2025.03.21.post1 MAJOR

app_main_hf.py

@@ -41,6 +41,7 @@ import importlib.util
 
 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
 
 
@@ -89,6 +90,10 @@ def free_memory():
 
 
 def main():
+
+    if "current_page" not in st.session_state:
+        st.session_state.current_page = None
+
     # selection = option_menu(
     #     menu_title="Navigation",
     #     options=[
@@ -147,27 +152,34 @@ def main():
     #     # show_text_transformation()
     #     st.write("This section is under development.")
 
-    if selection == "Dashboard":
+
+
+    if st.session_state.current_page != selection:
         st.cache_resource.clear()
         free_memory()
+        st.session_state.current_page = selection
+
+    if selection == "Dashboard":
+        # st.cache_resource.clear()
+        # free_memory()
         show_dashboard()
 
     elif selection == "Stage 1: Sentiment Polarity Analysis":
-        st.cache_resource.clear()
-        free_memory()
+        # st.cache_resource.clear()
+        # free_memory()
         show_sentiment_analysis()
 
     elif selection == "Stage 2: Emotion Mood-tag Analysis":
-        st.cache_resource.clear()
-        free_memory()
+        # 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()
+        # st.cache_resource.clear()
         # free_memory()
-        # show_text_transformation()
-        st.write("This section is under development.")
+        transform_and_normalize()
+        # st.write("This section is under development.")
 
 
 

emotionMoodtag_analysis/emotion_analysis_main.py

@@ -202,6 +202,16 @@ if "disabled" not in st.session_state:
 
 # Enabling Resource caching
 def show_emotion_analysis():
+
+    model_names = list(MODEL_OPTIONS.keys())
+
+    # Check if the stored selected model is valid; if not, reset it
+    if "selected_model" in st.session_state:
+        if st.session_state.selected_model not in model_names:
+            st.session_state.selected_model = model_names[0]
+    else:
+        st.session_state.selected_model = model_names[0]
+
     st.title("Stage 2: Emotion Mood-tag Analysis")
     st.write("This section handles emotion mood-tag analysis.")
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "tachygraphy-microtext-analysis-and-normalization"
-version = "2025.03.20.post1"
+version = "2025.03.21.post1"
 description = ""
 authors = [
     { name = "Archisman Karmakar", email = "[email protected]" },

pyprojectOLD.toml

@@ -1,6 +1,7 @@
 [project]
 name = "tachygraphy-microtext-analysis-and-normalization"
-version = "2025.03.18.post5"
+version = "2025.03.21.post1"
+# version = "2025.03.18.post5"
 # version = "2025.03.18.post4_3"
 # version = "2025.03.18.post3"
 # version = "2025.03.18.post2"

sentimentPolarity_analysis/sentiment_analysis_main.py

@@ -201,6 +201,15 @@ if "disabled" not in st.session_state:
 
 def show_sentiment_analysis():
 
+    model_names = list(MODEL_OPTIONS.keys())
+
+    # Check if the stored selected model is valid; if not, reset it
+    if "selected_model" in st.session_state:
+        if st.session_state.selected_model not in model_names:
+            st.session_state.selected_model = model_names[0]
+    else:
+        st.session_state.selected_model = model_names[0]
+
     st.title("Stage 1: Sentiment Polarity Analysis")
     st.write("This section handles sentiment analysis.")
 

