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import os
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import matplotlib.pyplot as plt
# Model setup
device = torch.device('cpu') # Use 'cuda' if GPU is available
dtype = torch.float32
model_name_or_path = 'GoodBaiBai88/M3D-LaMed-Phi-3-4B'
proj_out_num = 256
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
torch_dtype=torch.float32,
device_map='cpu',
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
model_name_or_path,
model_max_length=512,
padding_side="right",
use_fast=False,
trust_remote_code=True
)
# Chat history storage
chat_history = []
current_image = None
def extract_and_display_images(image_path):
npy_data = np.load(image_path)
if npy_data.ndim == 4 and npy_data.shape[1] == 32:
npy_data = npy_data[0]
elif npy_data.ndim != 3 or npy_data.shape[0] != 32:
return "Invalid .npy file format. Expected shape (1, 32, 256, 256) or (32, 256, 256)."
fig, axes = plt.subplots(4, 8, figsize=(12, 6))
for i, ax in enumerate(axes.flat):
ax.imshow(npy_data[i], cmap='gray')
ax.axis('off')
image_output = "extracted_images.png"
plt.savefig(image_output, bbox_inches='tight')
plt.close()
return image_output
def process_image(question):
global current_image
if current_image is None:
return "Please upload an image first."
image_np = np.load(current_image)
image_tokens = "<im_patch>" * proj_out_num
input_txt = image_tokens + question
input_id = tokenizer(input_txt, return_tensors="pt")['input_ids'].to(device=device)
image_pt = torch.from_numpy(image_np).unsqueeze(0).to(dtype=dtype, device=device)
generation = model.generate(image_pt, input_id, max_new_tokens=256, do_sample=True, top_p=0.9, temperature=1.0)
generated_texts = tokenizer.batch_decode(generation, skip_special_tokens=True)
return generated_texts[0]
def chat_interface(question):
global chat_history
response = process_image(question)
chat_history.append((question, response))
return chat_history
def upload_image(image):
global current_image
current_image = image.name
extracted_image_path = extract_and_display_images(current_image)
return "Image uploaded and processed successfully!", extracted_image_path
# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as chat_ui:
gr.Markdown("ICliniq AI-Powered Medical Image Analysis Workspace")
with gr.Row():
with gr.Column(scale=1, min_width=200):
chat_list = gr.Chatbot(value=[], label="Chat History", elem_id="chat-history")
with gr.Column(scale=4):
uploaded_image = gr.File(label="Upload .npy Image", type="filepath")
upload_status = gr.Textbox(label="Status", interactive=False)
extracted_image = gr.Image(label="Extracted Images")
question_input = gr.Textbox(label="Ask a question", placeholder="Ask something about the image...")
submit_button = gr.Button("Send")
uploaded_image.upload(upload_image, uploaded_image, [upload_status, extracted_image])
submit_button.click(chat_interface, question_input, chat_list)
question_input.submit(chat_interface, question_input, chat_list)
chat_ui.launch() |