(CONFIG/NAME)[Implement Upload_and_Run.py and Update References]: Change landing page name `app.py` → `Upload_and_Run.py` for main application logic and update README and Dockerfile to reflect new entry point.

Dockerfile

@@ -18,4 +18,4 @@ EXPOSE 8501
 
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
 
-ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+ENTRYPOINT ["streamlit", "run", "Upload_and_Run.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

@@ -4,7 +4,7 @@ emoji: 🔬
 colorFrom: indigo
 colorTo: yellow
 sdk: streamlit
-app_file: app.py
+app_file: Upload_and_Run.py
 pinned: false
 license: apache-2.0
 ---
@@ -106,7 +106,7 @@ _Resources, Conservation & Recycling_, **188**, 106718.
 
 **The system is built on a modular, production-ready architecture designed for scalability and maintainability.**
 
-- **Frontend**: Streamlit-based web application (`app.py`) with interactive, multi-tab UI.
+- **Frontend**: Streamlit-based web application (`Upload_and_Run.py`) with interactive, multi-tab UI.
 - **Backend**: PyTorch for deep learning operations including model loading and inference.
 - **Model Management**: Registry pattern (`models/registry.py`) for dynamic model loading and easy integration of new architectures.
 - **Data Processing**: Modality-aware preprocessing pipeline (`utils/preprocessing.py`) for data integrity and standardization (Raman & FTIR).

Upload_and_Run.py

@@ -0,0 +1,72 @@
+# In Upload_and_Run.py
+import streamlit as st
+
+from modules.callbacks import init_session_state
+
+from modules.ui_components import (
+    render_sidebar,
+    render_results_column,
+    render_input_column,
+    render_comparison_tab,
+    render_performance_tab,
+    load_css,
+)
+
+from modules.training_ui import render_training_tab
+
+from utils.image_processing import render_image_upload_interface
+
+st.set_page_config(
+    page_title="ML Polymer Classification",
+    page_icon="🔬",
+    layout="wide",
+    initial_sidebar_state="expanded",
+    menu_items=None,
+)
+
+
+def main():
+    """Modularized main content to other scripts to clean the main app"""
+    load_css("static/style.css")
+    init_session_state()
+
+    render_sidebar()
+
+    # Create main tabs for different analysis modes
+    tab1, tab2, tab3, tab4, tab5 = st.tabs(
+        [
+            "Standard Analysis",
+            "Model Comparison",
+            "Model Training",
+            "Image Analysis",
+            "Performance Tracking",
+        ]
+    )
+
+    with tab1:
+        # Standard single-model analysis
+        col1, col2 = st.columns([1, 1.35], gap="small")
+        with col1:
+            render_input_column()
+        with col2:
+            render_results_column()
+
+    with tab2:
+        # Multi-model comparison interface
+        render_comparison_tab()
+
+    with tab3:
+        # Model training interface
+        render_training_tab()
+
+    with tab4:
+        # Image analysis interface
+        render_image_upload_interface()
+
+    with tab5:
+        # Performance tracking interface
+        render_performance_tab()
+
+
+if __name__ == "__main__":
+    main()