Add application file

app.py

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+import sys
+sys.path.append('./LLAUS')
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
+import torch
+from llava import LlavaLlamaForCausalLM
+from llava.conversation import conv_templates
+from llava.utils import disable_torch_init
+from transformers import CLIPVisionModel, CLIPImageProcessor, StoppingCriteria
+from PIL import Image
+from torch.cuda.amp import autocast
+import gradio as gr
+import spaces
+from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model
+import os
+from transformers import AutoProcessor, AutoModel
+import torch.nn.functional as F
+
+#---------------------------------
+#++++++++     Model     ++++++++++
+#---------------------------------
+
+DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
+DEFAULT_IM_START_TOKEN = "<im_start>"
+DEFAULT_IM_END_TOKEN = "<im_end>"
+
+def patch_config(config_path):
+    """Applies necessary patches to the model config."""
+    patch_dict = {
+        "use_mm_proj": True,
+        "mm_vision_tower": "openai/clip-vit-large-patch14",
+        "mm_hidden_size": 1024
+    }
+    cfg = AutoConfig.from_pretrained(config_path)
+    if not hasattr(cfg, "mm_vision_tower"):
+        print(f'`mm_vision_tower` not found in `{config_path}`, applying patch and save to disk.')
+        for k, v in patch_dict.items():
+            setattr(cfg, k, v)
+        cfg.save_pretrained(config_path)
+
+def load_llava_model():
+    """Loads and initializes the LLaVA model."""
+    model_name = "Baron-GG/LLaVA-Med" # Change this to your model if you uploaded a new one
+    disable_torch_init()
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    patch_config(model_name)
+    model = LlavaLlamaForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16).cuda()
+    model.model.requires_grad_(False)
+
+    image_processor = CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower, torch_dtype=torch.float16)
+
+    model.config.use_cache = False
+    model.config.tune_mm_mlp_adapter = False
+    model.config.freeze_mm_mlp_adapter = False
+    model.config.mm_use_im_start_end = True
+
+    mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
+    tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+    if mm_use_im_start_end:
+        tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+
+    vision_tower = model.model.vision_tower[0]
+    vision_tower.to(device='cuda', dtype=torch.float16)
+    vision_config = vision_tower.config
+    vision_config.im_patch_token = tokenizer.convert_tokens_to_ids([DEFAULT_IMAGE_PATCH_TOKEN])[0]
+    vision_config.use_im_start_end = mm_use_im_start_end
+    if mm_use_im_start_end:
+        vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
+    image_token_len = (vision_config.image_size // vision_config.patch_size) ** 2
+    
+    model = prepare_model_for_int8_training(model)
+    lora_config = LoraConfig(
+        r=64,
+        lora_alpha=16,
+        target_modules=["q_proj", "v_proj","k_proj","o_proj"],
+        lora_dropout=0.05,
+        bias="none",
+        task_type="CAUSAL_LM",
+    )
+    model = get_peft_model(model, lora_config).cuda()
+
+    model.eval()
+    return model, tokenizer, image_processor, image_token_len, mm_use_im_start_end
+    
+def load_biomedclip_model():
+    """Loads the BiomedCLIP model and tokenizer."""
+    biomedclip_model_name = 'microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224'
+    processor = AutoProcessor.from_pretrained(biomedclip_model_name)
+    model = AutoModel.from_pretrained(biomedclip_model_name).cuda().eval()
+    return model, processor
+
+
+class KeywordsStoppingCriteria(StoppingCriteria):
+    """Custom stopping criteria for generation."""
+    def __init__(self, keywords, tokenizer, input_ids):
+        self.keywords = keywords
+        self.tokenizer = tokenizer
+        self.start_len = None
+        self.input_ids = input_ids
+
+    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        if self.start_len is None:
+            self.start_len = self.input_ids.shape[1]
+        else:
+            outputs = self.tokenizer.batch_decode(output_ids[:, self.start_len:], skip_special_tokens=True)[0]
+            for keyword in self.keywords:
+                if keyword in outputs:
+                    return True
+        return False
+
+def compute_similarity(image, text, biomedclip_model, biomedclip_processor):
+    """Computes similarity scores using BiomedCLIP."""
+    with torch.no_grad():
+        inputs = biomedclip_processor(text=text, images=image, return_tensors="pt", padding=True).to(biomedclip_model.device)
+        outputs = biomedclip_model(**inputs)
+        image_embeds = outputs.image_embeds
+        text_embeds = outputs.text_embeds
+    image_embeds = F.normalize(image_embeds, dim=-1)
+    text_embeds = F.normalize(text_embeds, dim=-1)
+    similarity = (text_embeds @ image_embeds.transpose(-1, -2)).squeeze()
+    return similarity
+
+@torch.no_grad()
+def eval_llava_model(llava_model, llava_tokenizer, llava_image_processor, image, question, image_token_len, mm_use_im_start_end, max_new_tokens, temperature):
+    """Evaluates the LLaVA model for a given image and question."""
