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app.py

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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()

label_encoder_query_classifier.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a3aa7e324c30cf231a4d1024b079027fd0b757d04bce5b50aa49d02d55d4841d
+size 561

mlp_query_classifier.joblib

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+oid sha256:b6451080153abeeb9a38e3030d68f426d5d939fec81bf1dd0de8e6a3f54249d8
+size 5723034

requirements.txt

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+gradio
+torch
+transformers
+scikit-learn
+joblib
+numpy

scaler_query_classifier.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:02355072620b8db29e0397b73264905b25c0b3d6d7aba8f8d68e5f2335f94791
+size 31575