Update app.py

app.py

@@ -1,154 +1,72 @@
+# app.py
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel
+import time
+
+# 模型加载（约需2分钟）
+def load_model():
+    start = time.time()
+    model = CLIPModel.from_pretrained("vinid/plip")
+    processor = CLIPProcessor.from_pretrained("vinid/plip") 
+    print(f"模型加载完成，耗时：{time.time()-start:.1f}秒")
+    return model, processor
+
+model, processor = load_model()
+
+# 预测函数
+def classify_image(image, text_options):
+    try:
+        # 图像预处理
+        processed_img = image.convert("RGB").resize((224,224))
+        
+        # 文本处理（自动分割逗号分隔的标签）
+        labels = [t.strip() for t in text_options.split(',')]
+        
+        # 模型推理
+        inputs = processor(
+            text=labels,
+            images=processed_img,
+            return_tensors="pt",
+            padding=True
+        )
+        outputs = model(**inputs)
+        probs = outputs.logits_per_image.softmax(dim=1).tolist()[0]
+        
+        # 结果格式化
+        return {label: round(prob,3) for label, prob in zip(labels, probs)}
+    
+    except Exception as e:
+        return f"处理错误：{str(e)}"
+
+# 界面布局
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+    gr.Markdown("# 🩺 医学影像智能诊断系统")
+    
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="上传病理切片")
+        text_input = gr.Textbox(
+            label="诊断标签（逗号分隔）",
+            value="恶性肿瘤, 良性病变, 炎症反应"
+        )
+    
+    submit_btn = gr.Button("开始分析", variant="primary")
+    
+    output = gr.Label(label="诊断概率分布", num_top_classes=3)
+    
+    # 示例数据
+    gr.Examples(
+        examples=[
+            ["sample1.jpg", "肺癌, 肺结核, 正常组织"],
+            ["sample2.png", "胃癌, 胃炎, 胃溃疡"]
         ],
-        outputs=[result, seed],
+        inputs=[image_input, text_input]
+    )
+
+    submit_btn.click(
+        fn=classify_image,
+        inputs=[image_input, text_input],
+        outputs=output
     )
 
-if __name__ == "__main__":
-    demo.launch()
+app.launch(debug=True)