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import gradio as gr | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
import torch | |
# Load the pre-trained model and tokenizer | |
tokenizer = AutoTokenizer.from_pretrained("pparasurama/raceBERT-ethnicity") | |
model = AutoModelForSequenceClassification.from_pretrained("pparasurama/raceBERT-ethnicity") | |
# Mapping of model output IDs to ethnicity labels | |
id2label = { | |
0: "GreaterEuropean,British", | |
1: "GreaterEuropean,WestEuropean,French", | |
2: "GreaterEuropean,WestEuropean,Italian", | |
3: "GreaterEuropean,WestEuropean,Hispanic", | |
4: "GreaterEuropean,Jewish", | |
5: "GreaterEuropean,EastEuropean", | |
6: "Asian,IndianSubContinent", | |
7: "Asian,GreaterEastAsian,Japanese", | |
8: "GreaterAfrican,Muslim", | |
9: "Asian,GreaterEastAsian,EastAsian", | |
10: "GreaterEuropean,WestEuropean,Nordic", | |
11: "GreaterEuropean,WestEuropean,Germanic", | |
12: "GreaterAfrican,Africans" | |
} | |
# Function to make predictions based on the input name | |
def predict_ethnicity(name): | |
inputs = tokenizer(name, return_tensors="pt") | |
with torch.no_grad(): | |
outputs = model(**inputs) | |
logits = outputs.logits | |
probabilities = torch.softmax(logits, dim=1)[0] | |
# Get top 5 predictions | |
top_preds = torch.topk(probabilities, 5) | |
# Prepare the output as a sorted human-friendly list | |
result = "\n".join([f"{id2label[idx.item()]}: {prob.item() * 100:.2f}%" for idx, prob in zip(top_preds.indices, top_preds.values)]) | |
return result | |
# Gradio Interface | |
interface = gr.Interface( | |
fn=predict_ethnicity, | |
inputs=gr.Textbox(lines=1, placeholder="Enter a name"), | |
outputs="text", | |
title="TOPS Infosolutions Ethnicity Predictor - Kaleida", | |
description="Enter a person's name and get the predicted ethnicity breakdown.", | |
) | |
# Launch the Gradio app | |
interface.launch(auth=("kaleida", "kaleida@1234"), share=True) | |