Update UI

src/streamlit_app.py

@@ -2,7 +2,8 @@
 # app.py
 
 import os
-# Disable Streamlit file watcher to prevent conflicts with PyTorch
+# CRITICAL FIX: Disable Streamlit's file watcher to prevent conflicts with PyTorch
+# This MUST be the first thing, before any other imports or Streamlit calls
 os.environ["STREAMLIT_SERVER_ENABLE_FILE_WATCHER"] = "false"
 
 import streamlit as st
@@ -12,216 +13,297 @@ from PIL import Image
 import torch
 import torch.nn.functional as F
 import torchvision.transforms as transforms
-import traceback
+import traceback # Ensure this is imported
 
 # Import all necessary configuration values from config.py
-from config import (
-    IMG_HEIGHT, NUM_CLASSES, BLANK_TOKEN, VOCABULARY, BLANK_TOKEN_SYMBOL,
-    TRAIN_CSV_PATH, TEST_CSV_PATH, TRAIN_IMAGES_DIR, TEST_IMAGES_DIR,
-    MODEL_SAVE_PATH, BATCH_SIZE, NUM_EPOCHS
-)
+# Wrap this import in a try-except
+try:
+    from config import (
+        IMG_HEIGHT, NUM_CLASSES, BLANK_TOKEN, VOCABULARY, BLANK_TOKEN_SYMBOL,
+        TRAIN_CSV_PATH, TEST_CSV_PATH, TRAIN_IMAGES_DIR, TEST_IMAGES_DIR,
+        MODEL_SAVE_PATH, BATCH_SIZE, NUM_EPOCHS
+    )
+except Exception as e:
+    st.error(f"FATAL ERROR: Could not load config.py. Please check your config.py file for errors. Details: {e}")
+    st.stop() # Stop the app if config fails to load
 
 # Import classes and functions from data_handler_ocr.py and model_ocr.py
-from data_handler_ocr import CharIndexer, OCRDataset, ocr_collate_fn, load_ocr_dataframes, create_ocr_dataloaders
-from model_ocr import CRNN, train_ocr_model, save_ocr_model, load_ocr_model, ctc_greedy_decode
-from utils_ocr import preprocess_user_image_for_ocr, binarize_image, resize_image_for_ocr, normalize_image_for_model
+# Wrap these imports in a try-except
+try:
+    from data_handler_ocr import CharIndexer, OCRDataset, ocr_collate_fn, load_ocr_dataframes, create_ocr_dataloaders
+    from model_ocr import CRNN, train_ocr_model, save_ocr_model, load_ocr_model, ctc_greedy_decode
+    from utils_ocr import preprocess_user_image_for_ocr, binarize_image, resize_image_for_ocr, normalize_image_for_model
+except Exception as e:
+    st.error(f"FATAL ERROR: Could not load core modules (data_handler_ocr.py, model_ocr.py, utils_ocr.py). Please check these files for errors. Details: {e}")
+    st.stop() # Stop the app if core modules fail to load
 
 
 # --- Global Variables ---
-ocr_model = None
-char_indexer = None 
-training_history = None 
+# Initialize training_history in Streamlit's session state to persist across reruns
+if 'training_history' not in st.session_state:
+    st.session_state.training_history = None
+
+ocr_model = None # Will be initialized by @st.cache_resource
+char_indexer = None # Will be initialized below
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # --- Streamlit App Setup ---
-st.set_page_config(layout="wide", page_title="Handwritten Name OCR App",)
-
+st.set_page_config(layout="wide", page_title="Handwritten Name OCR App")
 
-st.title("📝 Handwritten Name Recognition (OCR) App")
-st.markdown("""
-    This application uses a Convolutional Recurrent Neural Network (CRNN) to perform
-    Optical Character Recognition (OCR) on handwritten names. You can upload an image
-    of a handwritten name for prediction or train a new model using the provided dataset.
-
-    **Note:** Training a robust OCR model can be time-consuming.
-""")
+col1, col2, col3 = st.columns([1, 3, 1])
+with col2:
+    st.title("📝 Handwritten Name Recognition (OCR) App")
 
