Upload 3 files

app.py

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+import gradio as gr
+import torch
+from transformers import BertForSequenceClassification, BertTokenizer
+
+# Load the tokenizer from Hugging Face
+token_model = "indolem/indobertweet-base-uncased"
+tokenizer = BertTokenizer.from_pretrained(token_model)
+
+# Define the model directory where your config.json and pytorch_model.bin are located
+model_directory = "model_directory"  # Make sure this directory has config.json and pytorch_model.bin
+
+# Load the model
+# If your weights are named differently, ensure the file is named pytorch_model.bin or modify the loading method
+model = BertForSequenceClassification.from_pretrained(model_directory)
+model.eval()  # Set the model to evaluation mode
+
+# Check if CUDA is available and set the device accordingly
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+
+def classify_transaction(notes):
+    # Tokenize the input text
+    inputs = tokenizer.encode_plus(
+        notes,
+        None,
+        add_special_tokens=True,
+        max_length=256,
+        padding='max_length',
+        return_token_type_ids=False,
+        return_attention_mask=True,
+        truncation=True,
+        return_tensors='pt'
+    )
+
+    # Move tensors to the same device as the model
+    input_ids = inputs['input_ids'].to(device)
+    attention_mask = inputs['attention_mask'].to(device)
+
+    # Model in evaluation mode
+    model.eval()
+
+    # Make prediction
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask=attention_mask)
+
+    # Extract logits and convert to probabilities
+    logits = outputs[0]
+    probabilities = torch.softmax(logits, dim=1)
+
+    # Get the predicted class
+    predicted_class = torch.argmax(probabilities, dim=1).cpu().numpy()
+
+    # Return the predicted class
+    return f"Predicted Category: {predicted_class}"
+
+# Creating the Gradio interface
+iface = gr.Interface(
+    fn=classify_transaction,
+    inputs=gr.Textbox(lines=3, placeholder="Enter Transaction Notes Here", label="Transaction Notes"),
+    outputs=gr.Text(label="Classification Result"),
+    title="Transaction Category Classifier",
+    description="Enter transaction notes to get the predicted category.",
+    live=True  # Update the output as soon as the input changes
+)
+
+if __name__ == "__main__":
+    iface.launch()

modeling/finetuned_BERT_epoch_1.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:620d5d7ce69f6dfa7490dffd300e09853b73936b4a21286736660bbb2cf733a9
+size 442385251

requirements.txt

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+Flask==2.1.2
+gradio==4.15.0
+requests==2.27.1
+transformers==4.20.1
+torch==2.1.2