chore: update gradio dependency to version 4.35.0

app.py

@@ -1,25 +1,35 @@
-
 # from .demo_modelpart import InferenceDemo
 import gradio as gr
 import os
+
 # import time
 import cv2
 
 
 # import copy
 import torch
-import spaces
+
+# import spaces
 import numpy as np
 
 from llava import conversation as conversation_lib
 from llava.constants import DEFAULT_IMAGE_TOKEN
 
 
-from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from llava.constants import (
+    IMAGE_TOKEN_INDEX,
+    DEFAULT_IMAGE_TOKEN,
+    DEFAULT_IM_START_TOKEN,
+    DEFAULT_IM_END_TOKEN,
+)
 from llava.conversation import conv_templates, SeparatorStyle
 from llava.model.builder import load_pretrained_model
 from llava.utils import disable_torch_init
-from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
+from llava.mm_utils import (
+    tokenizer_image_token,
+    get_model_name_from_path,
+    KeywordsStoppingCriteria,
+)
 
 from PIL import Image
 
@@ -28,12 +38,19 @@ from PIL import Image
 from io import BytesIO
 from transformers import TextStreamer
 
+
 class InferenceDemo(object):
-    def __init__(self,args,model_path,tokenizer, model, image_processor, context_len) -> None:
+    def __init__(
+        self, args, model_path, tokenizer, model, image_processor, context_len
+    ) -> None:
         disable_torch_init()
 
-        
-        self.tokenizer, self.model, self.image_processor, self.context_len = tokenizer, model, image_processor, context_len
+        self.tokenizer, self.model, self.image_processor, self.context_len = (
+            tokenizer,
+            model,
+            image_processor,
+            context_len,
+        )
 
         if "llama-2" in model_name.lower():
             conv_mode = "llava_llama_2"
@@ -41,32 +58,36 @@ class InferenceDemo(object):
             conv_mode = "llava_v1"
         elif "mpt" in model_name.lower():
             conv_mode = "mpt"
-        elif 'qwen' in model_name.lower():
+        elif "qwen" in model_name.lower():
             conv_mode = "qwen_1_5"
         else:
             conv_mode = "llava_v0"
 
         if args.conv_mode is not None and conv_mode != args.conv_mode:
-            print("[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format(conv_mode, args.conv_mode, args.conv_mode))
+            print(
+                "[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format(
+                    conv_mode, args.conv_mode, args.conv_mode
+                )
+            )
         else:
             args.conv_mode = conv_mode
-        self.conv_mode=conv_mode
+        self.conv_mode = conv_mode
         self.conversation = conv_templates[args.conv_mode].copy()
         self.num_frames = args.num_frames
 
 
-
 def is_valid_video_filename(name):
-    video_extensions = ['avi', 'mp4', 'mov', 'mkv', 'flv', 'wmv', 'mjpeg']
-    
-    ext = name.split('.')[-1].lower()
-    
+    video_extensions = ["avi", "mp4", "mov", "mkv", "flv", "wmv", "mjpeg"]
+
+    ext = name.split(".")[-1].lower()
+
     if ext in video_extensions:
         return True
     else:
         return False
 
-def sample_frames(video_file, num_frames) :
+
+def sample_frames(video_file, num_frames):
     video = cv2.VideoCapture(video_file)
     total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
     interval = total_frames // num_frames
@@ -81,18 +102,19 @@ def sample_frames(video_file, num_frames) :
     video.release()
     return frames
 
+
 def load_image(image_file):
     if image_file.startswith("http") or image_file.startswith("https"):
         response = requests.get(image_file)
         if response.status_code == 200:
             image = Image.open(BytesIO(response.content)).convert("RGB")
         else:
-            print('failed to load the image')
+            print("failed to load the image")
     else:
-        print('Load image from local file')
+        print("Load image from local file")
         print(image_file)
         image = Image.open(image_file).convert("RGB")
-        
+
     return image
 
 
@@ -101,6 +123,8 @@ def clear_history(history):
     our_chatbot.conversation = conv_templates[our_chatbot.conv_mode].copy()
 
     return None
+
+
 def clear_response(history):
     for index_conv in range(1, len(history)):
         # loop until get a text response from our model.
@@ -111,165 +135,294 @@ def clear_response(history):
     history = history[:-index_conv]
     return history, question
 
