some tokens
-                     token_stop = [0]) # stop generation whenever you see any token here
-    ctx = qa_prompt(ctx)
-    all_tokens = []
-    out_last = 0
-    out_str = ''
-    occurrence = {}
-    state = copy.deepcopy(state_chn)
-    for i in range(int(token_count)):
         input_ids = pipeline_v6.encode(ctx)[-ctx_limit:] if i == 0 else [token]
         out, state = model_v6.forward(tokens=input_ids, state=state)
         for n in occurrence:
@@ -267,167 +122,12 @@ examples = [
     ['''“当然可以，大宇宙不会因为这五公斤就不坍缩了。”关一帆说，他还有一个没说出来的想法：也许大宇宙真的会因为相差一个原子的质量而由封闭转为开放。大自然的精巧有时超出想象，比如生命的诞生，就需要各项宇宙参数在几亿亿分之一精度上的精确配合。但程心仍然可以留下她的生态球，因为在那无数文明创造的无数小宇宙中，肯定有相当一部分不响应回归运动的号召，所以，大宇宙最终被夺走的质量至少有几亿吨，甚至可能是几亿亿亿吨。\n但愿大宇宙能够忽略这个误差。\n程心和关一帆进入了飞船，智子最后也进来了。她早就不再穿那身华丽的和服了，她现在身着迷彩服，再次成为一名轻捷精悍的战士，她的身上佩带着许多武器和生存装备，最引人注目的是那把插在背后的武士刀。\n“放心，我在，你们就在！”智子对两位人类朋友说。\n聚变发动机启动了，推进器发出幽幽的蓝光，''', gen_limit, 1, 0.3, 0.5, 0.5],
 ]
 
-examples_eng = [
-    ["How can I craft an engaging story featuring vampires on Mars?", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["Compare the business models of Apple and Google.", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["In JSON format, list the top 5 tourist attractions in Paris.", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["Write an outline for a fantasy novel where dreams can alter reality.", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["Can fish get thirsty?", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["Write a Bash script to check disk usage and send alerts if it's too high.", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["Write a simple webpage. When a user clicks the button, it shows a random joke from a list of 4 jokes.", gen_limit_long, 1, 0.2, 0.3, 0.3],
-]
-
-examples_chn = [
-    ["怎样写一个在火星上的吸血鬼的有趣故事？", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["比较苹果和谷歌的商业模式。", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["鱼会口渴吗？", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["以 JSON 格式列举北京的美食。", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["编写一个Bash脚本来检查磁盘使用情况，如果使用量过高则发送警报。", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["用HTML编写一个简单的网站。当用户点击按钮时，从4个笑话的列表中随机显示一个笑话。", gen_limit_long, 1, 0.2, 0.3, 0.3],
-]
-
-if ENABLE_VISUAL:
-    ########################## visual rwkv ################################################################
-    visual_title = 'ViusualRWKV-v5'
-    rwkv_remote_path = "rwkv1b5-vitl336p14-577token_mix665k_rwkv.pth"
-    vision_remote_path = "rwkv1b5-vitl336p14-577token_mix665k_visual.pth"
-    vision_tower_name = 'openai/clip-vit-large-patch14-336'
-
-    model_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=rwkv_remote_path)
-    visual_rwkv = RWKV(model=model_path, strategy='cuda fp16')
-
-    ##########################################################################
-    from modeling_vision import VisionEncoder, VisionEncoderConfig
-    config = VisionEncoderConfig(n_embd=model.args.n_embd, 
-                                vision_tower_name=vision_tower_name, 
-                                grid_size=-1)
-    visual_encoder = VisionEncoder(config)
-    vision_local_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=vision_remote_path)
-    vision_state_dict = torch.load(vision_local_path, map_location='cpu')
-    visual_encoder.load_state_dict(vision_state_dict)
-    image_processor = CLIPImageProcessor.from_pretrained(vision_tower_name)
-    visual_encoder = visual_encoder.to(device)
-    ##########################################################################
-    def visual_generate_prompt(instruction):
-        instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
-        return f"\n{instruction}\n\nAssistant:"
-
-    def generate(
-        ctx,
-        image_state,
-        token_count=200,
-        temperature=1.0,
-        top_p=0.1,
-        presencePenalty = 0.0,
-        countPenalty = 1.0,
-    ):
-        args = PIPELINE_ARGS(temperature = 1.0, top_p = 0.1,
-                        alpha_frequency = 1.0,
-                        alpha_presence = 0.0,
-                        token_ban = [], # ban the generation of some tokens
-                        token_stop = [0, 261]) # stop generation whenever you see any token here
-        ctx = ctx.strip()
-        all_tokens = []
