Create app.py

app.py

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+import os
+import zipfile
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# 情感模型（京东）
+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()
+
+# 解压你自己的多标签模型
+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
+custom_tokenizer = AutoTokenizer.from_pretrained("result")
+custom_model = AutoModelForSequenceClassification.from_pretrained("result", use_safetensors=True)
+custom_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"
+}
+
+# 推理函数
+def analyze(text, threshold=0.5):
+    # 情感分析
+    inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    with torch.no_grad():
+        outputs = sentiment_model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=1).squeeze().tolist()
+    sentiment = "积极 (Positive)" if torch.argmax(outputs.logits) == 1 else "消极 (Negative)"
+    sentiment_result = f"{sentiment}\nPositive: {probs[1]:.2f}, Negative: {probs[0]:.2f}"
+
+    # 多标签分类
+    inputs = custom_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    with torch.no_grad():
+        logits = custom_model(**inputs).logits
+        probs = torch.sigmoid(logits).squeeze().tolist()
+    if isinstance(probs, float):  # 单个标签时
+        probs = [probs]
+    results = [
+        f"{label_map[i]} ({probs[i]:.2f})"
+        for i in range(len(probs)) if probs[i] >= threshold
+    ]
+    if results:
+        label_result = "\n".join(results)
+    else:
+        label_result = "The model was unable to identify the correct labels."
+
+    return f"【Sentiment analysis】\n{sentiment_result}\n\n【Category of topic】\n{label_result}"
+
+# 创建 Gradio 页面
+demo = gr.Interface(
+    fn=analyze,
+    inputs=[
+        gr.Textbox(lines=3, label="请输入评论内容"),
+        gr.Slider(minimum=0.1, maximum=0.9, step=0.05, value=0.5, label="分类标签阈值")
+    ],
+    outputs="text",
+    title="中文评论分析器",
+    description="使用京东情感模型 + 自定义多标签模型，对评论内容进行双重分析"
+)
+
+demo.launch()