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import gradio as gr
import torch
import joblib
import numpy as np
from transformers import BertTokenizer, BertModel
# ----------------- 1. Setup Device -----------------
# HF Spaces (Free) usually runs on CPU, but this keeps it robust
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")
# ----------------- 2. Load BERT -----------------
print("Loading BERT model...")
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
bert_model = BertModel.from_pretrained('bert-base-uncased')
bert_model.to(device)
bert_model.eval()
# ----------------- 3. Load MLP + Scaler + LabelEncoder -----------------
# Ensure these files are uploaded to your HF Space Files tab!
print("Loading classification components...")
try:
mlp = joblib.load("mlp_query_classifier.joblib")
scaler = joblib.load("scaler_query_classifier.joblib")
le = joblib.load("label_encoder_query_classifier.joblib")
print("Loaded MLP, scaler, and label encoder.")
except FileNotFoundError as e:
print(f"Error: {e}. Please make sure you uploaded the .joblib files to the Space.")
# ----------------- 4. Embedding Function -----------------
def get_bert_embeddings(text_list):
inputs = tokenizer(
text_list,
padding=True,
truncation=True,
max_length=128,
return_tensors="pt"
).to(device)
with torch.no_grad():
outputs = bert_model(**inputs)
cls_embeddings = outputs.last_hidden_state[:, 0, :]
return cls_embeddings.cpu().numpy()
# ----------------- 5. Prediction Function -----------------
def predict_new_query(text):
# 1) BERT embedding
embedding = get_bert_embeddings([text])
# 2) scale with same scaler as training
embedding_scaled = scaler.transform(embedding)
# 3) MLP prediction -> class index
prediction_index = mlp.predict(embedding_scaled)[0]
# 4) map index back to string label
label = le.inverse_transform([prediction_index])[0]
# Optional: Get probability if your MLP supports it
try:
probs = mlp.predict_proba(embedding_scaled)[0]
confidence = np.max(probs)
return f"Label: {label} (Confidence: {confidence:.2f})"
except:
return f"Label: {label}"
# ----------------- 6. Launch Gradio Interface -----------------
# This creates the web UI
iface = gr.Interface(
fn=predict_new_query,
inputs=gr.Textbox(lines=2, placeholder="Enter your query here..."),
outputs="text",
title="BERT Query Classifier",
description="Enter a text query to classify it using the custom BERT+MLP model."
)
iface.launch() |