transformation_and_Normalization/config/stage3_models.json

@@ -0,0 +1,17 @@
+{
+    "1": {
+        "name": "Facebook BART Base for Conditional Text Generation",
+        "type": "hf_automodel_finetuned_fbtctg",
+        "module_path": "hmv_cfg_base_stage3.model1",
+        "hf_location": "tachygraphy-microtrext-norm-org/BART-base-HF-Seq2Seq-Trainer-Batch4",
+        "tokenizer_class": "BartTokenizer",
+        "model_class": "BartForConditionalGeneration",
+        "problem_type": "text_transformamtion_and_normalization",
+        "base_model": "facebook/bart-base",
+        "base_model_class": "BartForConditionalGeneration",
+        "device": "cpu",
+        "max_top_k": 50265,
+        "load_function": "load_model",
+        "predict_function": "predict"
+    }
+}

transformation_and_Normalization/hmv_cfg_base_stage3/imports.py

@@ -0,0 +1,25 @@
+import os
+import sys
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), )))
+
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModel
+# import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import plotly.express as px
+import pandas as pd
+import json
+import gc
+import psutil
+import importlib
+import importlib.util
+import asyncio
+# import pytorch_lightning as pl
+
+import safetensors
+from safetensors import load_file, save_file
+import json
+import huggingface_hub
+from huggingface_hub import hf_hub_download

transformation_and_Normalization/hmv_cfg_base_stage3/model1.py

@@ -0,0 +1,117 @@
+from transformers import BartForConditionalGeneration, BartTokenizer, AutoTokenizer, AutoModelForSequenceClassification, AutoModel
+import torch.nn.functional as F
+from imports import *
+import torch.nn as nn
+import torch
+import os
+import sys
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), )))
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+CONFIG_STAGE2 = os.path.join(BASE_DIR, "..", "config", "stage2_models.json")
+
+
+MODEL_OPTIONS = {
+    "1": {
+        "name": "Facebook BART Base for Conditional Text Generation",
+        "type": "hf_automodel_finetuned_fbtctg",
+        "module_path": "hmv_cfg_base_stage3.model1",
+        "hf_location": "tachygraphy-microtrext-norm-org/BART-base-HF-Seq2Seq-Trainer-Batch4",
+        "tokenizer_class": "BartTokenizer",
+        "model_class": "BartForConditionalGeneration",
+        "problem_type": "text_transformamtion_and_normalization",
+        "base_model": "facebook/bart-base",
+        "base_model_class": "BartForConditionalGeneration",
+        "device": "cpu",
+        "max_top_k": 50265,
+        "load_function": "load_model",
+        "predict_function": "predict"
+    }
+}
+
+model_key = "1"
+model_info = MODEL_OPTIONS[model_key]
+hf_location = model_info["hf_location"]
+
+tokenizer_class = globals()[model_info["tokenizer_class"]]
+model_class = globals()[model_info["model_class"]]
+
+
+@st.cache_resource
+def load_model():
+    tokenizer = tokenizer_class.from_pretrained(hf_location)
+    print("Loading model 1")
+    model = model_class.from_pretrained(hf_location,
+                                        device_map=torch.device(
+                                            "cuda" if torch.cuda.is_available() else "cpu")
+                                        )
+    print("Model 1 loaded")
+
+    return model, tokenizer
+
+
+def predict(
+    model, tokenizer, text, device, 
+    num_return_sequences=1, 
+    beams=None,  # Beam search
+    do_sample=False,  # Sampling flag
+    temp=None,  # Temperature (only for sampling)
+    top_p=None, 
+    top_k=None, 
+    max_new_tokens=1024, 
+    early_stopping=True
+):
+    # Tokenize input
+    padded = tokenizer(text, return_tensors='pt', truncation=False, padding=True).to(device)
+    input_ids = padded['input_ids'].to(device)
+    attention_mask = padded['attention_mask'].to(device)
+
+    # Validate arguments
+    if beams is not None and do_sample:
+        raise ValueError("Cannot use `beams` and `do_sample=True` together. Choose either beam search (`beams=5`) or sampling (`do_sample=True, temp=0.7`).")
+    
+    if temp is not None and not do_sample:
+        raise ValueError("`temp` (temperature) can only be used in sampling mode (`do_sample=True`).")
+
+    if (top_p is not None or top_k is not None) and not do_sample:
+        raise ValueError("`top_p` and `top_k` can only be used in sampling mode (`do_sample=True`).")
+
+    # Beam search (Deterministic)
+    if beams is not None:
+        outputs = model.generate(
+            input_ids=input_ids, 
+            attention_mask=attention_mask, 
+            max_new_tokens=max_new_tokens, 
+            num_return_sequences=num_return_sequences, 
+            num_beams=beams, 
+            early_stopping=early_stopping, 
+            do_sample=False  # No randomness
+        )
+
+    # Sampling Cases
+    else:
+        generate_args = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "max_new_tokens": max_new_tokens,
+            "num_return_sequences": num_return_sequences,
+            "do_sample": True,  # Enable stochastic sampling
+            "temperature": temp if temp is not None else 0.7,  # Default temp if not passed
+        }
+
+        # Add `top_p` if set
+        if top_p is not None:
+            generate_args["top_p"] = top_p
+
+        # Add `top_k` if set
+        if top_k is not None:
+            generate_args["top_k"] = top_k
+
+        # Generate
+        outputs = model.generate(**generate_args)
+
+    # Decode predictions into human-readable text
+    predictions = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+    return predictions

