Create app.py

app.py

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+# 导入所需库
+import os
+from PIL import Image
+import pandas as pd
+from transformers import pipeline
+
+# 任务 2: 加载预训练模型
+# 加载年龄分类模型
+age_classifier = pipeline("image-classification", model="nateraw/vit-age-classifier")
+# 加载性别分类模型
+gender_classifier = pipeline("image-classification", model="rizvandwiki/gender-classification")
+# 加载幸福度分类模型
+emotion_classifier = pipeline("image-classification", model="Rajaram1996/Happiness-Classifier")
+
+# 任务 1: 定义图像文件夹路径
+image_folder = "images/"
+image_files = [f for f in os.listdir(image_folder) if f.endswith(('.jpg', '.png'))]
+
+# 初始化结果列表
+results = []
+
+# 任务 3: 处理每张图像并分类属性
+for image_file in image_files:
+    image_path = os.path.join(image_folder, image_file)
+    image = Image.open(image_path)
+
+    # 预测年龄
+    age_prediction = age_classifier(image)
+    predicted_age = age_prediction[0]['label']
+
+    # 预测性别
+    gender_prediction = gender_classifier(image)
+    predicted_gender = gender_prediction[0]['label']
+
+    # 预测幸福度
+    emotion_prediction = emotion_classifier(image)
+    predicted_happiness = emotion_prediction[0]['label']
+
+    # 将结果添加到列表
+    results.append({
+        "Image Name": image_file,
+        "Predicted Age": predicted_age,
+        "Predicted Gender": predicted_gender,
+        "Predicted Happiness": predicted_happiness
+    })
+
+# 任务 4: 使用 pandas 生成 CSV 报告
+df = pd.DataFrame(results)
+df.to_csv("participant_classification_report.csv", index=False)
+
+print("分类完成，结果已保存到 participant_classification_report.csv")