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import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import pickle
# Load the model and tokenizer from Hugging Face Hub
model_name = "peterkros/cofogv1-bert"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Load the label encoder
with open('label_encoder.pkl', 'rb') as file:
label_encoder = pickle.load(file)
def predict(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
predicted_class = torch.argmax(probs, dim=-1).item()
predicted_label = label_encoder.inverse_transform([predicted_class])[0]
return predicted_label
# Define the markdown text with bullet points
markdown_text = """
- Trained with ~1500 rows of data on bert-base-uncased, 110M, English.
- Input one budget line per time.
- Accuracy of the model is ~72%.
"""
# Define the interface
iface = gr.Interface(
fn=predict,
inputs=gr.inputs.Textbox(lines=2, placeholder="Enter Budget line here..."),
outputs="text",
title="COFOG Level 1 Classification",
description=markdown_text # Add the markdown text to the description
)
# Run the interface
if __name__ == "__main__":
iface.launch()
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