transformation_and_Normalization/transformationNormalization_main.py

@@ -0,0 +1,585 @@
+import shutil
+from transformers.utils.hub import TRANSFORMERS_CACHE
+import torch
+import time
+import joblib
+import importlib.util
+from imports import *
+import os
+import sys
+import time
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), )))
+
+# from transformers.utils import move_cache_to_trash
+# from huggingface_hub import delete_cache
+
+
+# from hmv_cfg_base_stage1.model1 import load_model as load_model1
+# from hmv_cfg_base_stage1.model1 import predict as predict1
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+CONFIG_STAGE3 = os.path.join(BASE_DIR, "config", "stage3_models.json")
+LOADERS_STAGE3 = os.path.join(BASE_DIR, "hmv_cfg_base_stage3")
+
+
+EMOTION_MOODTAG_LABELS = [
+    "anger", "disgust", "fear", "joy", "neutral",
+    "sadness", "surprise"
+]
+
+current_model = None
+current_tokenizer = None
+
+
+# Enabling Resource caching
+
+
+# @st.cache_resource
+def load_model_config():
+    with open(CONFIG_STAGE3, "r") as f:
+        model_data = json.load(f)
+
+    # Extract names for dropdown
+    model_options = {v["name"]: v for v in model_data.values()}
+    return model_data, model_options
+
+
+MODEL_DATA, MODEL_OPTIONS = load_model_config()
+
+
+# ✅ Dynamically Import Model Functions
+def import_from_module(module_name, function_name):
+    try:
+        module = importlib.import_module(module_name)
+        return getattr(module, function_name)
+    except (ModuleNotFoundError, AttributeError) as e:
+        st.error(f"❌ Import Error: {e}")
+        return None
+
+
+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 load_selected_model(model_name):
+    global current_model, current_tokenizer
+
+    # st.cache_resource.clear()
+
+    # free_memory()
+
+    # st.write("DEBUG: Available Models:", MODEL_OPTIONS.keys())  # ✅ See available models
+    # st.write("DEBUG: Selected Model:", MODEL_OPTIONS[model_name])  # ✅ Check selected model
+    # st.write("DEBUG: Model Name:", model_name)  # ✅ Check selected model
+
+    if model_name not in MODEL_OPTIONS:
+        st.error(f"⚠️ Model '{model_name}' not found in config!")
+        return None, None, None
+
+    model_info = MODEL_OPTIONS[model_name]
+    hf_location = model_info["hf_location"]
+
+    model_module = model_info["module_path"]
+    load_function = model_info["load_function"]
+    predict_function = model_info["predict_function"]
+
+    load_model_func = import_from_module(model_module, load_function)
+    predict_func = import_from_module(model_module, predict_function)
+
+    if load_model_func is None or predict_func is None:
+        st.error("❌ Model functions could not be loaded!")
+        return None, None, None
+
+    model, tokenizer = load_model_func()
+
+    current_model, current_tokenizer = model, tokenizer
+    return model, tokenizer, predict_func
+
+
+def disable_ui():
+    st.components.v1.html(
+        """
+        <style>
+        #ui-disable-overlay {
+            position: fixed;
+            top: 0;
+            left: 0;
+            width: 100vw;
+            height: 100vh;
+            background-color: rgba(200, 200, 200, 0.5);
+            z-index: 9999;
+        }
+        </style>
+        <div id="ui-disable-overlay"></div>
+        """,
+        height=0,
+        scrolling=False
+    )
+
+
+def enable_ui():
+    st.components.v1.html(
+        """
+        <script>
+        var overlay = document.getElementById("ui-disable-overlay");
+        if (overlay) {
+            overlay.parentNode.removeChild(overlay);
+        }
+        </script>
+        """,
+        height=0,
+        scrolling=False
+    )
+
+# Function to increment progress dynamically
+
+
+def update_progress(progress_bar, start, end, delay=0.1):
+    for i in range(start, end + 1, 5):  # Increment in steps of 5%
+        progress_bar.progress(i)