 # --- Initialize CharIndexer ---
-# This initializes char_indexer once when the script starts
-char_indexer = CharIndexer(vocabulary_string=VOCABULARY, blank_token_symbol=BLANK_TOKEN_SYMBOL)
-
-# --- Model Loading / Initialization ---
-@st.cache_resource # Cache the model to prevent reloading on every rerun
-def get_and_load_ocr_model_cached(num_classes, model_path):
-    """
-    Initializes the OCR model and attempts to load a pre-trained model.
-    If no pre-trained model exists, a new model instance is returned.
-    """
-    model_instance = CRNN(num_classes=num_classes, cnn_output_channels=512, rnn_hidden_size=256, rnn_num_layers=2)
-    
-    if os.path.exists(model_path):
-        st.sidebar.info("Loading pre-trained OCR model...")
-        try:
-            # Load model to CPU first, then move to device
-            model_instance.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-            st.sidebar.success("OCR model loaded successfully!")
-        except Exception as e:
-            st.sidebar.error(f"Error loading model: {e}. A new model will be initialized.")
-            # If loading fails, re-initialize an untrained model
-            model_instance = CRNN(num_classes=num_classes, cnn_output_channels=512, rnn_hidden_size=256, rnn_num_layers=2)
-    else:
-        st.sidebar.warning("No pre-trained OCR model found. Please train a model using the sidebar option.")
-    
-    return model_instance
-
-# Get the model instance and assign it to the global 'ocr_model'
-ocr_model = get_and_load_ocr_model_cached(char_indexer.num_classes, MODEL_SAVE_PATH)
-# Ensure the model is on the correct device for inference
-ocr_model.to(device)
-ocr_model.eval() # Set model to evaluation mode for inference by default
-
-
-# --- Sidebar for Model Training ---
-st.sidebar.header("Train OCR Model")
-st.sidebar.write("Click the button below to start training the OCR model.")
-
-# Progress bar and label for training in the sidebar
-progress_bar_sidebar = st.sidebar.progress(0)
-progress_label_sidebar = st.sidebar.empty()
-
-def update_progress_callback_sidebar(value, text):
-    progress_bar_sidebar.progress(int(value * 100))
-    progress_label_sidebar.text(text)
-
-if st.sidebar.button("📊 Start Training"):
-    progress_bar_sidebar.progress(0)
-    progress_label_sidebar.empty()
-    st.empty()
-
-    if not os.path.exists(TRAIN_CSV_PATH) or not os.path.isdir(TRAIN_IMAGES_DIR):
-        st.sidebar.error(f"Training CSV '{TRAIN_CSV_PATH}' or Images directory '{TRAIN_IMAGES_DIR}' not found!")
-    elif not os.path.exists(TEST_CSV_PATH) or not os.path.isdir(TEST_IMAGES_DIR):
-        st.sidebar.warning(f"Test CSV '{TEST_CSV_PATH}' or Images directory '{TEST_IMAGES_DIR}' not found. "
-                   "Evaluation might be affected or skipped. Please ensure all data paths are correct.")
-    else:
-        st.sidebar.info(f"Training a new CRNN model for {NUM_EPOCHS} epochs. This will take significant time...")
+# Wrap this in a try-except
+try:
+    char_indexer = CharIndexer(vocabulary_string=VOCABULARY, blank_token_symbol=BLANK_TOKEN_SYMBOL)
+except Exception as e:
+    st.error(f"FATAL ERROR: Could not initialize CharIndexer. Check config.py (VOCABULARY, BLANK_TOKEN_SYMBOL) and data_handler_ocr.py (CharIndexer class). Details: {e}")
+    st.stop()
+
+# --- Define Tabs ---
+col1, col2, col3 = st.columns([1, 3, 1])
+with col2:
+    tab1, tab2, tab3 = st.tabs(["Project Description", "Predict Name", "Train & Evaluate"])
+
+# --- Tab 1: Project Description ---
+with tab1:
+    # Use columns for centering content within the tab
+        st.markdown("""
+            This application implements a Handwritten Name Recognition (OCR) system using a Convolutional Recurrent Neural Network (CRNN) built with PyTorch. 
+            Its core aim is to accurately convert handwritten text from images into digital format, providing a user-friendly interface via Streamlit.
         
