Create app.py

app.py

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+import torch
+import numpy as np
+import pickle
+from transformers import AutoTokenizer, AutoModel
+from normalizer import normalize
+import gradio as gr
+
+# --- Device ---
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# --- Load tokenizers and models ---
+bert_tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglabert")
+titu_tokenizer = AutoTokenizer.from_pretrained("hishab/titulm-llama-3.2-1b-v1.0")
+bert_model = AutoModel.from_pretrained("csebuetnlp/banglabert").to(device).eval()
+titu_model = AutoModel.from_pretrained("hishab/titulm-llama-3.2-1b-v1.0").to(device).eval()
+
+# --- Load trained LightGBM model and preprocessing info ---
+with open("multiclass_lightgbm_bert_titu.pkl", "rb") as f:
+    classifier = pickle.load(f)
+
+with open("preprocessing_info.pkl", "rb") as f:
+    info = pickle.load(f)
+class_names = info['class_names']
+bert_max_len = 45
+titu_max_len = 148
+
+# --- Helper functions ---
+def preprocess(text):
+    return normalize(text)
+
+def get_embedding(text, tokenizer, model, max_len):
+    enc = tokenizer([text], return_tensors="pt", padding=True, truncation=True, max_length=max_len).to(device)
+    with torch.inference_mode():
+        out = model(**enc)
+    last_hidden = out.last_hidden_state
+    attn = enc.get("attention_mask", None)
+    if attn is not None:
+        attn = attn.unsqueeze(-1)
+        emb = (last_hidden * attn).sum(dim=1) / attn.sum(dim=1).clamp(min=1e-6)
+    else:
+        emb = last_hidden.mean(dim=1)
+    return emb.detach().cpu().numpy()
+
+def predict(text):
+    text = preprocess(text)
+    bert_emb = get_embedding(text, bert_tokenizer, bert_model, bert_max_len)
+    titu_emb = get_embedding(text, titu_tokenizer, titu_model, titu_max_len)
+    features = np.concatenate([bert_emb, titu_emb], axis=1)
+    pred_idx = classifier.predict(features)[0]
+    return class_names[pred_idx]
+
+# --- Gradio interface ---
+iface = gr.Interface(
+    fn=predict, 
+    inputs=gr.Textbox(label="Enter Bangla text"),
+    outputs=gr.Textbox(label="Predicted Trait"),
+    title="Bangla Personality Trait Predictor",
+    description="Enter Bangla text and get the predicted personality trait."
+)
+
+iface.launch()