+        time.sleep(delay)  # Simulate processing time
+        # st.experimental_rerun() # Refresh the page
+
+
+# Function to update session state when model changes
+def on_model_change():
+    st.cache_resource.clear()
+    free_memory()
+    st.session_state.model_changed = True  # Mark model as changed
+
+
+# Function to update session state when text changes
+
+
+def on_text_change():
+    st.session_state.text_changed = True  # Mark text as changed
+
+
+def update_top_k_from_slider():
+    st.session_state.top_k = st.session_state.top_k_slider
+
+
+def update_top_k_from_input():
+    st.session_state.top_k = st.session_state.top_k_input
+
+
+# Initialize session state variables
+if "selected_model" not in st.session_state:
+    st.session_state.selected_model = list(MODEL_OPTIONS.keys())[
+        0]  # Default model
+if "user_input" not in st.session_state:
+    st.session_state.user_input = ""
+if "last_processed_input" not in st.session_state:
+    st.session_state.last_processed_input = ""
+if "model_changed" not in st.session_state:
+    st.session_state.model_changed = False
+if "text_changed" not in st.session_state:
+    st.session_state.text_changed = False
+if "disabled" not in st.session_state:
+    st.session_state.disabled = False
+
+if "top_k" not in st.session_state:
+    st.session_state.top_k = 50
+
+
+if "last_change" not in st.session_state:
+    st.session_state.last_change = time.time()
+if "auto_predict_triggered" not in st.session_state:
+    st.session_state.auto_predict_triggered = False
+
+
+def transform_and_normalize():
+    # No cache clearing here—only in the model change callback!
+
+    # st.write(st.session_state)
+    
+    if "top_k" not in st.session_state:
+        st.session_state.top_k = 50
+
+    model_names = list(MODEL_OPTIONS.keys())
+
+    # Check if the stored selected model is valid; if not, reset it
+    if "selected_model" in st.session_state:
+        if st.session_state.selected_model not in model_names:
+            st.session_state.selected_model = model_names[0]
+    else:
+        st.session_state.selected_model = model_names[0]
+
+    st.title("Stage 3: Text Transformation & Normalization")
+    st.write("This section handles the transformation and normalization of informal text into standard formal English.")
+
+    # Model selection with change detection; clearing cache happens in on_model_change()
+    selected_model = st.selectbox(
+        "Choose a model:", model_names, key="selected_model", on_change=on_model_change
+    )
+
+    # Text input with change detection
+    user_input = st.text_input(
+        "Enter text for emotions mood-tag analysis:", key="user_input", on_change=on_text_change
+    )
+
+    st.markdown("#### Generation Parameters")
+    col1, col2 = st.columns(2)
+
+    with col1:
+        use_beam = st.checkbox("Use Beam Search", value=False)
+        if use_beam:
+            beams = st.number_input("Number of beams:", min_value=1, max_value=10, value=3, step=1)
+            do_sample = False
+            temp = None
+            top_p = None
+            top_k = None
+        else:
+            beams = None
+            do_sample = st.checkbox("Enable Sampling", value=True)
+            temp = st.slider("Temperature:", min_value=0.1, max_value=2.0, value=0.4, step=0.1) if do_sample else None
+
+    with col2:
+        top_p = st.slider("Top-p (nucleus sampling):", min_value=0.0, max_value=1.0, value=0.9, step=0.05) if (not use_beam and do_sample) else None
+        model_config = MODEL_OPTIONS[selected_model]
+        max_top_k = model_config.get("max_top_k", 50)
+        if not use_beam and do_sample:
+            col_slider, col_input = st.columns(2)
+            st.write("Top-K: Top K most probable tokens, recommended range: 10-60")
+            with col_slider:
+                top_k_slider = st.slider(