-def print_like_dislike(x: gr.LikeData):
-    print(x.index, x.value, x.liked)
 
+# def print_like_dislike(x: gr.LikeData):
+#     print(x.index, x.value, x.liked)
 
 
 def add_message(history, message):
     # history=[]
     global our_chatbot
-    if len(history)==0:
-        our_chatbot = InferenceDemo(args,model_path,tokenizer, model, image_processor, context_len)
-        
+    if len(history) == 0:
+        our_chatbot = InferenceDemo(
+            args, model_path, tokenizer, model, image_processor, context_len
+        )
+
     for x in message["files"]:
         history.append(((x,), None))
     if message["text"] is not None:
         history.append((message["text"], None))
     return history, gr.MultimodalTextbox(value=None, interactive=False)
 
-@spaces.GPU
+
+# @spaces.GPU
 def bot(history):
-    text=history[-1][0]
-    images_this_term=[]
-    text_this_term=''
+    text = history[-1][0]
+    images_this_term = []
+    text_this_term = ""
     # import pdb;pdb.set_trace()
     num_new_images = 0
-    for i,message in enumerate(history[:-1]):
+    for i, message in enumerate(history[:-1]):
         if type(message[0]) is tuple:
             images_this_term.append(message[0][0])
             if is_valid_video_filename(message[0][0]):
-                num_new_images+=our_chatbot.num_frames
+                num_new_images += our_chatbot.num_frames
             else:
-                num_new_images+=1
+                num_new_images += 1
         else:
-            num_new_images=0
-            
+            num_new_images = 0
+
     # for message in history[-i-1:]:
     #     images_this_term.append(message[0][0])
 
-    assert len(images_this_term)>0, "must have an image"
+    assert len(images_this_term) > 0, "must have an image"
     # image_files = (args.image_file).split(',')
     # image = [load_image(f) for f in images_this_term if f]
-    image_list=[]
+    image_list = []
     for f in images_this_term:
         if is_valid_video_filename(f):
-            image_list+=sample_frames(f, our_chatbot.num_frames)
+            image_list += sample_frames(f, our_chatbot.num_frames)
         else:
             image_list.append(load_image(f))
-    image_tensor = [our_chatbot.image_processor.preprocess(f, return_tensors="pt")["pixel_values"][0].half().to(our_chatbot.model.device) for f in image_list]
+    image_tensor = [
+        our_chatbot.image_processor.preprocess(f, return_tensors="pt")["pixel_values"][
+            0
+        ]
+        .half()
+        .to(our_chatbot.model.device)
+        for f in image_list
+    ]
 
     image_tensor = torch.stack(image_tensor)
-    image_token = DEFAULT_IMAGE_TOKEN*num_new_images
+    image_token = DEFAULT_IMAGE_TOKEN * num_new_images
     # if our_chatbot.model.config.mm_use_im_start_end:
     #     inp = DEFAULT_IM_START_TOKEN + image_token + DEFAULT_IM_END_TOKEN + "\n" + inp
     # else:
-    inp=text
-    inp = image_token+ "\n" + inp
+    inp = text
+    inp = image_token + "\n" + inp
     our_chatbot.conversation.append_message(our_chatbot.conversation.roles[0], inp)
     # image = None
     our_chatbot.conversation.append_message(our_chatbot.conversation.roles[1], None)
     prompt = our_chatbot.conversation.get_prompt()
 
