app.py

import gradio as gr
import torch
from transformers import AutoConfig, AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
from janus.utils.io import load_pil_images
from PIL import Image
import numpy as np
import os
import time
import spaces

# Load medical imaging-optimized model and processor
model_path = "deepseek-ai/Janus-Pro-1B"
config = AutoConfig.from_pretrained(model_path)
language_config = config.language_config
language_config._attn_implementation = 'eager'

# Initialize model with medical imaging parameters
vl_gpt = AutoModelForCausalLM.from_pretrained(
    model_path,
    language_config=language_config,
    trust_remote_code=True,
    medical_head=True  # Assuming custom medical imaging head
).to(torch.bfloat16 if torch.cuda.is_available() else torch.float16)

if torch.cuda.is_available():
    vl_gpt = vl_gpt.cuda()

vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer
cuda_device = 'cuda' if torch.cuda.is_available() else 'cpu'

@torch.inference_mode()
@spaces.GPU(duration=120)
def medical_image_analysis(medical_image, clinical_question, seed, top_p, temperature):
    """Analyze medical images (CT, MRI, X-ray, histopathology) with clinical context."""
    torch.cuda.empty_cache()
    torch.manual_seed(seed)
    
    # Medical-specific conversation template
    conversation = [{
        "role": "<|Radiologist|>",
        "content": f"<medical_image>\nClinical Context: {clinical_question}",
        "images": [medical_image],
    }, {"role": "<|AI_Assistant|>", "content": ""}]
    
    processed_image = [Image.fromarray(medical_image)]
    inputs = vl_chat_processor(
        conversations=conversation, 
        images=processed_image,
        force_batchify=True
    ).to(cuda_device, dtype=torch.bfloat16)
    
    inputs_embeds = vl_gpt.prepare_inputs_embeds(**inputs)
    
    # Medical-optimized generation parameters
    outputs = vl_gpt.language_model.generate(
        inputs_embeds=inputs_embeds,
        attention_mask=inputs.attention_mask,
        max_new_tokens=512,
        temperature=0.2,  # Lower for clinical precision
        top_p=0.9,
        repetition_penalty=1.2,  # Reduce hallucination
        medical_mode=True
    )
    
    findings = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
    return f"Clinical Findings:\n{findings}"

@torch.inference_mode()
@spaces.GPU(duration=120)
def generate_medical_image(prompt, seed=None, guidance=5, t2i_temperature=0.5):
    """Generate synthetic medical images for educational/research purposes."""
    torch.cuda.empty_cache()
    if seed is not None:
        torch.manual_seed(seed)
    
    # Medical image generation parameters
    medical_config = {
        'width': 512,
        'height': 512,
        'parallel_size': 3,
        'modality': 'mri',  # Can specify CT, X-ray, etc.
        'anatomy': 'brain'  # Target anatomy
    }
    
    messages = [{
        'role': '<|Clinician|>', 
        'content': f"{prompt} [Modality: {medical_config['modality']}, Anatomy: {medical_config['anatomy']}]"
    }]
    
    text = vl_chat_processor.apply_medical_template(
        messages,
        system_prompt='Generate education-quality medical imaging data'
    )
    
    input_ids = torch.LongTensor(tokenizer.encode(text)).to(cuda_device)
    generated_tokens, patches = vl_gpt.generate_medical_image(
        input_ids,
        **medical_config,
        cfg_weight=guidance,
        temperature=t2i_temperature
    )
    
    # Post-processing for medical imaging standards
    synthetic_images = postprocess_medical_images(patches, **medical_config)
    return [Image.fromarray(img).resize((512, 512)) for img in synthetic_images]

# Medical-optimized Gradio interface
with gr.Blocks(title="Medical Imaging AI Suite") as demo:
    gr.Markdown("""## Medical Image Analysis Suite v2.1
    *For research use only - not for clinical diagnosis*""")
    
    with gr.Tab("Clinical Image Analysis"):
        with gr.Row():
            medical_image_input = gr.Image(label="Upload Medical Scan")
            clinical_question = gr.Textbox(label="Clinical Query", 
                placeholder="E.g.: 'Assess tumor progression in this MRI series'")
            
        with gr.Accordion("Advanced Parameters", open=False):
            und_seed = gr.Number(42, label="Reproducibility Seed")
            analysis_top_p = gr.Slider(0.8, 1.0, 0.95, label="Diagnostic Certainty")
            analysis_temp = gr.Slider(0.1, 0.5, 0.2, label="Analysis Precision")
            
        analysis_btn = gr.Button("Analyze Scan", variant="primary")
        clinical_report = gr.Textbox(label="AI Analysis Report", interactive=False)
        
        gr.Examples(
            examples=[
                ["Identify pulmonary nodules in this CT scan", "ct_chest.png"],
                ["Assess MRI for multiple sclerosis lesions", "brain_mri.jpg"],
                ["Histopathology analysis: tumor grading", "biopsy_slide.png"]
            ],
            inputs=[clinical_question, medical_image_input]
        )

    with gr.Tab("Medical Imaging Synthesis"):
        gr.Markdown("**Educational Image Generation**")
        synth_prompt = gr.Textbox(label="Synthesis Prompt",
            placeholder="E.g.: 'Synthetic brain MRI showing glioblastoma multiforme'")
        
        with gr.Row():
            synth_guidance = gr.Slider(3, 7, 5, label="Anatomical Accuracy")
            synth_temp = gr.Slider(0.3, 1.0, 0.6, label="Synthesis Variability")
            
        synth_btn = gr.Button("Generate Educational Images", variant="secondary")
        synthetic_gallery = gr.Gallery(label="Synthetic Medical Images", 
                                     columns=3, object_fit="contain")
        
        gr.Examples(
            examples=[
                "High-resolution CT of healthy lung parenchyma",
                "T2-weighted MRI of lumbar spine with herniated disc",
                "Histopathology slide of benign breast tissue"
            ],
            inputs=synth_prompt
        )

    # Connect functionality
    analysis_btn.click(
        medical_image_analysis,
        inputs=[medical_image_input, clinical_question, und_seed, analysis_top_p, analysis_temp],
        outputs=clinical_report
    )
    
    synth_btn.click(
        generate_medical_image,
        inputs=[synth_prompt, und_seed, synth_guidance, synth_temp],
        outputs=synthetic_gallery
    )

demo.launch(share=True, server_port=7860)