-        try:
-            train_df, test_df = load_ocr_dataframes(TRAIN_CSV_PATH, TEST_CSV_PATH)
-            st.sidebar.success("Training and Test DataFrames loaded successfully.")
+            Here are some helpful resources related to this project:
+        """)
+        st.markdown("""
+            **[📃 Project Documentation ](https://drive.google.com/file/d/1HBrQT_UnzNLdEsouW9wMk4alAeCsQxZb/view?usp=sharing)**
+                        
+            **[🎞️ Demo Presentation ](https://drive.google.com/drive/folders/1rOmwyTJkDCsU-Wuh-_CzvQ9sdb_ci_kX?usp=sharing)**
+                        
+            **[📚 Dataset (from Kaggle)](https://www.kaggle.com/datasets/landlord/handwriting-recognition)**
+            
+            **[📂 Github Repository ](https://github.com/marianeft/handwritten_name_ocr_app)**
+        """)
+
+# --- Tab 2: Predict Name (Main Content: Prediction Section) ---
+with tab2:
+        st.header("Predict on a New Image")
+        st.markdown("Upload a clear image of a single handwritten name or word for recognition.")
+
+        if ocr_model is None:
+            st.warning("Model not loaded. Please train or load a model in the 'Train & Evaluate' tab before attempting prediction.")
+        else:
+            uploaded_file = st.file_uploader("🖼️ Choose an image...", type=["png", "jpg", "jpeg", "jfif"])
+
+            if uploaded_file is not None:
+                try:
+                    image_pil = Image.open(uploaded_file).convert('L')
+                    st.image(image_pil, caption="Uploaded Image", use_container_width=True) 
+                    st.write("---")
+                    st.write("Processing and Recognizing...")
+
+                    processed_image_tensor = preprocess_user_image_for_ocr(image_pil, IMG_HEIGHT).to(device)
+                    
+                    ocr_model.eval() # Ensure model is in eval mode for prediction
+                    with torch.no_grad():
+                        output = ocr_model(processed_image_tensor)
+                    
+                    predicted_texts = ctc_greedy_decode(output, char_indexer)
+                    predicted_text = predicted_texts[0]
+
+                    st.success(f"Recognized Text: **{predicted_text}**")
+
+                except Exception as e:
+                    st.error(f"Error processing image or recognizing text: {e}")
+                    st.info("💡 **Tips for best results:**\n"
+                            "- Ensure the handwritten text is clear and on a clean background.\n"
+                            "- Only include one name/word per image.\n"
+                            "- The model is trained on specific characters. Unusual symbols might not be recognized.")
+                    st.exception(e)
+
+# --- Tab 3: Train & Evaluate ---
+with tab3:
+    st.header("Model Training and Evaluation")
+    st.markdown("Here you can train a new OCR model or load a pre-trained one.")
+
+        # --- Model Loading / Initialization (Cached) ---
+    @st.cache_resource # Cache the model to prevent reloading on every rerun
+    def get_and_load_ocr_model_cached(num_classes, model_path):
+        """
+        Initializes the OCR model and attempts to load a pre-trained model.
+        If no pre-trained model exists, a new model instance is returned.
+        """
+        model_instance = CRNN(num_classes=num_classes, cnn_output_channels=512, rnn_hidden_size=256, rnn_num_layers=2)
+        
+        if os.path.exists(model_path):
+            st.info("Loading pre-trained OCR model...") 
+            try:
+                # Load model to CPU first, then move to device
+                model_instance.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+                st.success("OCR model loaded successfully!") 
+            except Exception as e:
+                st.error(f"Error loading model from '{model_path}': {e}. A new model will be initialized.") 
+                # If loading fails, re-initialize an untrained model
+                model_instance = CRNN(num_classes=num_classes, cnn_output_channels=512, rnn_hidden_size=256, rnn_num_layers=2)
+        else:
+            st.warning("No pre-trained OCR model found. Please train a model.") 
+        
+        return model_instance
 
-            st.sidebar.success(f"CharIndexer initialized with {char_indexer.num_classes} classes.")
+    # Wrap model loading in a try-except
+    try:
+        ocr_model = get_and_load_ocr_model_cached(char_indexer.num_classes, MODEL_SAVE_PATH)
+        ocr_model.to(device)
+        ocr_model.eval() # Set model to evaluation mode for inference by default
+    except Exception as e:
+        st.error(f"FATAL ERROR: Could not initialize or load OCR model. Check model_ocr.py (CRNN class) or your saved model file. Details: {e}")
+        st.stop()
 
