Create app.py

app.py

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+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer, AutoModel
+import gradio as gr
+
+class StanceClassifier(nn.Module):
+    def __init__(self,transformer_model, num_classes, dropout_rate=0.6):
+        super(StanceClassifier, self).__init__()
+        self.transformer = transformer_model
+        self.dropout = nn.Dropout(dropout_rate)
+        self.layer_norm = nn.LayerNorm(transformer_model.config.hidden_size)
+        self.classifier = nn.Sequential(
+            nn.Dropout(dropout_rate),
+            nn.Linear(transformer_model.config.hidden_size, transformer_model.config.hidden_size//2),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate),
+            nn.Linear(transformer_model.config.hidden_size//2, num_classes)
+        )
+    def forward(self, input_ids, attention_mask):
+        outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask)
+        pooled_output = outputs.last_hidden_state[:, 0]
+        pooled_output = self.layer_norm(pooled_output)
+        logits = self.classifier(pooled_output)
+        return logits
+    
+torch.manual_seed(42)
+checkpoint = "bert-base-chinese"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+base_model = AutoModel.from_pretrained(checkpoint)
+
+model = StanceClassifier(base_model, num_classes=3)
+model.load_state_dict(torch.load("stance_classifier.pth", map_location=torch.device('cpu')))
+model.eval()
+labels = ['KMT', 'DPP', 'Neutral']
+
+def predict_stance(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=64)
+    with torch.no_grad():
+        outputs = model(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"]
+        )
+        probs = nn.Softmax(dim=1)(outputs)
+        print(probs)
+        predicted_class = torch.argmax(probs, dim=1).item()
+        confidence = probs[0][predicted_class].item()
+    return labels[predicted_class], confidence
+
+def gradio_interface(text):
+    stance, conf = predict_stance(text)
+    return f"Predicted Stance: {stance} with confidence {conf:.4f}"
+
+def ui():
+    gr.Interface(
+        fn=gradio_interface,
+        inputs=gr.Textbox(label="Input Text", placeholder="Enter text to predict political stance..."),
+        outputs=gr.Textbox(label="Prediction Result"),
+        title="Political Stance Prediction",
+        description="Enter a text to predict its political stance (KMT, DPP, Neutral)."
+    ).launch()
+
+if __name__ == "__main__":
+    ui()