Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline, AutoModelForImageClassification, AutoFeatureExtractor
+from PIL import Image
+import torch
+import os
+import json
+
+# 设置 Kaggle API 凭证
+def setup_kaggle():
+    # 创建 .kaggle 目录
+    os.makedirs(os.path.expanduser("~/.kaggle"), exist_ok=True)
+    # 读取并写入 kaggle.json 文件
+    with open("/app/kaggle.json", "r") as f:  # 直接使用 /app/kaggle.json 路径
+        kaggle_token = json.load(f)
+    with open(os.path.expanduser("~/.kaggle/kaggle.json"), "w") as f:
+        json.dump(kaggle_token, f)
+    os.chmod(os.path.expanduser("~/.kaggle/kaggle.json"), 0o600)
+
+# 从 Kaggle 下载模型文件
+def download_model():
+    # 设置 Kaggle API 凭证
+    setup_kaggle()
+
+    # 使用 Kaggle API 下载文件
+    os.system("kaggle kernels output sonia0822/20241015 -p /app")  # 修改为您的 Kernel ID 和下载路径
+
+    # 确保模型文件已下载
+    if not os.path.exists("/app/model.pth"):
+        raise FileNotFoundError("模型文件下载失败！")
+
+# 在加载模型前下载
+if not os.path.exists("model.pth"):
+    print("Downloading model...")
+    download_model()
+
+# 模型保存路径
+classification_model_path = "/app/model.pth"
+gpt2_model_path = "/app/gpt2-finetuned"
+
+# 加载分类模型和特征提取器
+print("加载分类模型...")
+classification_model = AutoModelForImageClassification.from_pretrained(
+    "microsoft/beit-base-patch16-224-pt22k", num_labels=16
+)
+classification_model.load_state_dict(torch.load(classification_model_path, map_location="cpu"))
+feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
+print("分类模型加载成功")
+
+# 加载 GPT-2 文本生成模型
+print("加载 GPT-2 模型...")
+gpt2_generator = pipeline("text-generation", model=gpt2_model_path, tokenizer=gpt2_model_path)
+print("GPT-2 模型加载成功")
+
+# 定义风格标签列表
+art_styles = [
+    "现实主义", "巴洛克", "后印象派", "印象派", "浪漫主义", "超现实主义",
+    "表现主义", "立体派", "野兽派", "抽象艺术", "新艺术", "象征主义",
+    "新古典主义", "洛可可", "文艺复兴", "极简主义"
+]
+
+# 标签映射
+label_mapping = {0: 0, 2: 1, 3: 2, 4: 3, 7: 4, 9: 5, 10: 6, 12: 7, 15: 8, 17: 9, 18: 10, 20: 11, 21: 12, 23: 13, 24: 14, 25: 15}
+reverse_label_mapping = {v: k for k, v in label_mapping.items()}
+
+# 生成风格描述的函数
+def classify_and_generate_description(image):
+    image = image.convert("RGB")
+    inputs = feature_extractor(images=image, return_tensors="pt").to("cpu")
+    classification_model.eval()
+    with torch.no_grad():
+        outputs = classification_model(**inputs).logits
+        predicted_class = torch.argmax(outputs, dim=1).item()
+
+    predicted_label = reverse_label_mapping.get(predicted_class, "未知")
+    predicted_style = art_styles[predicted_class] if predicted_class < len(art_styles) else "未知"
+
+    prompt = f"请详细描述{predicted_style}的艺术风格。"
+    description = gpt2_generator(prompt, max_length=100, num_return_sequences=1)[0]["generated_text"]
+    return predicted_style, description
+
+def ask_gpt2(question):
+    response = gpt2_generator(question, max_length=100, num_return_sequences=1)[0]["generated_text"]
+    return response
+
+# Gradio 界面
+with gr.Blocks() as demo:
+    gr.Markdown("# 艺术风格分类和生成描述")
+    with gr.Row():
+        image_input = gr.Image(label="上传一张艺术图片")
+        style_output = gr.Textbox(label="预测的艺术风格")
+        description_output = gr.Textbox(label="生成的风格描述")
+    with gr.Row():
+        question_input = gr.Textbox(label="输入问题")
+        answer_output = gr.Textbox(label="GPT-2 生成的回答")
+    classify_btn = gr.Button("生成风格描述")
+    question_btn = gr.Button("问 GPT-2 一个问题")
+    classify_btn.click(fn=classify_and_generate_description, inputs=image_input, outputs=[style_output, description_output])
+    question_btn.click(fn=ask_gpt2, inputs=question_input, outputs=answer_output)
+
+demo.launch()