Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# 加载模型和 tokenizer
+model_name3_p2 = "rajesh426/distilbert-base-uncased_Up_Sampling_Sub_Category_SPEECH_TEXT_DISPLAY_v1"
+num_labels = 10  # 根据你的数据集设置标签的数量
+model3_p2 = AutoModelForSequenceClassification.from_pretrained(model_name3_p2, num_labels=num_labels, ignore_mismatched_sizes=True)
+tokenizer3 = AutoTokenizer.from_pretrained(model_name3_p2)
+
+# Streamlit 应用
+st.title("Text Classification with HuggingFace Spaces")
+st.write("Enter a sentence to classify:")
+
+user_input = st.text_input("")
+
+if user_input:
+    inputs = tokenizer3(user_input, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model3_p2(**inputs)
+    logits = outputs.logits
+    predicted_class_id = torch.argmax(logits, dim=-1).item()
+
+    st.write(f"Predicted class ID: {predicted_class_id}")