-    input_ids = tokenizer_image_token(prompt, our_chatbot.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(our_chatbot.model.device)
-    stop_str = our_chatbot.conversation.sep if our_chatbot.conversation.sep_style != SeparatorStyle.TWO else our_chatbot.conversation.sep2
+    input_ids = (
+        tokenizer_image_token(
+            prompt, our_chatbot.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
+        )
+        .unsqueeze(0)
+        .to(our_chatbot.model.device)
+    )
+    stop_str = (
+        our_chatbot.conversation.sep
+        if our_chatbot.conversation.sep_style != SeparatorStyle.TWO
+        else our_chatbot.conversation.sep2
+    )
     keywords = [stop_str]
-    stopping_criteria = KeywordsStoppingCriteria(keywords, our_chatbot.tokenizer, input_ids)
-    streamer = TextStreamer(our_chatbot.tokenizer, skip_prompt=True, skip_special_tokens=True)
+    stopping_criteria = KeywordsStoppingCriteria(
+        keywords, our_chatbot.tokenizer, input_ids
+    )
+    streamer = TextStreamer(
+        our_chatbot.tokenizer, skip_prompt=True, skip_special_tokens=True
+    )
     print(our_chatbot.model.device)
     print(input_ids.device)
     print(image_tensor.device)
     # import pdb;pdb.set_trace()
     with torch.inference_mode():
-        output_ids = our_chatbot.model.generate(input_ids, images=image_tensor, do_sample=True, temperature=0.2, max_new_tokens=1024, streamer=streamer, use_cache=False, stopping_criteria=[stopping_criteria])
+        output_ids = our_chatbot.model.generate(
+            input_ids,
+            images=image_tensor,
+            do_sample=True,
+            temperature=0.2,
+            max_new_tokens=1024,
+            streamer=streamer,
+            use_cache=False,
+            stopping_criteria=[stopping_criteria],
+        )
 
