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import gc
import psutil
import torch
import shutil
from transformers.utils.hub import TRANSFORMERS_CACHE
import streamlit as st
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), )))
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 create_footer():
st.divider()
# 🛠️ Layout using Streamlit columns
col1, col2, col3 = st.columns([1, 1, 1])
# 🚀 Contributors Section
with col1:
st.markdown("### 🚀 Contributors")
st.write("**Archisman Karmakar**")
st.write("[🔗 LinkedIn](https://www.linkedin.com/in/archismankarmakar/) | [🐙 GitHub](https://www.github.com/ArchismanKarmakar) | [📊 Kaggle](https://www.kaggle.com/archismancoder)")
st.write("**Sumon Chatterjee**")
st.write("[🔗 LinkedIn](https://www.linkedin.com/in/sumon-chatterjee-3b3b43227) | [🐙 GitHub](https://github.com/Sumon670) | [📊 Kaggle](https://www.kaggle.com/sumonchatterjee)")
# 🎓 Mentors Section
with col2:
st.markdown("### 🎓 Mentors")
st.write("**Prof. Anupam Mondal**")
st.write("[🔗 LinkedIn](https://www.linkedin.com/in/anupam-mondal-ph-d-8a7a1a39/) | [📚 Google Scholar](https://scholar.google.com/citations?user=ESRR9o4AAAAJ&hl=en) | [🌐 Website](https://sites.google.com/view/anupammondal/home)")
st.write("**Prof. Sainik Kumar Mahata**")
st.write("[🔗 LinkedIn](https://www.linkedin.com/in/mahatasainikk) | [📚 Google Scholar](https://scholar.google.co.in/citations?user=OcJDM50AAAAJ&hl=en) | [🌐 Website](https://sites.google.com/view/sainik-kumar-mahata/home)")
# 📌 Research Project Info Section
with col3:
st.markdown("### 📝 About the Project")
st.write("This is our research project for our **B.Tech final year** and a **journal** which is yet to be published.")
st.write("Built with 💙 using **Streamlit**.")
# 🚀 Display Footer
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
""")
st.write("""
- Training Source: [GitHub @ Tachygraphy Micro-text Analysis & Normalization](https://github.com/ArchismanKarmakar/Tachygraphy-Microtext-Analysis-And-Normalization)
- Kaggle Collections: [Kaggle @ Tachygraphy Micro-text Analysis & Normalization](https://www.kaggle.com/datasets/archismancoder/dataset-tachygraphy/data?select=Tachygraphy_MicroText-AIO-V3.xlsx)
- Hugging Face Org: [Hugging Face @ Tachygraphy Micro-text Analysis & Normalization](https://huggingface.co/tachygraphy-microtrext-norm-org)
- Deployment: [Streamlit + Hugging Face @ GitHub](https://github.com/ArchismanKarmakar/Tachygraphy-Microtext-Analysis-And-Normalization-Deployment-Source-HuggingFace_Streamlit_JPX14032025)
""")
create_footer()
def __main__():
show_dashboard()
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