+                    "Top-k (slider):",
+                    min_value=0,
+                    max_value=max_top_k,
+                    value=st.session_state.top_k,
+                    step=1,
+                    key="top_k_slider",
+                    on_change=update_top_k_from_slider
+                )
+            with col_input:
+                top_k_input = st.number_input(
+                    "Top-k (number input):",
+                    min_value=0,
+                    max_value=max_top_k,
+                    value=st.session_state.top_k,
+                    step=1,
+                    key="top_k_input",
+                    on_change=update_top_k_from_input
+                )
+            final_top_k = st.session_state.top_k
+        else:
+            final_top_k = None
+
+    col_tokens, col_return = st.columns(2)
+    with col_tokens:
+        max_new_tokens = st.number_input("Max New Tokens:", min_value=1, value=1024, step=1)
+        early_stopping = st.checkbox("Early Stopping", value=True)
+    with col_return:
+        if beams is not None:
+            num_return_sequences = st.number_input(
+                "Num Return Sequences:",
+                min_value=1,
+                max_value=beams,
+                value=1,
+                step=1
+            )
+        else:
+            num_return_sequences = st.number_input(
+                "Num Return Sequences:",
+                min_value=1,
+                max_value=3,
+                value=1,
+                step=1
+            )
+        user_input_copy = user_input
+
+    current_time = time.time()
+    if user_input.strip() and (current_time - st.session_state.last_change >= 1.5):
+        st.session_state.last_processed_input = user_input
+        
+        progress_bar = st.progress(0)
+        update_progress(progress_bar, 0, 10)
+        with st.spinner("Predicting..."):
+            model, tokenizer, predict_func = load_selected_model(selected_model)
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            if model is None:
+                st.error("⚠️ Error: Model failed to load!")
+                st.stop()
+            if hasattr(model, "to"):
+                model.to(device)
+            predictions = predict_func(
+                model, tokenizer, user_input, device,
+                num_return_sequences,
+                beams,
+                do_sample,
+                temp,
+                top_p,
+                final_top_k,
+                max_new_tokens,
+                early_stopping
+            )
+        update_progress(progress_bar, 10, 100)
+        
+        if len(predictions) > 1:
+            st.write("### Multiple Predictions:")
+            for i, pred in enumerate(predictions, start=1):
+                st.markdown(f"**Sequence {i}:** {pred}")
+        else:
+            st.write("### Prediction:")
+            st.write(predictions[0])
+        progress_bar.empty()
+    # else:
+    #     st.info("Waiting for input to settle...")
+
+if __name__ == "__main__":
+    transform_and_normalize()
+
+
+
+
+# # Main function to show the app
+# def transform_and_normalize():
+
+#     # st.cache_resource.clear()
+#     # free_memory()
+
+#     if "top_k" not in st.session_state:
+#         st.session_state.top_k = 50
+
+#     model_names = list(MODEL_OPTIONS.keys())
+
+#     # Check if the stored selected model is valid; if not, reset it
+#     if "selected_model" in st.session_state:
+#         if st.session_state.selected_model not in model_names:
+#             st.session_state.selected_model = model_names[0]
+#     else:
+#         st.session_state.selected_model = model_names[0]
+
+#     st.title("Stage 3: Text Transformation & Normalization")
+#     st.write("This section handles the transformation and normalization of informal text containing short-hands (microtexts), abbreviations, acronyms, slangs, multilingual conversational text etc. into readable, understandable standard formal English.")
