Initial Draft

lib/ui/main.py

@@ -0,0 +1,15 @@
+import streamlit as st
+import pandas as pd
+
+def fn_main_configuration(ui_status_message,sm):
+    try:
+        if st.session_state.sv_mapping_file_name is not None:
+            # Excel to Dataframe
+            print(st.session_state.sv_mapping_file_name)
+            lv_excel_df = pd.read_excel(st.session_state.sv_mapping_file_name,sheet_name="Sheet1")
+            st.data_editor(
+                            lv_excel_df,
+                            use_container_width=True
+                          )        
+    except Exception as e:
+        sm.fn_display_status_messages(f"Error: {e}", "Error", ui_status_message)

lib/ui/sidebar.py

@@ -1,110 +1,169 @@
-import subprocess
-import time
-import streamlit as st
-import pandas as pd
-
-
-def fn_process_excel(lv_file_name,ui_status_message,sm):
-    try:
-        # Read the Excel file
-        excel_data = pd.read_excel(lv_file_name,sheet_name=None)
-
-        # Iterate over each sheet in the Excel file
-        for sheet_name, sheet_data in excel_data.items():
-            # Process the data in each sheet
-            print(f"Sheet Name: {sheet_name}")
-            print(sheet_data.head())
-
-        # Display a success message
-        sm.fn_display_status_messages("Excel data processed successfully!", "Success", ui_status_message)
-
-    except Exception as e:
-        # Display an error message if there is an exception
-        sm.fn_display_status_messages(f"Error: {e}", "Error", ui_status_message)    
-
-# Function to start the Flask server
-def start_flask_server():
-    """
-    Function to start the Flask server.
-
-    Returns:
-    None
-    """
-    lv_proc = subprocess.Popen(["python", "flask_app.py"])
-    st.session_state.sv_flask_server_proc = lv_proc
-
-# Function to stop the Flask server
-def stop_flask_server():
-    """
-    Function to stop the Flask server.
-
-    Returns:
-    None
-    """
-
-    lv_proc = st.session_state.sv_flask_server_proc
-    if lv_proc is not None:
-        lv_proc.terminate()
-        lv_proc.wait(timeout=5)
-        if lv_proc.poll() is None:
-            lv_proc.kill()
-        st.session_state.sv_flask_server_proc = None
-
-# Function to configure the sidebar UI elements.
-def fn_sidebar_configuration(ui_status_message,sm):
-    """
-    Function to configure the sidebar UI elements.
-
-    Parameters:
-    - ui_status_message (str): The status message to be display in UI.
-
-    Returns:
-    None
-    """
-
-    try:
-        if not(st.session_state.sv_flask_server_proc):
-            # Start Flask Server
-            start_flask_server()
-
-        # Toggle button to enable/disable training mode
-        ui_training_indicator = st.toggle("Training", value=False)
-
-        # Container to hold the UI elements
-        ui_container = st.container(border=True)
-
-        with ui_container:
-
-            # Training new domain
-            if ui_training_indicator:
-                # File uploader for training file
-                ui_training_file = st.file_uploader("Training File", type=["xlsx"], accept_multiple_files=False, key="training_file")
-
-                # Text input for domain name
-                ui_domain_name = st.text_input("Domain Name")
-
-                # Button to submit the form
-                if st.button("Submit"):
-                    
-                    # Check if the training file is uploaded
-                    if ui_training_file is not None and ui_domain_name != "":
-                        lv_file_name = "storage/"+ui_domain_name+".xlsx"
-
-                        # Saving File
-                        with open(lv_file_name, "wb") as f:
-                            f.write(ui_training_file.getvalue())
-
-                        # Process the Excel file
-                        fn_process_excel(lv_file_name, ui_status_message,sm)
-                    else:
-                        # Display an error message if the training file is not uploaded
-                        sm.fn_display_status_messages("Please upload the training file.", "Error", ui_status_message)
-            
-            # Mapping data to trained domain
-            else:
-                # Selectbox for domain selection
-                ui_training_vector_db = st.selectbox("Domain", ["OFSLL", "OBRL"])
-    
-    except Exception as e:
-        # Display an error message if there is an exception
-        sm.fn_display_status_messages(f"Error: {e}", "Error", ui_status_message)
+import subprocess
+import streamlit as st
+import os
+import pandas as pd
+
+import lib.ui.apiClient.APIClient as APIClient
+
