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from streamlit_extras.bottom_container import bottom
from streamlit_extras.app_logo import add_logo
from streamlit_extras.add_vertical_space import add_vertical_space
from streamlit_extras.stylable_container import stylable_container
from imports import *
import streamlit as st
from streamlit_option_menu import option_menu
import os
import asyncio
import sys
import shutil
import gc
from transformers.utils.hub import TRANSFORMERS_CACHE
try:
asyncio.get_running_loop()
except RuntimeError:
asyncio.run(asyncio.sleep(0))
if sys.platform == "win32":
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
else:
try:
asyncio.get_running_loop()
except RuntimeError:
asyncio.set_event_loop(asyncio.new_event_loop())
import joblib
import importlib
import importlib.util
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), )))
from emotion_analysis import show_emotion_analysis
from sentiment_analysis.sentiment_analysis_main import show_sentiment_analysis
from dashboard import show_dashboard
# from text_transformation import show_text_transformation
st.set_page_config(
page_title="Tachygraphy Microtext Analysis & Normalization",
# layout="wide"
)
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 main():
# selection = option_menu(
# menu_title="Navigation",
# options=[
# "Dashboard",
# "Stage 1: Sentiment Polarity Analysis",
# "Stage 2: Emotion Mood-tag Analysis",
# "Stage 3: Text Transformation & Normalization"
# ],
# icons=["joystick", "bar-chart", "emoji-laughing", "pencil"],
# styles={
# "container": {}},
# menu_icon="menu-button-wide-fill",
# default_index=0,
# orientation="horizontal"
# )
st.sidebar.title("Navigation")
with st.sidebar:
selection = option_menu(
menu_title=None, # No title for a sleek look
options=["Dashboard", "Stage 1: Sentiment Polarity Analysis", "Stage 2: Emotion Mood-tag Analysis", "Stage 3: Text Transformation & Normalization"],
icons=None,
menu_icon="cast", # Main menu icon
default_index=0, # Highlight the first option
orientation="vertical",
styles={
"container": {"padding": "0!important", "background-color": "#f8f9fa"},
"icon": {"color": "#6c757d", "font-size": "18px"},
"nav-link": {
"font-size": "16px",
"text-align": "center",
"margin": "0px",
"color": "#6c757d",
"transition": "0.3s",
},
"nav-link-selected": {
"background-color": "#FF4B4B",
"color": "white",
"font-weight": "bold",
"border-radius": "8px",
},
}
)
# st.sidebar.title("Navigation")
# selection = st.sidebar.radio("Go to", ["Dashboard", "Stage 1: Sentiment Polarity Analysis", "Stage 2: Emotion Mood-tag Analysis", "Stage 3: Text Transformation & Normalization"])
# if selection == "Dashboard":
# show_dashboard()
# elif selection == "Stage 1: Sentiment Polarity Analysis":
# show_sentiment_analysis()
# elif selection == "Stage 2: Emotion Mood-tag Analysis":
# # show_emotion_analysis()
# st.write("This section is under development.")
# elif selection == "Stage 3: Text Transformation & Normalization":
# # show_text_transformation()
# st.write("This section is under development.")
if selection == "Dashboard":
st.cache_resource.clear()
# free_memory()
show_dashboard()
elif selection == "Stage 1: Sentiment Polarity Analysis":
st.cache_resource.clear()
# free_memory()
show_sentiment_analysis()
elif selection == "Stage 2: Emotion Mood-tag Analysis":
st.cache_resource.clear()
# free_memory()
# show_emotion_analysis()
st.write("This section is under development.")
elif selection == "Stage 3: Text Transformation & Normalization":
st.cache_resource.clear()
# free_memory()
# show_text_transformation()
st.write("This section is under development.")
# st.sidebar.title("Navigation")
# selection = st.sidebar.radio("Go to", ["Dashboard", "Stage 1: Sentiment Polarity Analysis", "Stage 2: Emotion Mood-tag Analysis", "Stage 3: Text Transformation & Normalization"])
# if selection == "Dashboard":
# show_dashboard()
# elif selection == "Stage 1: Sentiment Polarity Analysis":
# show_sentiment_analysis()
# elif selection == "Stage 2: Emotion Mood-tag Analysis":
# # show_emotion_analysis()
# st.write("This section is under development.")
# elif selection == "Stage 3: Text Transformation & Normalization":
# # show_text_transformation()
# st.write("This section is under development.")
st.sidebar.title("About")
st.sidebar.info("""
**Contributors:**
- Archisman Karmakar
- [LinkedIn](https://www.linkedin.com/in/archismankarmakar/)
- [GitHub](https://www.github.com/ArchismanKarmakar)
- [Kaggle](https://www.kaggle.com/archismancoder)
- Sumon Chatterjee
- [LinkedIn](https://www.linkedin.com/in/sumon-chatterjee-3b3b43227)
- [GitHub](https://github.com/Sumon670)
- [Kaggle](https://www.kaggle.com/sumonchatterjee)
**Mentors:**
- Prof. Anupam Mondal
- [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)
- Prof. Sainik Kumar Mahata
- [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)
This is our research project for our B.Tech final year and a journal which is yet to be published.
""")
if __name__ == "__main__":
main()
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