     outputs = our_chatbot.tokenizer.decode(output_ids[0]).strip()
     if outputs.endswith(stop_str):
-        outputs = outputs[:-len(stop_str)]
+        outputs = outputs[: -len(stop_str)]
     our_chatbot.conversation.messages[-1][-1] = outputs
-   
-    history[-1]=[text,outputs]
-    
+
+    history[-1] = [text, outputs]
+
     return history
+
+
 txt = gr.Textbox(
     scale=4,
     show_label=False,
     placeholder="Enter text and press enter.",
     container=False,
 )
-with gr.Blocks() as demo:
-    
+
+with gr.Blocks(
+    css=".message-wrap.svelte-1lcyrx4>div.svelte-1lcyrx4  img {min-width: 40px}",
+) as demo:
+
     # Informations
-    title_markdown = ("""
+    title_markdown = """
         # LLaVA-NeXT Interleave
         [[Blog]](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/)  [[Code]](https://github.com/LLaVA-VL/LLaVA-NeXT) [[Model]](https://huggingface.co/lmms-lab/llava-next-interleave-7b)
-    """)
-    tos_markdown = ("""
+    """
+    tos_markdown = """
     ### TODO!. Terms of use
     By using this service, users are required to agree to the following terms:
     The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
     Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
     For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
-    """)
-    learn_more_markdown = ("""
+    """
+    learn_more_markdown = """
     ### TODO!. License
     The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
-    """)
+    """
     models = [
         "LLaVA-Interleave-7B",
     ]
     cur_dir = os.path.dirname(os.path.abspath(__file__))
     gr.Markdown(title_markdown)
-    with gr.Row():
-        with gr.Column(scale=1):
-            with gr.Row():
-                chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image","video"], placeholder="Enter message or upload file...", show_label=False)
-            with gr.Row():
-                gr.Examples(examples=[
-                    [{"files": [f"{cur_dir}/examples/code1.jpeg",f"{cur_dir}/examples/code2.jpeg"], "text": "Please pay attention to the movement of the object from the first image to the second image, then write a HTML code to show this movement."}],
-                    [{"files": [f"{cur_dir}/examples/shub.jpg",f"{cur_dir}/examples/shuc.jpg",f"{cur_dir}/examples/shud.jpg"], "text": "what is fun about the images?"}],
-                    [{"files": [f"{cur_dir}/examples/iphone-15-price-1024x576.jpg",f"{cur_dir}/examples/dynamic-island-1024x576.jpg",f"{cur_dir}/examples/iphone-15-colors-1024x576.jpg",f"{cur_dir}/examples/Iphone-15-Usb-c-charger-1024x576.jpg",f"{cur_dir}/examples/A-17-processors-1024x576.jpg"], "text": "The images are the PPT of iPhone 15 review. can you summarize the main information?"}],
-                    [{"files": [f"{cur_dir}/examples/fangao3.jpeg",f"{cur_dir}/examples/fangao2.jpeg",f"{cur_dir}/examples/fangao1.jpeg"], "text": "Do you kown who draw these paintings?"}],
-                    [{"files": [f"{cur_dir}/examples/oprah-winfrey-resume.png",f"{cur_dir}/examples/steve-jobs-resume.jpg"], "text": "Hi, there are two candidates, can you provide a brief description for each of them for me?"}],
-                    [{"files": [f"{cur_dir}/examples/original_bench.jpeg",f"{cur_dir}/examples/changed_bench.jpeg"], "text": "How to edit image1 to make it look like image2?"}],
-                    [{"files": [f"{cur_dir}/examples/twitter2.jpeg",f"{cur_dir}/examples/twitter3.jpeg",f"{cur_dir}/examples/twitter4.jpeg"], "text": "Please write a twitter blog post with the images."}],
-                    # [{"files": [f"{cur_dir}/examples/twitter3.jpeg",f"{cur_dir}/examples/twitter4.jpeg"], "text": "Please write a twitter blog post with the images."}],
-                    # [{"files": [f"playground/demo/examples/lion1_.mp4",f"playground/demo/examples/lion2_.mp4"], "text": "The input contains two videos, the first half is the first video and the second half is the second video. What is the difference between the two videos?"}],
-        
-            ], inputs=[chat_input], label="Compare images: ",examples_per_page=3)
-        with gr.Column(scale=2):
-            with gr.Row():
-                chatbot = gr.Chatbot(
-                    [],
-                    elem_id="chatbot",
-                    bubble_full_width=False
-                )
-            with gr.Row():
-                upvote_btn = gr.Button(value="👍  Upvote", interactive=True)
-                downvote_btn = gr.Button(value="👎  Downvote", interactive=True)
-                flag_btn = gr.Button(value="⚠️  Flag", interactive=True)
-                #stop_btn = gr.Button(value="⏹️  Stop Generation", interactive=True)
-                regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=True)
-                clear_btn = gr.Button(value="🗑️  Clear history", interactive=True)
-    chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
+    with gr.Column():
+        with gr.Row():
+            chatbot = gr.Chatbot([], elem_id="chatbot", bubble_full_width=False)
+
+        with gr.Row():
+            upvote_btn = gr.Button(value="👍  Upvote", interactive=True)
+            downvote_btn = gr.Button(value="👎  Downvote", interactive=True)
+            flag_btn = gr.Button(value="⚠️  Flag", interactive=True)
+            # stop_btn = gr.Button(value="⏹️  Stop Generation", interactive=True)
+            regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=True)
+            clear_btn = gr.Button(value="🗑️  Clear history", interactive=True)
+
+        chat_input = gr.MultimodalTextbox(
+            interactive=True,
+            file_types=["image", "video"],
+            placeholder="Enter message or upload file...",
+            show_label=False,
+        )
+
+        print(cur_dir)
+        gr.Examples(
+            examples=[
+                # [
+                #     {
+                #         "text": "<image> <image> <image> Which image shows a different mood of character from the others?",
+                #         "files": [f"{cur_dir}/examples/examples_image12.jpg", f"{cur_dir}/examples/examples_image13.jpg", f"{cur_dir}/examples/examples_image14.jpg"]
+                #     },
+                #     {
+                #         "text": "Please pay attention to the movement of the object from the first image to the second image, then write a HTML code to show this movement.",
+                #         "files": [
+                #             f"{cur_dir}/examples/code1.jpeg",
+                #             f"{cur_dir}/examples/code2.jpeg",
+                #         ],
+                #     }
+                # ],
+                [
+                    {
+                        "files": [
+                            f"{cur_dir}/examples/shub.jpg",
+                            f"{cur_dir}/examples/shuc.jpg",
+                            f"{cur_dir}/examples/shud.jpg",
+                        ],
+                        "text": "what is fun about the images?",
+                    }
+                ],
+                [
+                    {
+                        "files": [
+                            f"{cur_dir}/examples/iphone-15-price-1024x576.jpg",
+                            f"{cur_dir}/examples/dynamic-island-1024x576.jpg",
+                            f"{cur_dir}/examples/iphone-15-colors-1024x576.jpg",
+                            f"{cur_dir}/examples/Iphone-15-Usb-c-charger-1024x576.jpg",
+                            f"{cur_dir}/examples/A-17-processors-1024x576.jpg",
+                        ],
+                        "text": "The images are the PPT of iPhone 15 review. can you summarize the main information?",
+                    }
+                ],
+                [
+                    {
+                        "files": [
+                            f"{cur_dir}/examples/fangao3.jpeg",
+                            f"{cur_dir}/examples/fangao2.jpeg",
+                            f"{cur_dir}/examples/fangao1.jpeg",
+                        ],
+                        "text": "Do you kown who draw these paintings?",
+                    }
+                ],
+                [
+                    {
+                        "files": [
+                            f"{cur_dir}/examples/oprah-winfrey-resume.png",
+                            f"{cur_dir}/examples/steve-jobs-resume.jpg",
+                        ],
+                        "text": "Hi, there are two candidates, can you provide a brief description for each of them for me?",
+                    }
+                ],
+                [
+                    {
+                        "files": [
+                            f"{cur_dir}/examples/original_bench.jpeg",
+                            f"{cur_dir}/examples/changed_bench.jpeg",
+                        ],
+                        "text": "How to edit image1 to make it look like image2?",
+                    }
+                ],
+                [
+                    {
+                        "files": [
+                            f"{cur_dir}/examples/twitter2.jpeg",
+                            f"{cur_dir}/examples/twitter3.jpeg",
+                            f"{cur_dir}/examples/twitter4.jpeg",
+                        ],
+                        "text": "Please write a twitter blog post with the images.",
+                    }
+                ],
+                [
+                    {
+                        "files": [
+                            f"{cur_dir}/examples/twitter3.jpeg",
+                            f"{cur_dir}/examples/twitter4.jpeg",
+                        ],
+                        "text": "Please write a twitter blog post with the images.",
+                    }
+                ],
+                [
+                    {
+                        "files": [
+                            f"playground/demo/examples/lion1_.mp4",
+                            f"playground/demo/examples/lion2_.mp4",
+                        ],
+                        "text": "The input contains two videos, the first half is the first video and the second half is the second video. What is the difference between the two videos?",
+                    }
+                ],
+            ],
+            inputs=[chat_input],
+            label="Compare images: ",
+            examples_per_page=3,
+        )
+
+    chat_msg = chat_input.submit(
+        add_message, [chatbot, chat_input], [chatbot, chat_input]
+    )
     bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
     bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
 