-        out_last = 0
-        out_str = ''
-        occurrence = {}
-        for i in range(int(token_count)):
-            if i == 0:
-                input_ids = pipeline.encode(ctx)[-ctx_limit:]
-                out, state = visual_rwkv.forward(tokens=input_ids, state=image_state)
-            else:
-                input_ids = [token]
-                out, state = visual_rwkv.forward(tokens=input_ids, state=state)
-            for n in occurrence:
-                out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
-
-            token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
-            if token in args.token_stop:
-                break
-            all_tokens += [token]
-            for xxx in occurrence:
-                occurrence[xxx] *= 0.994        
-            if token not in occurrence:
-                occurrence[token] = 1
-            else:
-                occurrence[token] += 1
-            
-            tmp = pipeline.decode(all_tokens[out_last:])
-            if '\ufffd' not in tmp:
-                out_str += tmp
-                yield out_str.strip()
-                out_last = i + 1
-
-        gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
-        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
-        del out
-        del state
-        gc.collect()
-        torch.cuda.empty_cache()
-        yield out_str.strip()
-
-
-    ##########################################################################
-    cur_dir = os.path.dirname(os.path.abspath(__file__))
-    visual_examples = [
-        [
-            f"{cur_dir}/examples_pizza.jpg",
-            "What are steps to cook it?"
-        ],
-        [
-            f"{cur_dir}/examples_bluejay.jpg",
-            "what is the name of this bird?",
-        ],
-        [
-            f"{cur_dir}/examples_woman_and_dog.png",
-            "describe this image",
-        ],
-    ]
-
-
-    def pil_image_to_base64(pil_image):
-        buffered = BytesIO()
-        pil_image.save(buffered, format="JPEG")  # You can change the format as needed (JPEG, PNG, etc.)
-        # Encodes the image data into base64 format as a bytes object
-        base64_image = base64.b64encode(buffered.getvalue()).decode('utf-8')
-        return base64_image
-
-    image_cache = {}
-    ln0_weight = model.w['blocks.0.ln0.weight'].to(torch.float32).to(device)
-    ln0_bias = model.w['blocks.0.ln0.bias'].to(torch.float32).to(device)
-    def compute_image_state(image):
-        base64_image = pil_image_to_base64(image)
-        if base64_image in image_cache:
-            image_state = image_cache[base64_image]
-        else:
-            image = image_processor(images=image.convert('RGB'), return_tensors='pt')['pixel_values'].to(device)
-            image_features = visual_encoder.encode_images(image.unsqueeze(0)).squeeze(0) # [L, D]
-            # apply layer norm to image feature, very important
-            image_features = F.layer_norm(image_features, 
-                                        (image_features.shape[-1],), 
-                                        weight=ln0_weight, 
-                                        bias=ln0_bias)
-            _, image_state = model.forward(embs=image_features, state=None)
-            image_cache[base64_image] = image_state
-        return image_state
-
-    def chatbot(image, question):
-        if image is None:
-            yield "Please upload an image."
-            return
-        image_state = compute_image_state(image)
-        input_text = visual_generate_prompt(question)
-        for output in generate(input_text, image_state):
-            yield output
-
-
 ##################################################################################################################
 with gr.Blocks(title=title_v6) as demo:
     gr.HTML(f"<div style=\"text-align: center;\">\n<h1>{title_v6}</h1>\n</div>")
 
     with gr.Tab("=== Base Model (Raw Generation) ==="):
-        gr.Markdown(f"This is [RWKV-6 World v2](https://huggingface.co/BlinkDL/rwkv-6-world) - a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM). Supports 100+ world languages and code. Check [300+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). *** Can try examples (bottom of page) *** (can edit them). Demo limited to ctxlen {ctx_limit}.")
