Create app.py

app.py

@@ -0,0 +1,61 @@
+import gradio as gr  
+from transformers import AutoModel, AutoTokenizer  
+import torch
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+
+
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+model_name = "model"  
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)
+model = AutoModel.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+).to(torch.bfloat16).eval().cuda()
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+
+def preprocess_image(file_path, image_size=448):
+
+    transform = build_transform(image_size)
+    pixel_values = transform(file_path)
+    return torch.stack([pixel_values]).to(torch.bfloat16).cuda()
+
+def generate_response(image, text):  
+
+    pixel_values = preprocess_image(image, dynamic=True)
+
+    generation_config = dict(max_new_tokens=2048, do_sample=False)
+
+    question = '<image>\n' + text
+
+    response = model.chat(tokenizer, pixel_values, question, generation_config)
+    
+    return response  
+
+iface = gr.Interface(  
+    fn=generate_response,  
+    inputs=[  
+        gr.Image(type="pil", label="上传图片"),  
+        gr.Textbox(lines=2, placeholder="输入你的问题..."),  
+    ],  
+    outputs="text",  
+    title="Llava-QW",  
+    description="上传一张图片并输入你的问题，模型将生成相应的回答。",  
+)  
+
+iface.launch()