import os import sys import time sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ))) # 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 import torch from imports import * import streamlit as st from streamlit_option_menu import option_menu import asyncio import shutil import gc from transformers.utils.hub import TRANSFORMERS_CACHE torch.classes.__path__ = [] 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()) st.set_page_config( page_title="Tachygraphy Microtext Analysis & Normalization", layout="wide" ) import joblib import importlib import importlib.util # sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ))) from emotionMoodtag_analysis.emotion_analysis_main import show_emotion_analysis from sentimentPolarity_analysis.sentiment_analysis_main import show_sentiment_analysis from transformation_and_Normalization.transformationNormalization_main import transform_and_normalize from dashboard import show_dashboard from stacked_stacking_stages.stacking_stages import show_stacking_stages from data_collection_form.data_collector import show_data_collector # from text_transformation import show_text_transformation 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}") if "last_run" not in st.session_state: st.session_state.last_run = time.time() def main(): if "last_run" not in st.session_state: st.session_state.last_run = time.time() if time.time() - st.session_state.last_run > 3600: st.session_state.clear() st.rerun() if "current_page" not in st.session_state: st.session_state.current_page = None # 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: # selected = option_menu("Main Menu", ["Home", 'Settings'], # icons=['house', 'gear'], menu_icon="cast", default_index=1) # selected # # 2. horizontal menu # selected2 = option_menu(None, ["Home", "Upload", "Tasks", 'Settings'], # icons=['house', 'cloud-upload', "list-task", 'gear'], # menu_icon="cast", default_index=0, orientation="horizontal") # selected2 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", "Stacked Stages", "Data Correction & Collection"], icons=['house', 'diagram-3', "snow", 'activity', 'collection', 'database-up'], 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": "left", "margin": "0px", "color": "#000000", "transition": "0.3s", }, "nav-link-selected": { "background-color": "#020045", "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 st.session_state.current_page != selection: st.cache_data.clear() st.cache_resource.clear() free_memory() st.session_state.current_page = selection if selection == "Dashboard": # st.title("Tachygraphy Micro-text Analysis & Normalization") # st.cache_resource.clear() # free_memory() show_dashboard() elif selection == "Stage 1: Sentiment Polarity Analysis": # st.title("Sentiment Polarity Analysis") # st.cache_resource.clear() # free_memory() show_sentiment_analysis() elif selection == "Stage 2: Emotion Mood-tag Analysis": # st.title("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.title("Text Transformation & Normalization") # st.cache_resource.clear() # free_memory() transform_and_normalize() # st.write("This section is under development.") elif selection == "Stacked Stages": # st.title("Stacked Stages") # st.cache_resource.clear() # free_memory() show_stacking_stages() elif selection == "Data Correction & Collection": # st.title("Data Correction & Collection") # st.cache_resource.clear() # free_memory() show_data_collector() # 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()