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import streamlit as st
from transformers.utils.hub import TRANSFORMERS_CACHE
import shutil
import torch
import psutil
import gc
import os
def free_memory():
# """Free up CPU & GPU memory before loading a new model."""
global current_model, current_tokenizer
if current_model is not None:
del current_model # Delete the existing model
current_model = None # Reset reference
if current_tokenizer is not None:
del current_tokenizer # Delete the tokenizer
current_tokenizer = None
gc.collect() # Force garbage collection for CPU memory
if torch.cuda.is_available():
torch.cuda.empty_cache() # Free GPU memory
torch.cuda.ipc_collect() # Clean up PyTorch GPU cache
# If running on CPU, reclaim memory using OS-level commands
try:
if torch.cuda.is_available() is False:
psutil.virtual_memory() # Refresh memory stats
except Exception as e:
print(f"Memory cleanup error: {e}")
# Delete cached Hugging Face models
try:
cache_dir = TRANSFORMERS_CACHE
if os.path.exists(cache_dir):
shutil.rmtree(cache_dir)
print("Cache cleared!")
except Exception as e:
print(f"❌ Cache cleanup error: {e}")
def show_dashboard():
# free_memory()
st.title("Tachygraphy Micro-text Analysis & Normalization")
st.write("""
Welcome to the Tachygraphy Micro-text Analysis & Normalization Project. This application is designed to analyze text data through three stages:
1. Sentiment Polarity Analysis
2. Emotion Mood-tag Analysis
3. Text Transformation & Normalization
""")
def __main__():
show_dashboard() |