Update app.py

app.py

@@ -4,47 +4,53 @@ from qwen_vl_utils import process_vision_info
 import gradio as gr
 
 # 加载模型和处理器
-model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    "Qwen/Qwen2.5-VL-7B-Instruct", 
-    torch_dtype="auto", 
-    device_map="auto"
-)
-processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+try:
+    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+        "Qwen/Qwen2.5-VL-7B-Instruct", 
+        torch_dtype="auto", 
+        device_map="auto"
+    )
+    processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+except Exception as e:
+    print(f"模型加载失败: {e}")
 
 # 定义处理函数
 def recognize_and_analyze(image, text_prompt):
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": image},
-                {"type": "text", "text": text_prompt},
-            ],
-        }
-    ]
+    try:
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": image},
+                    {"type": "text", "text": text_prompt},
+                ],
+            }
+        ]
 
-    # 准备推理输入数据
-    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    image_inputs, video_inputs = process_vision_info(messages)
-    inputs = processor(
-        text=[text],
-        images=image_inputs,
-        videos=video_inputs,
-        padding=True,
-        return_tensors="pt",
-    )
-    inputs = inputs.to(model.device)
+        # 准备推理输入数据
+        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        image_inputs, video_inputs = process_vision_info(messages)
+        inputs = processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
+        )
+        inputs = inputs.to(model.device)
 
-    # 推理：生成输出文本
-    generated_ids = model.generate(**inputs, max_new_tokens=128)
-    generated_ids_trimmed = [
-        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-    ]
-    output_text = processor.batch_decode(
-        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )
-    
-    return output_text[0]
+        # 推理：生成输出文本
+        generated_ids = model.generate(**inputs, max_new_tokens=128)
+        generated_ids_trimmed = [
+            out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        output_text = processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+        
+        return output_text[0]
+    except Exception as e:
+        return f"处理过程中出现错误: {e}"
 
 # 设置 Gradio 界面
 interface = gr.Interface(