-            train_loader, test_loader = create_ocr_dataloaders(train_df, test_df, char_indexer, BATCH_SIZE)
-            st.sidebar.success("DataLoaders created successfully.")
-            
-            ocr_model.train() 
-
-            st.sidebar.write("Training in progress... This may take a while.")
-            ocr_model, training_history = train_ocr_model(
-                model=ocr_model,
-                train_loader=train_loader,
-                test_loader=test_loader,
-                char_indexer=char_indexer,
-                epochs=NUM_EPOCHS,
-                device=device,
-                progress_callback=update_progress_callback_sidebar 
-            )
-            st.sidebar.success("OCR model training finished!")
-            update_progress_callback_sidebar(1.0, "Training complete!")
-
-            os.makedirs(os.path.dirname(MODEL_SAVE_PATH), exist_ok=True)
-            save_ocr_model(ocr_model, MODEL_SAVE_PATH) 
-            st.sidebar.success(f"Trained model saved to `{MODEL_SAVE_PATH}`")
-
-        except Exception as e:
-            st.sidebar.error(f"An error occurred during training: {e}")
-            st.exception(e) 
-            update_progress_callback_sidebar(0.0, "Training failed!")
-
-# --- Sidebar for Model Loading ---
-st.sidebar.header("Load Pre-trained Model")
-st.sidebar.write("If you have a saved model, you can load it here instead of training.")
-
-if st.sidebar.button("💾 Load Model"):
-    if os.path.exists(MODEL_SAVE_PATH):
-        try:
-            loaded_model = CRNN(num_classes=char_indexer.num_classes)
-            load_ocr_model(loaded_model, MODEL_SAVE_PATH)
-            loaded_model.to(device)
-            
-            st.sidebar.success(f"Model loaded successfully from `{MODEL_SAVE_PATH}`")
-        except Exception as e:
-            st.sidebar.error(f"Error loading model: {e}")
-            st.exception(e) 
-    else:
-        st.sidebar.warning(f"No model found at `{MODEL_SAVE_PATH}`. Please train a model first or check the path.")
-
-# --- Main Content: Prediction Section and Training History  ---
-
-# Display training history chart
-if training_history:
-    st.subheader("Training History Plots") 
-    history_df = pd.DataFrame({
-        'Epoch': range(1, len(training_history['train_loss']) + 1),
-        'Train Loss': training_history['train_loss'],
-        'Test Loss': training_history['test_loss'],
-        'Test CER (%)': [cer * 100 for cer in training_history['test_cer']],
-        'Test Exact Match Accuracy (%)': [acc * 100 for acc in training_history['test_exact_match_accuracy']]
-    })
-
-    st.markdown("**Loss over Epochs**")
-    st.line_chart(history_df.set_index('Epoch')[['Train Loss', 'Test Loss']])
-    st.caption("Lower loss indicates better model performance.")
-
-    st.markdown("**Character Error Rate (CER) over Epochs**")
-    st.line_chart(history_df.set_index('Epoch')[['Test CER (%)']])
-    st.caption("Lower CER indicates fewer character errors (0% is perfect).")
-
-    st.markdown("**Exact Match Accuracy over Epochs**")
-    st.line_chart(history_df.set_index('Epoch')[['Test Exact Match Accuracy (%)']])
-    st.caption("Higher exact match accuracy indicates more perfectly recognized names.")
-
-    st.markdown("**Performance Metrics over Epochs (CER vs. Exact Match Accuracy)**")
-    st.line_chart(history_df.set_index('Epoch')[['Test CER (%)', 'Test Exact Match Accuracy (%)']])
-    st.caption("CER should decrease, Accuracy should increase.")
-    st.write("---") # Separator after charts
-
-
-# Predict on a New Image
-
-if ocr_model is None:
-    st.warning("Please train or load a model before attempting prediction.")
-else:
-    uploaded_file = st.file_uploader("🖼️ Choose an image...", type=["png", "jpg", "jpeg", "jfif"])
-
-    if uploaded_file is not None:
-        try:
-            image_pil = Image.open(uploaded_file).convert('L')
-            st.image(image_pil, caption="Uploaded Image", use_container_width=True) 
-            st.write("---")
-            st.write("Processing and Recognizing...")
-
-            processed_image_tensor = preprocess_user_image_for_ocr(image_pil, IMG_HEIGHT).to(device)
-            
-            ocr_model.eval()
-            with torch.no_grad():
-                output = ocr_model(processed_image_tensor)
+
+
+        # --- Model Training Section ---
+        st.subheader("1. Train OCR Model")
+        st.write("Click the button below to start training the OCR model.")
+
+        # Progress bar and label for training within this tab
+        progress_container = st.empty() # Container for dynamic messages and progress
+        progress_message_placeholder = st.empty()
+        progress_bar_placeholder = st.progress(0)
+        
+        def update_progress_callback(value, text):
+            progress_bar_placeholder.progress(int(value * 100))
