Update app.py

app.py

@@ -9,6 +9,27 @@ sentiment_model_name = "uer/roberta-base-finetuned-jd-binary-chinese"
 sentiment_tokenizer = AutoTokenizer.from_pretrained(sentiment_model_name)
 sentiment_model = AutoModelForSequenceClassification.from_pretrained(sentiment_model_name)
 sentiment_model.eval()
+if not os.path.exists("result"):
+    with zipfile.ZipFile("model_output.zip", "r") as zip_ref:
+        zip_ref.extractall(".")
+
+
+# 加载你的多标签分类模型
+label_tokenizer = AutoTokenizer.from_pretrained("result")
+label_model = AutoModelForSequenceClassification.from_pretrained("result", use_safetensors=True)
+label_model.eval()
+
+# 多标签类别
+label_map = {
+    0: "Landscape & Culture",
+    1: "Service & Facilities",
+    2: "Experience & Atmosphere",
+    3: "Transportation Accessibility",
+    4: "Interactive Activities",
+    5: "Price & Consumption"
+}
+threshold = 0.5
+
 
 # --------- Multi-label Classification Model (Your model) ---------
 label_dir = "./result"