Update app.py

app.py

@@ -1,15 +1,15 @@
+import os
 import numpy as np
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
-import simple_slice_viewer as ssv
-import SimpleITK as sikt
 import gradio as gr
+import matplotlib.pyplot as plt
 
-device = torch.device('cpu')  # Set to 'cuda' if using a GPU
-dtype = torch.float32  # Data type for model processing
-
+# 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  # Number of projection outputs required for the image
+proj_out_num = 256
 
 # Load model and tokenizer
 model = AutoModelForCausalLM.from_pretrained(
@@ -27,35 +27,72 @@ tokenizer = AutoTokenizer.from_pretrained(
     trust_remote_code=True
 )
 
-def process_image(image_path, question):
-    # Load the image
-    image_np = np.load(image_path)  # Load the .npy image
+# 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)
-
-    # Prepare image for model
+    
     image_pt = torch.from_numpy(image_np).unsqueeze(0).to(dtype=dtype, device=device)
-
-    # Generate model response
     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]
 
-# Gradio Interface
-def gradio_interface(image, question):
-    response = process_image(image.name, question)
-    return response
-
-# Gradio App
-gr.Interface(
-    fn=gradio_interface,
-    inputs=[
-        gr.File(label="Upload .npy Image", type="filepath"),  # For uploading .npy image
-        gr.Textbox(label="Enter your question", placeholder="Ask something about the image..."),
-    ],
-    outputs=gr.Textbox(label="Model Response"),
-    title="Medical Image Analysis",
-    description="Upload a .npy image and ask a question to analyze it using the model."
-).launch()
+
+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()