+
+#     # Model selection with change detection
+#     selected_model = st.selectbox(
+#         "Choose a model:", model_names, key="selected_model", on_change=on_model_change
+#     )
+
+#     # Text input with change detection
+#     user_input = st.text_input(
+#         "Enter text for emotions mood-tag analysis:", key="user_input", on_change=on_text_change
+#     )
+
+#     st.markdown("#### Generation Parameters")
+#     col1, col2 = st.columns(2)
+
+#     with col1:
+#         use_beam = st.checkbox("Use Beam Search", value=False)
+#         if use_beam:
+#             beams = st.number_input(
+#                 "Number of beams:", min_value=1, value=5, step=1)
+#             do_sample = False
+#             temp = None
+#             top_p = None
+#             top_k = None
+#         else:
+#             beams = None
+#             do_sample = st.checkbox("Enable Sampling", value=True)
+#             temp = st.slider("Temperature:", min_value=0.1, max_value=2.0,
+#                              value=0.7, step=0.1) if do_sample else None
+
+#     with col2:
+#         top_p = st.slider("Top-p (nucleus sampling):", min_value=0.0, max_value=1.0,
+#                           value=0.9, step=0.05) if not use_beam and do_sample else None
+#         model_config = MODEL_OPTIONS[selected_model]
+#         max_top_k = model_config.get("max_top_k", 50)
+#         # top_k = st.number_input("Top-k:", min_value=0, value=50, step=1) if not use_beam and do_sample else None
+#         # top_k = st.slider("Top-k:", min_value=0, max_value=max_top_k, value=50, step=1) if (not use_beam and do_sample) else None
+
+#         if not use_beam and do_sample:
+
+#             col_slider, col_input = st.columns(2)
+
+#             with col_slider:
+#                 top_k_slider = st.slider(
+#                     "Top-k (slider):",
+#                     min_value=0,
+#                     max_value=max_top_k,
+#                     value=st.session_state.top_k,
+#                     step=1,
+#                     key="top_k_slider",
+#                     on_change=update_top_k_from_slider
+#                 )
+#             with col_input:
+#                 top_k_input = st.number_input(
+#                     "Top-k (number input):",
+#                     min_value=0,
+#                     max_value=max_top_k,
+#                     value=st.session_state.top_k,
+#                     step=1,
+#                     key="top_k_input",
+#                     on_change=update_top_k_from_input
+#                 )
+#             final_top_k = st.session_state.top_k
+#         else:
+#             final_top_k = None
+
+#     # max_new_tokens = st.number_input("Max New Tokens:", min_value=1, value=1024, step=1)
+#     # early_stopping = st.checkbox("Early Stopping", value=True)
+#     # num_return_sequences = st.number_input("Num Return Sequences:", min_value=1, value=1, step=1)
+
+#     col_tokens, col_return = st.columns(2)
+
+#     with col_tokens:
+#         max_new_tokens = st.number_input(
+#             "Max New Tokens:", min_value=1, value=1024, step=1)
+#         early_stopping = st.checkbox("Early Stopping", value=True)
+
+#     with col_return:
+#         if beams is not None:
+#             num_return_sequences = st.number_input(
+#                 "Num Return Sequences:",
+#                 min_value=1,
+#                 max_value=beams,
+#                 value=1,
+#                 step=1
+#             )
+#         else:
+#             num_return_sequences = st.number_input(
+#                 "Num Return Sequences:",
+#                 min_value=1,
+#                 max_value=3,
+#                 value=1,
+#                 step=1
+#             )
+
+#         user_input_copy = user_input
+
+
+