-    chatbot.like(print_like_dislike, None, None)
-    clear_btn.click(fn=clear_history, inputs=[chatbot], outputs=[chatbot], api_name="clear_all")
-
-    
-    
-    
+    # chatbot.like(print_like_dislike, None, None)
+    clear_btn.click(
+        fn=clear_history, inputs=[chatbot], outputs=[chatbot], api_name="clear_all"
+    )
 
 
 demo.queue()
 if __name__ == "__main__":
     import argparse
+
     argparser = argparse.ArgumentParser()
     argparser.add_argument("--server_name", default="0.0.0.0", type=str)
     argparser.add_argument("--port", default="6123", type=str)
-    argparser.add_argument("--model_path", default="lmms-lab/llava-next-interleave-qwen-7b", type=str)
+    argparser.add_argument(
+        "--model_path", default="lmms-lab/llava-next-interleave-qwen-7b", type=str
+    )
     # argparser.add_argument("--model-path", type=str, default="facebook/opt-350m")
     argparser.add_argument("--model-base", type=str, default=None)
     argparser.add_argument("--num-gpus", type=int, default=1)
@@ -280,15 +433,14 @@ if __name__ == "__main__":
     argparser.add_argument("--load-8bit", action="store_true")
     argparser.add_argument("--load-4bit", action="store_true")
     argparser.add_argument("--debug", action="store_true")
-    
+
     args = argparser.parse_args()
     model_path = args.model_path
-    filt_invalid="cut"
-    model_name = get_model_name_from_path(args.model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit)
-    model=model.to(torch.device('cuda'))
+    filt_invalid = "cut"
+    # model_name = get_model_name_from_path(args.model_path)
+    # tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit)
+    # model=model.to(torch.device('cuda'))
     our_chatbot = None
     # import pdb;pdb.set_trace()
     # try:
     demo.launch()
-

requirements.txt

@@ -3,7 +3,7 @@ ninja
 opencv-python
 open_clip_torch
 fastapi
-gradio==3.35.2
+gradio==4.35.0
 markdown2[all]
 numpy
 requests