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"metadata": {
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"source": [
"import json\n",
"import os\n",
"from transformers import AutoProcessor, AutoModelForVision2Seq\n",
"import torch\n",
"from PIL import Image\n",
"import gradio as gr\n",
"import subprocess\n",
"from llava.model.builder import load_pretrained_model\n",
"from llava.mm_utils import get_model_name_from_path\n",
"from llava.eval.run_llava import eval_model\n",
"\n",
"# Load the LLaVA model and processor\n",
"llava_model_path = \"/workspace/LLaVA/LLaVA/llava-fine_tune_model\"\n",
"\n",
"# Load the LLaVA-Med model and processor\n",
"llava_med_model_path = \"/workspace/LLaVA-Med/Model/fine_tuned-med-llava\"\n",
"\n",
"# Args class to store arguments for LLaVA models\n",
"class Args:\n",
" def __init__(self, model_path, model_base, model_name, query, image_path, conv_mode, image_file, sep, temperature, top_p, num_beams, max_new_tokens):\n",
" self.model_path = model_path\n",
" self.model_base = model_base\n",
" self.model_name = model_name\n",
" self.query = query\n",
" self.image_path = image_path\n",
" self.conv_mode = conv_mode\n",
" self.image_file = image_file\n",
" self.sep = sep\n",
" self.temperature = temperature\n",
" self.top_p = top_p\n",
" self.num_beams = num_beams\n",
" self.max_new_tokens = max_new_tokens\n",
"\n",
"# Function to predict using Idefics2\n",
"def predict_idefics2(image, question, temperature, max_tokens):\n",
" image = image.convert(\"RGB\")\n",
" images = [image]\n",
"\n",
" messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"image\"},\n",
" {\"type\": \"text\", \"text\": question}\n",
" ]\n",
" }\n",
" ]\n",
" input_text = idefics2_processor.apply_chat_template(messages, add_generation_prompt=False).strip()\n",
"\n",
" inputs = idefics2_processor(text=[input_text], images=images, return_tensors=\"pt\", padding=True).to(\"cuda:0\")\n",
"\n",
" with torch.no_grad():\n",
" outputs = idefics2_model.generate(**inputs, max_length=max_tokens, max_new_tokens=max_tokens, temperature=temperature)\n",
"\n",
" predictions = idefics2_processor.decode(outputs[0], skip_special_tokens=True)\n",
"\n",
" return predictions\n",
"\n",
"# Function to predict using LLaVA\n",
"def predict_llava(image, question, temperature, max_tokens):\n",
" # Save the image temporarily\n",
" image.save(\"temp_image.jpg\")\n",
"\n",
" # Setup evaluation arguments\n",
" args = Args(\n",
" model_path=llava_model_path,\n",
" model_base=None,\n",
" model_name=get_model_name_from_path(llava_model_path),\n",
" query=question,\n",
" image_path=\"temp_image.jpg\",\n",
" conv_mode=None,\n",
" image_file=\"temp_image.jpg\",\n",
" sep=\",\",\n",
" temperature=temperature,\n",
" top_p=None,\n",
" num_beams=1,\n",
" max_new_tokens=max_tokens\n",
" )\n",
"\n",
" # Generate the answer using the selected model\n",
" output = eval_model(args)\n",
"\n",
" return output\n",
"\n",
"# Function to predict using LLaVA-Med\n",
"def predict_llava_med(image, question, temperature, max_tokens):\n",
" # Save the image temporarily\n",
" image_path = \"temp_image_med.jpg\"\n",
" image.save(image_path)\n",
"\n",
" # Command to run the LLaVA-Med model\n",
" command = [\n",
" \"python\", \"-m\", \"llava.eval.run_llava\",\n",
" \"--model-name\", llava_med_model_path,\n",
" \"--image-file\", image_path,\n",
" \"--query\", question,\n",
" \"--temperature\", str(temperature),\n",
" \"--max-new-tokens\", str(max_tokens)\n",
" ]\n",
"\n",
" # Execute the command and capture the output\n",
" result = subprocess.run(command, capture_output=True, text=True)\n",
"\n",
" return result.stdout.strip() # Return the output as text\n",
"\n",
"# Main prediction function\n",
"def predict(model_name, image, text, temperature, max_tokens):\n",
" if model_name == \"LLaVA\":\n",
" return predict_llava(image, text, temperature, max_tokens)\n",
" elif model_name == \"LLaVA-Med\":\n",
" return predict_llava_med(image, text, temperature, max_tokens)\n",
"\n",
"# Define the Gradio interface\n",
"interface = gr.Interface(\n",
" fn=predict,\n",
" inputs=[\n",
" gr.Radio(choices=[\"LLaVA\", \"LLaVA-Med\"], label=\"Select Model\"),\n",
" gr.Image(type=\"pil\", label=\"Input Image\"),\n",
" gr.Textbox(label=\"Input Text\"),\n",
" gr.Slider(minimum=0.1, maximum=1.0, default=0.7, label=\"Temperature\"),\n",
" gr.Slider(minimum=1, maximum=512, default=256, label=\"Max Tokens\"),\n",
" ],\n",
" outputs=gr.Textbox(label=\"Output Text\"),\n",
" title=\"Multimodal LLM Interface\",\n",
" description=\"Switch between models and adjust parameters.\",\n",
")\n",
"\n",
"# Launch the Gradio interface\n",
"interface.launch()\n"
],
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