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import gradio as gr
from utils import get_kmers # ensure this is accessible for joblib
import joblib
import pandas as pd
# 🔹 Load trained pipeline & label encoder
pipeline = joblib.load("models/gradient_boosting_pipeline.pkl")
label_encoder = joblib.load("models/label_encoder.pkl")
idx_to_label = {
0: "BIOLOGICAL_REGION",
1: "OTHER",
2: "PROTEIN_CODING",
3: "PSEUDO",
4: "ncRNA",
5: "rRNA",
6: "scRNA",
7: "snRNA",
8: "snoRNA",
9: "tRNA"
}
def predict_gene(sequence, description):
"""
Run prediction for a single sample (called from UI).
"""
data = {
"NucleotideSequence": sequence,
"GeneGroupMethod": 'NCBI Ortholog',
"Description": description,
"SequenceLength": int(len(sequence)),
}
df = pd.DataFrame([data])
pred = pipeline.predict(df)[0]
return idx_to_label.get(pred, "Unknown")
def build_ui():
with gr.Blocks() as demo:
gr.Markdown("# 🧬 Gene Type Classifier")
gr.Markdown("Enter gene details below and get predictions:")
sequence = gr.Textbox(label="Nucleotide Sequence", placeholder="Enter sequence...")
description = gr.Textbox(label="Description", placeholder="Enter description...")
output = gr.Textbox(label="Prediction Result")
gr.Button("Predict").click(
predict_gene,
inputs=[sequence, description],
outputs=output
)
return demo
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