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from utils.model import BiLSTMAttentionBERT, BiLSTMConfig
import torch
from transformers import AutoTokenizer, AutoModel
from sklearn.preprocessing import LabelEncoder
import numpy as np
import streamlit as st
import requests
from huggingface_hub import hf_hub_download
def load_model_for_prediction():
try:
st.write("Starting model loading...")
# Initialize BERT first
bert = AutoModel.from_pretrained('dmis-lab/biobert-base-cased-v1.2')
# Initialize config and model
config = BiLSTMConfig(
hidden_dim=128,
num_classes=22,
num_layers=2,
dropout=0.5
)
model = BiLSTMAttentionBERT(config)
model.bert = bert # Set pre-trained BERT
# Load custom layers from checkpoint
model_path = hf_hub_download(
repo_id="joko333/BiLSTM_v01",
filename="model_epoch8_acc72.53.pt"
)
checkpoint = torch.load(model_path, map_location='cpu')
# Debug checkpoint structure
st.write("Checkpoint keys:", checkpoint.keys())
if 'model_state_dict' in checkpoint:
# Extract only custom layer weights
custom_state_dict = {}
state_dict = checkpoint['model_state_dict']
for key, value in state_dict.items():
if not key.startswith('bert.'):
custom_state_dict[key] = value
# Load custom layers
model.load_state_dict(custom_state_dict, strict=False)
st.write("Model loaded successfully")
else:
st.error("Invalid checkpoint format")
return None, None, None
# Initialize label encoder from checkpoint
label_encoder = LabelEncoder()
if 'label_encoder_classes' in checkpoint:
label_encoder.classes_ = checkpoint['label_encoder_classes']
else:
st.error("Label encoder data not found in checkpoint")
return None, None, None
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained('dmis-lab/biobert-base-cased-v1.2')
return model, label_encoder, tokenizer
except Exception as e:
st.error(f"Error loading model: {str(e)}")
return None, None, None
def predict_sentence(model, sentence, tokenizer, label_encoder):
"""
Make prediction for a single sentence with label validation.
"""
import time
start_time = time.time()
# Validation checks
st.write("🔄 Starting prediction process...")
if model is None:
st.error("Error: Model not loaded")
return "Error: Model not loaded", 0.0
if tokenizer is None:
st.error("Error: Tokenizer not loaded")
return "Error: Tokenizer not loaded", 0.0
if label_encoder is None:
st.error("Error: Label encoder not loaded")
return "Error: Label encoder not loaded", 0.0
# Force CPU device
st.write("⚙️ Preparing model...")
device = torch.device('cpu')
model = model.to(device)
model.eval()
# Tokenize
try:
st.write(f"📝 Processing text: {sentence[:50]}...")
encoding = tokenizer(
sentence,
add_special_tokens=True,
max_length=512,
padding='max_length',
truncation=True,
return_tensors='pt'
).to(device)
st.write("🤖 Running model inference...")
with torch.no_grad():
outputs = model(encoding['input_ids'], encoding['attention_mask'])
probabilities = torch.softmax(outputs, dim=1)
prob, pred_idx = torch.max(probabilities, dim=1)
predicted_label = label_encoder.classes_[pred_idx.item()]
elapsed_time = time.time() - start_time
st.write(f"✅ Prediction completed in {elapsed_time:.2f} seconds")
return predicted_label, prob.item()
except Exception as e:
st.error(f"❌ Prediction error: {str(e)}")
return f"Error: {str(e)}", 0.0
def print_labels(label_encoder, show_counts=False):
"""Print all labels and their corresponding indices"""
print("\nAvailable labels:")
print("-" * 40)
for idx, label in enumerate(label_encoder.classes_):
print(f"Index {idx}: {label}")
print("-" * 40)
print(f"Total number of classes: {len(label_encoder.classes_)}\n")
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