update

README.md

@@ -5,7 +5,7 @@ colorFrom: green
 colorTo: yellow
 sdk: gradio
 sdk_version: 4.24.0
-app_file: app.py
+app_file: app_idefics2.py
 pinned: false
 license: apache-2.0
 short_description: Multimodal Language Model

app_idefics2.py

@@ -0,0 +1,224 @@
+import gradio as gr
+import spaces
+import time
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from transformers.image_utils import load_image
+from typing import List
+processor = AutoProcessor.from_pretrained("TIGER-Lab/Mantis-8B-Idefics2")
+model = AutoModelForVision2Seq.from_pretrained("TIGER-Lab/Mantis-8B-Idefics2")
+
+@spaces.GPU
+def generate_stream(text:str, images:List[Image.Image], history: List[dict], **kwargs):
+    global processor, model
+    model = model.to("cuda")
+    if not images:
+        images = None
+        
+    prompt = processor.apply_chat_template(history, add_generation_prompt=True)
+    print("Prompt: ")
+    print(prompt)
+    print("Images: ")
+    print(images)
+    inputs = processor(text=prompt, images=images, return_tensors="pt")
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    
+    from transformers import TextIteratorStreamer
+    from threading import Thread
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    kwargs["streamer"] = streamer
+    inputs.update(kwargs)
+    thread = Thread(target=model.generate, kwargs=inputs)
+    thread.start()
+    output = ""
+    for _output in streamer:
+        output += _output
+        yield output
+
+def enable_next_image(uploaded_images, image):
+    uploaded_images.append(image)
+    return uploaded_images, gr.MultimodalTextbox(value=None, interactive=False)
+
+def add_message(history, message):
+    if message["files"]:
+        for file in message["files"]:
+            history.append([(file,), None])
+    if message["text"]:
+        history.append([message["text"], None])
+    return history, gr.MultimodalTextbox(value=None)
+
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+
+def get_chat_images(history):
+    images = []
+    for message in history:
+        if isinstance(message[0], tuple):
+            image = load_image(message[0][0])
+            images.append(image)
+    return images
+
+def get_chat_history(history):
+    
+    images = get_chat_images(history)
+    messages = []
+    cur_image_idx = 0
+    for i, message in enumerate(history):
+        if isinstance(message[0], str):
+            num_images = message[0].count("<image>")
+            messages.append(
+                {
+                    "role": "user",
+                    "content": []
+                }
+            )
+            assert num_images + cur_image_idx <= len(images), f"Number of images uploaded is less than the number of <image> placeholders in the text. Please upload more images."
+            if num_images > 0:
+                for sub_text in message[0].split("<image>"):
+                    if sub_text.strip():
+                        messages[-1]["content"].append({"type": "text", "text": sub_text.strip()})
+                    if cur_image_idx < len(images):
+                        messages[-1]["content"].append({"type": "image"})
+                        cur_image_idx += 1
+            else:
+                messages[-1]["content"].append({"type": "text", "text": message[0]})
+        elif isinstance(message[0], tuple):
+            pass
+    return messages, images
+
+
+def bot(history):
+    cur_messages = {"text": "", "images": []}
+    for message in history[::-1]:
+        if message[1]:
+            break
+        if isinstance(message[0], str):
+            cur_messages["text"] = message[0] + " " + cur_messages["text"]
+        elif isinstance(message[0], tuple):
+            cur_messages["images"].extend(message[0])
+    cur_messages["text"] = cur_messages["text"].strip()
+    cur_messages["images"] = cur_messages["images"][::-1]
+    if not cur_messages["text"]:
+        raise gr.Error("Please enter a message")
+    if cur_messages['text'].count("<image>") < len(cur_messages['images']):
+        gr.Warning("The number of images uploaded is more than the number of <image> placeholders in the text. Will automatically prepend <image> to the text.")
+        cur_messages['text'] = "<image> "* (len(cur_messages['images']) - cur_messages['text'].count("<image>")) + cur_messages['text']
+        history[-1][0] = cur_messages["text"]
+    if cur_messages['text'].count("<image>") > len(cur_messages['images']):
+        gr.Warning("The number of images uploaded is less than the number of <image> placeholders in the text. Will automatically remove extra <image> placeholders from the text.")
+        cur_messages['text'] = cur_messages['text'][::-1].replace("<image>"[::-1], "", cur_messages['text'].count("<image>") - len(cur_messages['images']))[::-1]
+        history[-1][0] = cur_messages["text"]