+            progress_message_placeholder.info(text) # Use info for dynamic messages
+
+        if st.button("📊 Start Training"):
+            progress_message_placeholder.empty() # Clear previous messages
+            progress_bar_placeholder.progress(0) # Reset progress bar
+
+            if not os.path.exists(TRAIN_CSV_PATH) or not os.path.isdir(TRAIN_IMAGES_DIR):
+                st.error(f"Training CSV '{TRAIN_CSV_PATH}' or Images directory '{TRAIN_IMAGES_DIR}' not found! Please check file paths and ensure data is uploaded correctly.")
+            elif not os.path.exists(TEST_CSV_PATH) or not os.path.isdir(TEST_IMAGES_DIR):
+                st.warning(f"Test CSV '{TEST_CSV_PATH}' or Images directory '{TEST_IMAGES_DIR}' not found. "
+                        "Evaluation might be affected or skipped. Please ensure all data paths are correct and data is uploaded.")
+            else:
+                progress_message_placeholder.info(f"Training a new CRNN model for {NUM_EPOCHS} epochs. This will take significant time...")
+                
+                try:
+                    train_df, test_df = load_ocr_dataframes(TRAIN_CSV_PATH, TEST_CSV_PATH)
+                    progress_message_placeholder.success("Training and Test DataFrames loaded successfully.")
+                    progress_message_placeholder.info(f"Train DataFrame size: {len(train_df)} samples")
+                    progress_message_placeholder.info(f"Test DataFrame size: {len(test_df)} samples")
+                    if len(test_df) == 0:
+                        progress_message_placeholder.error("ERROR: Test DataFrame is empty! Evaluation cannot proceed. Check TEST_CSV_PATH and TEST_IMAGES_DIR.")
+                    if len(train_df) == 0:
+                        progress_message_placeholder.error("ERROR: Train DataFrame is empty! Training cannot proceed. Check TRAIN_CSV_PATH and TRAIN_IMAGES_DIR.")
+                    
+                    if len(train_df) == 0 or len(test_df) == 0: # Stop if critical data is missing
+                        st.stop() # Added st.stop for critical data missing scenario
+
+                    char_indexer_for_training = CharIndexer(vocabulary_string=VOCABULARY, blank_token_symbol=BLANK_TOKEN_SYMBOL)
+                    progress_message_placeholder.success(f"CharIndexer initialized with {char_indexer_for_training.num_classes} classes.")
+
+                    train_loader, test_loader = create_ocr_dataloaders(train_df, test_df, char_indexer_for_training, BATCH_SIZE)
+                    progress_message_placeholder.success("DataLoaders created successfully.")
+                    
+                    # Re-initialize the model to train from scratch if the button is pressed
+                    # This ensures we don't continue training a potentially already trained model if it was loaded.
+                    ocr_model_for_training = CRNN(num_classes=char_indexer_for_training.num_classes, cnn_output_channels=512, rnn_hidden_size=256, rnn_num_layers=2)
+                    ocr_model_for_training.to(device)
+                    ocr_model_for_training.train() 
+
+                    progress_message_placeholder.write("Training in progress... This may take a while.")
+                    
+                    # Capture the model and history
+                    ocr_model_for_training, history_result = train_ocr_model(
+                        model=ocr_model_for_training,
+                        train_loader=train_loader,
+                        test_loader=test_loader,
+                        char_indexer=char_indexer_for_training,
+                        epochs=NUM_EPOCHS,
+                        device=device,
+                        progress_callback=update_progress_callback
+                    )
+                    
+                    st.session_state.training_history = history_result # Save history to session state
+                    
+                    progress_message_placeholder.success("OCR model training finished!")
+                    update_progress_callback(1.0, "Training complete!")
+
+                    os.makedirs(os.path.dirname(MODEL_SAVE_PATH), exist_ok=True)
+                    save_ocr_model(ocr_model_for_training, MODEL_SAVE_PATH)
+                    progress_message_placeholder.success(f"Trained model saved to `{MODEL_SAVE_PATH}`")
+
+                    ocr_model = ocr_model_for_training 
+                    ocr_model.eval() # Set to eval mode for subsequent predictions
+
+                except Exception as e:
+                    progress_message_placeholder.error(f"An error occurred during training: {e}")
+                    st.exception(e) # This will print a detailed traceback in the Streamlit UI
+                    update_progress_callback(0.0, "Training failed!")
             
