Delete app.py

app.py

@@ -1,78 +0,0 @@
-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()