+        gr.Markdown(f"This is [RWKV-7 World v2.8](https://huggingface.co/BlinkDL/rwkv-7-world) 0.1B (L12-D768) - a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM). Supports 100+ world languages and code. Check [400+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). *** Can try examples (bottom of page) *** (can edit them). Demo limited to ctxlen {ctx_limit}.")
         with gr.Row():
             with gr.Column():
                 prompt = gr.Textbox(lines=2, label="Prompt", value="Assistant: How can we craft an engaging story featuring vampires on Mars? Let's think step by step and provide an expert response.")
@@ -446,63 +146,5 @@ with gr.Blocks(title=title_v6) as demo:
         clear.click(lambda: None, [], [output])
         data.click(lambda x: x, [data], [prompt, token_count, temperature, top_p, presence_penalty, count_penalty])
 
-    with gr.Tab("=== English Q/A ==="):
-        gr.Markdown(f"This is [RWKV-6](https://huggingface.co/BlinkDL/rwkv-6-world) state-tuned to [English Q/A](https://huggingface.co/BlinkDL/temp-latest-training-models/blob/main/{eng_name}.pth). RWKV is a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM), and we have [300+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). Demo limited to ctxlen {ctx_limit}.")
-        with gr.Row():
-            with gr.Column():
-                prompt = gr.Textbox(lines=2, label="Prompt", value="How can I craft an engaging story featuring vampires on Mars?")
-                token_count = gr.Slider(10, gen_limit_long, label="Max Tokens", step=10, value=gen_limit_long)
-                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.0)
-                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.2)
-                presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.3)
-                count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.3)
-            with gr.Column():
-                with gr.Row():
-                    submit = gr.Button("Submit", variant="primary")
-                    clear = gr.Button("Clear", variant="secondary")
-                output = gr.Textbox(label="Output", lines=30)
-        data = gr.Dataset(components=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples_eng, samples_per_page=50, label="Examples", headers=["Prompt", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
-        submit.click(evaluate_eng, [prompt, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
-        clear.click(lambda: None, [], [output])
-        data.click(lambda x: x, [data], [prompt, token_count, temperature, top_p, presence_penalty, count_penalty])
-
-    with gr.Tab("=== Chinese Q/A ==="):
-        gr.Markdown(f"This is [RWKV-6](https://huggingface.co/BlinkDL/rwkv-6-world) state-tuned to [Chinese Q/A](https://huggingface.co/BlinkDL/temp-latest-training-models/blob/main/{chn_name}.pth). RWKV is a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM), and we have [300+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). Demo limited to ctxlen {ctx_limit}.")
-        with gr.Row():
-            with gr.Column():
-                prompt = gr.Textbox(lines=2, label="Prompt", value="怎样写一个在火星上的吸血鬼的有趣故事？")
-                token_count = gr.Slider(10, gen_limit_long, label="Max Tokens", step=10, value=gen_limit_long)
-                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.0)
-                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.2)
-                presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.3)
-                count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.3)
-            with gr.Column():
-                with gr.Row():
-                    submit = gr.Button("Submit", variant="primary")
-                    clear = gr.Button("Clear", variant="secondary")
-                output = gr.Textbox(label="Output", lines=30)
-        data = gr.Dataset(components=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples_chn, samples_per_page=50, label="Examples", headers=["Prompt", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
-        submit.click(evaluate_chn, [prompt, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
-        clear.click(lambda: None, [], [output])
-        data.click(lambda x: x, [data], [prompt, token_count, temperature, top_p, presence_penalty, count_penalty])        
-
-    if ENABLE_VISUAL:
-        with gr.Tab("Visual RWKV-5 1.5B"):
-            with gr.Row():
-                with gr.Column():
-                    image = gr.Image(type='pil', label="Image")
-                with gr.Column():
-                    prompt = gr.Textbox(lines=8, label="Prompt", 
-                        value="Render a clear and concise summary of the photo.")
-                    with gr.Row():
-                        submit = gr.Button("Submit", variant="primary")
-                        clear = gr.Button("Clear", variant="secondary") 
-                with gr.Column():
-                    output = gr.Textbox(label="Output", lines=10)
-            data = gr.Dataset(components=[image, prompt], samples=visual_examples, label="Examples", headers=["Image", "Prompt"])
-            submit.click(chatbot, [image, prompt], [output])
-            clear.click(lambda: None, [], [output])
-            data.click(lambda x: x, [data], [image, prompt])
-
 demo.queue(concurrency_count=1, max_size=10)
-demo.launch(share=False)
+demo.launch(share=False)