Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
+import torch
+
+# 加载模型和处理器
+model_name = "microsoft/llava-med-v1.5-mistral-7b"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+processor = AutoProcessor.from_pretrained(model_name)
+
+def predict(image, question):
+    # 将图像和问题处理为模型输入格式
+    inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
+
+    # 生成答案
+    with torch.no_grad():
+        outputs = model.generate(**inputs)
+
+    # 解码输出
+    answer = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+    return answer
+
+# 创建 Gradio 界面
+interface = gr.Interface(
+    fn=predict,
+    inputs=[gr.inputs.Image(type="pil"), gr.inputs.Textbox(label="Question")],
+    outputs="text",
+    title="Medical Visual Question Answering"
+)
+
+if __name__ == "__main__":
+    interface.launch()