Create app.py

app.py

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+import torch
+import torch.nn as nn
+from transformers import BertTokenizer, BertModel
+import gradio as gr
+
+# Load tokenizer and base BERT model
+model_name = "keerthikapujari25/bert-emotion-classifier"  # Replace with your HF username/repo
+tokenizer = BertTokenizer.from_pretrained(model_name)
+bert_model = BertModel.from_pretrained(model_name)
+
+# Define your classifier architecture (same as training)
+class BERTClassifier(nn.Module):
+    def __init__(self, bert_model, num_labels=5, dropout=0.3):
+        super(BERTClassifier, self).__init__()
+        self.bert = bert_model
+        self.dropout = nn.Dropout(dropout)
+        self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)
+    
+    def forward(self, input_ids, attention_mask):
+        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        pooled_output = outputs.pooler_output
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        return logits
+
+# Load model
+model = BERTClassifier(bert_model, num_labels=5, dropout=0.3)
+model.load_state_dict(torch.load(f"{model_name}/pytorch_model.bin", map_location='cpu'))
+model.eval()
+
+emotion_labels = ['anger', 'fear', 'joy', 'sadness', 'surprise']
+
+def predict_emotions(text):
+    # Tokenize input
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    
+    # Get predictions
+    with torch.no_grad():
+        outputs = model(inputs['input_ids'], inputs['attention_mask'])
+        probs = torch.sigmoid(outputs)[0].numpy()
+    
+    # Create results dictionary
+    results = {emotion_labels[i]: float(probs[i]) for i in range(len(emotion_labels))}
+    return results
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_emotions,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text here to detect emotions..."),
+    outputs=gr.Label(num_top_classes=5),
+    title="Emotion Classification",
+    description="Multi-label emotion detection using fine-tuned BERT. Enter any text to detect anger, fear, joy, sadness, and surprise.",
+    examples=[
+        ["I am so happy and excited about this!"],
+        ["This is terrible and makes me angry."],
+        ["I can't believe this happened, it's shocking!"]
+    ]
+)
+
+iface.launch()