app.py

import streamlit as st
import torch
from PIL import Image
import numpy as np
import cv2
from model import load_model, predict_with_uncertainty
import torchvision.transforms as transforms
from transformers import AutoProcessor, AutoModelForImageTextToText
from io import BytesIO

# 设置页面配置
st.set_page_config(
    page_title="医学图像分析系统",
    page_icon="🏥",
    layout="wide"
)

# 初始化模型
@st.cache_resource
def load_models():
    # 加载分割模型
    seg_model = load_model()
    seg_model.eval()
    
    # 加载分析模型
    model_id = "google/medgemma-4b-it"
    analysis_model = AutoModelForImageTextToText.from_pretrained(
        model_id,
        torch_dtype=torch.bfloat16,
        device_map="auto",
        token="HUGGINGFACE_TOKEN"
    )
    processor = AutoProcessor.from_pretrained(
        model_id,
        token="HUGGINGFACE_TOKEN"
    )
    
    return seg_model, analysis_model, processor

# 页面标题
st.title("🏥 医学图像分析系统")
st.markdown("---")

# 加载模型
with st.spinner("正在加载模型..."):
    seg_model, analysis_model, processor = load_models()

# 创建两列布局
col1, col2 = st.columns(2)

with col1:
    st.subheader("📤 上传图片")
    uploaded_file = st.file_uploader("选择一张图片", type=["png", "jpg", "jpeg"])
    
    if uploaded_file is not None:
        # 显示原始图片
        image = Image.open(uploaded_file).convert("RGB")
        st.image(image, caption="原始图片", use_column_width=True)
        
        # 处理图片
        if st.button("开始分析"):
            with st.spinner("正在处理..."):
                # 图像分割
                image_resized = image.resize((224, 224))
                transform = transforms.ToTensor()
                image_tensor = transform(image_resized).unsqueeze(0)
                
                # 执行分割
                preds_mean, preds_uncertainty = predict_with_uncertainty(image_tensor)
                
                # 生成分割掩码
                pred_binary = (preds_mean > 0.5).astype(np.uint8) * 255
                mask_image = Image.fromarray(pred_binary).convert("L")
                
                # 处理不确定性图
                uncertainty = (preds_uncertainty - preds_uncertainty.min()) / (preds_uncertainty.max() - preds_uncertainty.min() + 1e-8)
                uncertainty_colormap = cv2.applyColorMap((uncertainty * 255).astype(np.uint8), cv2.COLORMAP_INFERNO)
                uncertainty_image = Image.fromarray(uncertainty_colormap).convert("RGB")
                
                # 合并图片
                combined = Image.new("RGB", (mask_image.width + uncertainty_image.width, mask_image.height))
                combined.paste(mask_image.convert("RGB"), (0, 0))
                combined.paste(uncertainty_image, (mask_image.width, 0))
                
                # 图像分析
                messages = [
                    {
                        "role": "system",
                        "content": [{"type": "text", "text": "你是一名皮肤病专家，请使用中文分析图片."}]
                    },
                    {
                        "role": "user",
                        "content": [
                            {"type": "text", "text": "这是一张皮肤病的图片，帮我分析一下"},
                            {"type": "image", "image": image}
                        ]
                    }
                ]
                
                inputs = processor.apply_chat_template(
                    messages, add_generation_prompt=True, tokenize=True,
                    return_dict=True, return_tensors="pt"
                ).to(analysis_model.device, dtype=torch.bfloat16)
                
                input_len = inputs["input_ids"].shape[-1]
                
                with torch.inference_mode():
                    generation = analysis_model.generate(**inputs, max_new_tokens=200, do_sample=False)
                    generation = generation[0][input_len:]
                
                analysis_text = processor.decode(generation, skip_special_tokens=True)
                
                # 显示结果
                with col2:
                    st.subheader("📊 分析结果")
                    st.image(combined, caption="分割结果", use_column_width=True)
                    st.markdown("### 📝 图像分析")
                    st.write(analysis_text)

# 添加页脚
st.markdown("---")
st.markdown("### 使用说明")
st.markdown("""
1. 在左侧上传一张医学图像
2. 点击"开始分析"按钮
3. 系统将自动进行图像分割和分析
4. 右侧将显示分割结果和分析报告
""")