+#     current_time = time.time()
+#     if user_input.strip() and (current_time - st.session_state.last_change >= 1.5):
+#         # Reset change flag (if needed)
+#         st.session_state.last_processed_input = user_input
+        
+#         progress_bar = st.progress(0)
+#         update_progress(progress_bar, 0, 10)
+#         with st.spinner("Predicting..."):
+#             model, tokenizer, predict_func = load_selected_model(selected_model)
+#             device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#             if model is None:
+#                 st.error("⚠️ Error: Model failed to load!")
+#                 st.stop()
+#             if hasattr(model, "to"):
+#                 model.to(device)
+#             predictions = predict_func(
+#                 model, tokenizer, user_input, device,
+#                 num_return_sequences,
+#                 beams,
+#                 do_sample,
+#                 temp,
+#                 top_p,
+#                 final_top_k,
+#                 max_new_tokens,
+#                 early_stopping
+#             )
+#         update_progress(progress_bar, 10, 100)
+        
+#         if len(predictions) > 1:
+#             st.write("### Multiple Predictions:")
+#             for i, pred in enumerate(predictions, start=1):
+#                 st.markdown(f"**Sequence {i}:** {pred}")
+#         else:
+#             st.write("### Prediction:")
+#             st.write(predictions[0])
+#         progress_bar.empty()
+#     else:
+#         st.info("Waiting for input to settle...")
+
+    # Only run inference if:
+    # 1. The text is NOT empty
+    # 2. The text has changed OR the model has changed
+    # auto_predict = False
+    # if user_input.strip():
+    #     if (user_input != st.session_state.last_processed_input) or st.session_state.model_changed:
+    #         auto_predict = True
+
+    # if auto_predict:
+    #     run_inference = True
+    # else:
+    #     run_inference = st.button("Run Prediction")
+
+    # if run_inference and user_input.strip():
+    #     # Reset change flags and update last processed input
+    #     st.session_state.last_processed_input = user_input
+    #     st.session_state.model_changed = False
+    #     st.session_state.text_changed = False
+
+    #     progress_bar = st.progress(0)
+    #     update_progress(progress_bar, 0, 10)
+
+    #     with st.spinner("Please wait..."):
+    #         model, tokenizer, predict_func = load_selected_model(selected_model)
+    #         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    #         if model is None:
+    #             st.error("⚠️ Error: Model failed to load! Check model selection or configuration.")
+    #             st.stop()
+    #         if hasattr(model, "to"):
+    #             model.to(device)
+
+    #         predictions = predict_func(
+    #             model, tokenizer, user_input, device,
+    #             num_return_sequences,
+    #             beams,
+    #             do_sample,
+    #             temp,
+    #             top_p,
+    #             final_top_k,
+    #             max_new_tokens,
+    #             early_stopping
+    #         )
+    #     update_progress(progress_bar, 10, 100)
+
+    #     if len(predictions) > 1:
+    #         st.write("### Multiple Predicted Transformed & Normalized Texts:")
+    #         for i, pred in enumerate(predictions, start=1):
+    #             st.markdown(f"**Sequence {i}:** {pred}")
+    #     else:
+    #         st.write("### Predicted Transformed & Normalized Text:")
+    #         st.write(predictions[0])
+    #     progress_bar.empty()
+
+
+# if __name__ == "__main__":
+#     # st.cache_resource.clear()
+#     # free_memory()
+#     transform_and_normalize()
+#     # show_dashboard()
+#     # show_emotion_analysis()
+#     # show_sentiment_analysis()
+#     # show_text_transformation()