+    
+    chat_history, chat_images = get_chat_history(history)
+    
+    generation_kwargs = {
+        "max_new_tokens": 4096,
+        "num_beams": 1,
+        "do_sample": False
+    }
+    
+    response = generate_stream(None, chat_images, chat_history, **generation_kwargs) 
+    for _output in response:
+        history[-1][1] = _output
+        time.sleep(0.05)
+        yield history
+
+
+        
+def build_demo():
+    with gr.Blocks() as demo:
+        
+        gr.Markdown(""" # Mantis
+Mantis is a multimodal conversational AI model that can chat with users about images and text. It's optimized for multi-image reasoning, where inverleaved text and images can be used to generate responses.
+
+### [Paper](https://arxiv.org/abs/2405.01483) | [Github](https://github.com/TIGER-AI-Lab/Mantis) | [Models](https://huggingface.co/collections/TIGER-Lab/mantis-6619b0834594c878cdb1d6e4) | [Dataset](https://huggingface.co/datasets/TIGER-Lab/Mantis-Instruct) | [Website](https://tiger-ai-lab.github.io/Mantis/)            
+        """)
+        
+        gr.Markdown("""## Chat with Mantis
+        Mantis supports interleaved text-image input format, where you can simply use the placeholder `<image>` to indicate the position of uploaded images.
+        The model is optimized for multi-image reasoning, while preserving the ability to chat about text and images in a single conversation.
+        (The model currently serving is [🤗 TIGER-Lab/Mantis-8B-Idefics2](https://huggingface.co/TIGER-Lab/Mantis-8B-Idefics2))
+        """)
+        
+        chatbot = gr.Chatbot(line_breaks=True)
+        chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload images. Please use <image> to indicate the position of uploaded images", show_label=True)
+        
+        chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
+        
+        """
+        with gr.Accordion(label='Advanced options', open=False):
+            temperature = gr.Slider(
+                label='Temperature',
+                minimum=0.1,
+                maximum=2.0,
+                step=0.1,
+                value=0.2,
+                interactive=True
+            )
+            top_p = gr.Slider(
+                label='Top-p',
+                minimum=0.05,
+                maximum=1.0,
+                step=0.05,
+                value=1.0,
+                interactive=True
+            )
+        """
+
+        bot_msg = chat_msg.success(bot, chatbot, chatbot, api_name="bot_response")
+        
+        chatbot.like(print_like_dislike, None, None)
+
+        with gr.Row():
+            send_button = gr.Button("Send")
+            clear_button = gr.ClearButton([chatbot, chat_input])
+
+        send_button.click(
+            add_message, [chatbot, chat_input], [chatbot, chat_input]
+        ).then(
+            bot, chatbot, chatbot, api_name="bot_response"
+        )
+        
+        gr.Examples(
+            examples=[
+                {
+                    "text": "<image> <image> <image> Which image shows a different mood of character from the others?",
+                    "files": ["./examples/image12.jpg", "./examples/image13.jpg", "./examples/image14.jpg"]
+                },
+                {
+                    "text": "<image> <image> What's the difference between these two images? Please describe as much as you can.", 
+                    "files": ["./examples/image1.jpg", "./examples/image2.jpg"]
+                },
+                {
+                    "text": "<image> <image> Which image shows an older dog?",
+                    "files": ["./examples/image8.jpg", "./examples/image9.jpg"]   
+                },
+                {
+                    "text": "Write a description for the given image sequence in a single paragraph, what is happening in this episode?", 
+                    "files": ["./examples/image3.jpg", "./examples/image4.jpg", "./examples/image5.jpg", "./examples/image6.jpg", "./examples/image7.jpg"]
+                },
+                {
+                    "text": "<image> <image> How many dices are there in image 1 and image 2 respectively?",
+                    "files": ["./examples/image10.jpg", "./examples/image15.jpg"]
+                },
+            ],
+            inputs=[chat_input],
+        )        
+        
+        gr.Markdown("""
+## Citation
+```
+@article{jiang2024mantis,
+  title={MANTIS: Interleaved Multi-Image Instruction Tuning},
+  author={Jiang, Dongfu and He, Xuan and Zeng, Huaye and Wei, Con and Ku, Max and Liu, Qian and Chen, Wenhu},
+  journal={arXiv preprint arXiv:2405.01483},
+  year={2024}
+}
+```""")
+    return demo    
+    
+
+if __name__ == "__main__":
+    demo = build_demo()
+    demo.launch()

requirements.txt

@@ -1,5 +1,5 @@
 torch
-transformers
+transformers>=4.41.0
 Pillow
 gradio
 spaces