-            predicted_texts = ctc_greedy_decode(output, char_indexer)
-            predicted_text = predicted_texts[0]
-
-            st.success(f"Recognized Text: **{predicted_text}**")
-
-        except Exception as e:
-            st.error(f"Error processing image or recognizing text: {e}")
-            st.info("💡 **Tips for best results:**\n"
-                    "- Ensure the handwritten text is clear and on a clean background.\n"
-                    "- Only include one name/word per image.\n"
-                    "- The model is trained on specific characters. Unusual symbols might not be recognized.")
-            st.exception(e)
-
-st.markdown("""
-    ---
-    *Built using Streamlit, PyTorch, OpenCV, and EditDistance ©2025 by MFT*
-    """)
+        st.write("---")
+
+        # --- Model Loading Section ---
+        st.subheader("2. Load Pre-trained Model")
+        st.write("If you have a saved model, you can load it here instead of training.")
+
+        if st.button("💾 Load Model"):
+            if os.path.exists(MODEL_SAVE_PATH):
+                try:
+                    loaded_model_instance = CRNN(num_classes=char_indexer.num_classes, cnn_output_channels=512, rnn_hidden_size=256, rnn_num_layers=2)
+                    load_ocr_model(loaded_model_instance, MODEL_SAVE_PATH)
+                    loaded_model_instance.to(device)
+                    ocr_model = loaded_model_instance
+                    ocr_model.eval() 
+                    st.success(f"Model loaded successfully from `{MODEL_SAVE_PATH}`")
+                    
+                    # If a model is loaded, we can try to re-evaluate it to get history,
+                    # but typically history is stored from a training run.
+                    # For simplicity, we'll assume training history is only stored after a training run.
+                    
+                except Exception as e:
+                    st.error(f"Error loading model: {e}")
+                    st.exception(e)
+            else:
+                st.warning(f"No model found at `{MODEL_SAVE_PATH}`. Please train a model first or check the path.")
+
+        st.write("---")
+
+        # --- Training History Plots Section ---
+        st.subheader("3. Training History Plots")
+        if st.session_state.training_history: # Check if history exists in session state
+            history_df = pd.DataFrame({
+                'Epoch': range(1, len(st.session_state.training_history['train_loss']) + 1),
+                'Train Loss': st.session_state.training_history['train_loss'],
+                'Test Loss': st.session_state.training_history['test_loss'],
+                'Test CER (%)': [cer * 100 for cer in st.session_state.training_history['test_cer']],
+                'Test Exact Match Accuracy (%)': [acc * 100 for acc in st.session_state.training_history['test_exact_match_accuracy']]
+            })
+
+            st.markdown("**Loss over Epochs**")
+            st.line_chart(history_df.set_index('Epoch')[['Train Loss', 'Test Loss']])
+            st.caption("Lower loss indicates better model performance.")
+
+            st.markdown("**Character Error Rate (CER) over Epochs**")
+            st.line_chart(history_df.set_index('Epoch')[['Test CER (%)']])
+            st.caption("Lower CER indicates fewer character errors (0% is perfect).")
+
+            st.markdown("**Exact Match Accuracy over Epochs**")
+            st.line_chart(history_df.set_index('Epoch')[['Test Exact Match Accuracy (%)']])
+            st.caption("Higher exact match accuracy indicates more perfectly recognized names.")
+
+            st.markdown("**Performance Metrics over Epochs (CER vs. Exact Match Accuracy)**")
+            st.line_chart(history_df.set_index('Epoch')[['Test CER (%)', 'Test Exact Match Accuracy (%)']])
+            st.caption("CER should decrease, Accuracy should increase.")
+        else:
+            st.info("Train the model first to see training history plots here.")
+
+
+# --- Final Footer ---
+col1, col2, col3 = st.columns([1, 3, 1])
+with col2:
+    st.markdown("""
+        ---
+        *Built using Streamlit, PyTorch, OpenCV, and EditDistance ©2025 by MFT*
+        """)