+    
+    image_list = []
+    image_tensor = llava_image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]  # 3, 224, 224
+    image_list.append(image_tensor)
+    image_idx = 1
+
+    if mm_use_im_start_end:
+        qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN * image_token_len * image_idx + DEFAULT_IM_END_TOKEN + question
+    else:
+        qs = DEFAULT_IMAGE_PATCH_TOKEN * image_token_len * image_idx + '\n' + question
+
+    conv = conv_templates["simple"].copy()
+    conv.append_message(conv.roles[0], qs)
+    prompt = conv.get_prompt()
+    inputs = llava_tokenizer([prompt])
+
+    image_tensor = torch.stack(image_list, dim=0).half().cuda()
+    input_ids = torch.as_tensor(inputs.input_ids).cuda()
+
+    keywords = ['###']
+    stopping_criteria = KeywordsStoppingCriteria(keywords, llava_tokenizer, input_ids)
+
+    with autocast():
+        output_ids = llava_model.generate(
+            input_ids=input_ids,
+            images=image_tensor,
+            do_sample=True,
+            temperature=temperature,
+            max_new_tokens=max_new_tokens,
+            stopping_criteria=[stopping_criteria]
+        )
+
+    input_token_len = input_ids.shape[1]
+    n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+    if n_diff_input_output > 0:
+        print(f'[Warning] Sample: {n_diff_input_output} output_ids are not the same as the input_ids')
+    outputs = llava_tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+
+    while True:
+        cur_len = len(outputs)
+        outputs = outputs.strip()
+        for pattern in ['###', 'Assistant:', 'Response:']:
+            if outputs.startswith(pattern):
+                outputs = outputs[len(pattern):].strip()
+        if len(outputs) == cur_len:
+            break
+
+    try:
+        index = outputs.index(conv.sep)
+    except ValueError:
+        outputs += conv.sep
+        index = outputs.index(conv.sep)
+
+    outputs = outputs[:index].strip()
+    print(outputs)
+    return outputs
+
+#---------------------------------
+#++++++++     Gradio     ++++++++++
+#---------------------------------
+
+SHARED_UI_WARNING = f'''### [NOTE] It is possible that you are waiting in a lengthy queue.
+You can duplicate and use it with a paid private GPU.
+<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/Vision-CAIR/minigpt4?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-xl-dark.svg" alt="Duplicate Space"></a>
+Alternatively, you can also use the demo on our [project page](https://minigpt-4.github.io).
+'''
+
+def gradio_reset(chat_state, img_list):
+    """Resets the chat state and image list."""
+    if chat_state is not None:
+        chat_state.messages = []
+    if img_list is not None:
+        img_list = []
+    return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your medical image first', interactive=False), gr.update(value="Upload & Start Analysis", interactive=True), chat_state, img_list
+
+def upload_img(gr_img, text_input, chat_state):
+    """Handles image upload."""
+    if gr_img is None:
+        return None, None, gr.update(interactive=True), chat_state, None
+    img_list = [gr_img]
+    return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Analysis", interactive=False), chat_state, img_list
+
+
+def gradio_ask(user_message, chatbot, chat_state):
+    """Handles user input."""
+    if not user_message:
+        return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
+    chatbot = chatbot + [[user_message, None]]
+    return '', chatbot, chat_state
+
+@spaces.GPU
+def gradio_answer(chatbot, chat_state, img_list, llava_model, llava_tokenizer, llava_image_processor, image_token_len, mm_use_im_start_end, max_new_token, temperature, biomedclip_model, biomedclip_processor):
+    """Generates and adds the bot's response to the chatbot using LLaVA"""
+    if not img_list:
+        return chatbot, chat_state, img_list
+        
+    # compute similarity using biomedclip
+    similarity_score = compute_similarity(img_list[0],chatbot[-1][0], biomedclip_model, biomedclip_processor)
+    print(f'Similarity Score is: {similarity_score}')
+    
+    # prepare the input for LLAVA
+    llava_input_text = f"Based on the image and query provided the similarity score is {similarity_score:.3f}. " + chatbot[-1][0]
+    llm_message = eval_llava_model(llava_model, llava_tokenizer, llava_image_processor, img_list[0], llava_input_text, image_token_len, mm_use_im_start_end, max_new_token, temperature)
+    
+    chatbot[-1][1] = llm_message
+    return chatbot, chat_state, img_list
+
+title = """<h1 align="center">Medical Image Analysis Tool</h1>"""
+description = """<h3>Upload medical images, ask questions, and receive analysis.</h3>"""
+examples_list=[
+                    ["./case1.png", "Analyze the X-ray for any abnormalities."],
+                    ["./case2.jpg", "What type of disease may be present?"],
+                    ["./case1.png","What is the anatomical structure shown here?"]
+                ]
+
+# Load models and related resources outside of the Gradio block for loading on startup
+llava_model, llava_tokenizer, llava_image_processor, image_token_len, mm_use_im_start_end = load_llava_model()
+biomedclip_model, biomedclip_processor = load_biomedclip_model()
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(title)
+    # gr.Markdown(SHARED_UI_WARNING)
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Column(scale=0.5):
+            image = gr.Image(type="pil", label="Medical Image")
+            upload_button = gr.Button(value="Upload & Start Analysis", interactive=True, variant="primary")
+            clear = gr.Button("Restart")
+
+            max_new_token = gr.Slider(
+                minimum=1,
+                maximum=512,
+                value=128,
+                step=1,
+                interactive=True,
+                label="Max new tokens"
+            )
+
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=2.0,
+                value=0.3,
+                step=0.1,
+                interactive=True,
+                label="Temperature",
+            )
+
+        with gr.Column():
+            chat_state = gr.State()
+            img_list = gr.State()
+            chatbot = gr.Chatbot(label='Medical Analysis')
+            text_input = gr.Textbox(label='Analysis Query', placeholder='Please upload your medical image first', interactive=False)
+            gr.Examples(examples=examples_list, inputs=[image, text_input])
+
+    upload_button.click(upload_img, [image, text_input, chat_state], [image, text_input, upload_button, chat_state, img_list])
+
+    text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
+        gradio_answer, [chatbot, chat_state, img_list, llava_model, llava_tokenizer, llava_image_processor, image_token_len, mm_use_im_start_end, max_new_token, temperature, biomedclip_model, biomedclip_processor], [chatbot, chat_state, img_list]
+    )
+    clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list], queue=False)
+
+
+demo.launch()