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app.py

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+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+import gradio as gr
+
+label_dict={'neutral': 0,'negative': 1, 'positive': 2}
+
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased",
+                                                      num_labels=len(label_dict),
+                                                      output_attentions=False,
+                                                      output_hidden_states=False)
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', 
+                                          do_lower_case=True)
+model.load_state_dict(torch.load('finetuned_BERT_epoch_2.model',map_location='cpu'))
+model.eval()
+
+def get_key_by_value(dictionary, target_value):
+    for key, value in dictionary.items():
+        if value == target_value:
+            return key
+
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    inputs.to('cpu')  
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.nn.functional.softmax(logits, dim=1)
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+    return get_key_by_value(label_dict,predicted_class)
+
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(),
+    outputs=gr.Textbox(),
+    live=True,
+    title="BERT Sentiment Analysis (CPU)",
+    description="Enter a text and get sentiment prediction. Model runs on CPU.",
+)
+iface.launch()