Update app.py

app.py

@@ -1,18 +1,7 @@
-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
 
@@ -20,69 +9,6 @@ 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'
@@ -90,25 +16,6 @@ def load_biomedclip_model():
     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():
@@ -121,91 +28,24 @@ def compute_similarity(image, text, biomedclip_model, biomedclip_processor):
     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):
+def gradio_reset(chat_state, img_list, similarity_output):
     """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
+    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, gr.update(value="", visible=False)
 
-def upload_img(gr_img, text_input, chat_state):
+def upload_img(gr_img, text_input, chat_state, similarity_output):
     """Handles image upload."""
     if gr_img is None:
-        return None, None, gr.update(interactive=True), chat_state, None
+        return None, None, gr.update(interactive=True), chat_state, None, gr.update(visible=False)
     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
-
+    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, gr.update(visible=True)
 
 def gradio_ask(user_message, chatbot, chat_state):
     """Handles user input."""
@@ -215,24 +55,21 @@ def gradio_ask(user_message, chatbot, chat_state):
     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"""
+def gradio_answer(chatbot, chat_state, img_list, biomedclip_model, biomedclip_processor, similarity_output):
+    """Computes and displays similarity scores."""
     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)
+        return chatbot, chat_state, img_list, similarity_output
+
+    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
+
+    similarity_text = f"Similarity Score: {similarity_score:.3f}"
+    chatbot[-1][1] = similarity_text
+    return chatbot, chat_state, img_list, gr.update(value=similarity_text, visible=True)
+
 
 title = """<h1 align="center">Medical Image Analysis Tool</h1>"""
-description = """<h3>Upload medical images, ask questions, and receive analysis.</h3>"""
+description = """<h3>Upload medical images, ask questions, and receive a similarity score.</h3>"""
 examples_list=[
                     ["./case1.png", "Analyze the X-ray for any abnormalities."],
                     ["./case2.jpg", "What type of disease may be present?"],
@@ -240,13 +77,10 @@ examples_list=[
                 ]
 
 # 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():
@@ -255,37 +89,19 @@ with gr.Blocks() as demo:
             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)
+            similarity_output = gr.Textbox(label="Similarity Score", visible=False, 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])
+    upload_button.click(upload_img, [image, text_input, chat_state, similarity_output], [image, text_input, upload_button, chat_state, img_list, similarity_output])
 
     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]
+        gradio_answer, [chatbot, chat_state, img_list, biomedclip_model, biomedclip_processor, similarity_output], [chatbot, chat_state, img_list, similarity_output]
     )
-    clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list], queue=False)
-
+    clear.click(gradio_reset, [chat_state, img_list, similarity_output], [chatbot, image, text_input, upload_button, chat_state, img_list, similarity_output], queue=False)
 
 demo.launch()