+# Function to process the training Excel file
+def fn_process_training_excel(lv_file_name, ui_domain_name, ui_status_message, sm):
+    """
+    Process the training Excel file and display status messages based on the response.
+
+    Parameters:
+    - lv_file_name (str): The name of the training Excel file.
+    - ui_domain_name (str): The domain name for the vector store.
+    - ui_status_message (str): The UI element to display the status message.
+    - sm (object): An instance of the StatusMessage class.
+
+    Returns:
+    None
+    """
+    try:
+        lv_response = APIClient.fn_create_vector_store(lv_file_name, ui_domain_name)
+
+        if lv_response.status_code == 200:
+            # Display a success message
+            sm.fn_display_status_messages(lv_response.json()['status'], "Success", ui_status_message)
+        else:
+            raise Exception(f"Error: {lv_response.json()['error_message']}")
+    except Exception as e:
+        # Display an error message if there is an exception
+        sm.fn_display_status_messages(f"Error: {e}", "Error", ui_status_message)    
+
+# Function to process the mapping Excel file
+def fn_process_mapping_excel(lv_file_name, ui_source_domain_name, ui_status_message, sm):
+    """
+    Process the mapping Excel file and display status messages based on the response.
+
+    Parameters:
+    - lv_file_name (str): The name of the mapping Excel file.
+    - ui_source_domain_name (str): The source vector store domain name.
+    - ui_status_message (str): The UI element to display the status message.
+    - sm (object): An instance of the StatusMessage class.
+
+    Returns:
+    None
+    """
+    try:
+        lv_response = APIClient.fn_create_data_mapping(lv_file_name, ui_source_domain_name)
+
+        if lv_response.status_code == 200:
+            lv_response_json = lv_response.json()
+            lv_response_pd = pd.DataFrame(lv_response_json)
+            lv_mapping_file_name = 'db/'+ui_source_domain_name+"_"+lv_file_name.split('/')[-1]
+            lv_response_pd.to_excel(lv_mapping_file_name, index=False)
+
+            st.session_state.sv_mapping_file_name = lv_mapping_file_name
+        else:
+            raise Exception(f"Error: {lv_response.json()['error_message']}")
+    except Exception as e:
+        # Display an error message if there is an exception
+        sm.fn_display_status_messages(f"Error: {e}", "Error", ui_status_message) 
+
+# Function to start the Flask server
+@st.cache_resource
+def start_flask_server():
+    """
+    Function to start the Flask server.
+
+    Returns:
+    None
+    """
+    lv_proc = subprocess.Popen(["python", "flask_app.py"])
+    st.session_state.sv_flask_server_proc = lv_proc
+
+# Function to stop the Flask server
+def stop_flask_server():
+    """
+    Function to stop the Flask server.
+
+    Returns:
+    None
+    """
+
+    lv_proc = st.session_state.sv_flask_server_proc
+    if lv_proc is not None:
+        lv_proc.terminate()
+        lv_proc.wait(timeout=5)
+        if lv_proc.poll() is None:
+            lv_proc.kill()
+        st.session_state.sv_flask_server_proc = None
+
+# Function to configure the sidebar UI elements.
+def fn_sidebar_configuration(ui_status_message,sm):
+    """
+    Function to configure the sidebar UI elements.
+
+    Parameters:
+    - ui_status_message (str): The status message to be display in UI.
+
+    Returns:
+    None
+    """
+
+    try:
+        if not(st.session_state.sv_flask_server_proc):
+            # Start Flask Server
+            start_flask_server()
+
+        # Toggle button to enable/disable training mode
+        ui_training_indicator = st.toggle("Train Domain Data", value=False)
+
+        # Container to hold the UI elements
+        ui_container = st.container(border=True)
+
+        with ui_container:
+
+            # Training new domain
+            if ui_training_indicator:
+                # File uploader for training file
+                ui_training_file = st.file_uploader("Training File", type=["xlsx"], accept_multiple_files=False, key="training_file")
+
+                # Text input for domain name
+                ui_domain_name = st.text_input("Domain Name")
+
+                # Button to submit the form
+                if st.button("Submit"):
+                    
+                    # Check if the training file is uploaded
+                    if ui_training_file is not None and ui_domain_name != "":
+                        lv_file_name = "storage/"+ui_domain_name+".xlsx"
+
+                        # Saving File
+                        with open(lv_file_name, "wb") as f:
+                            f.write(ui_training_file.getvalue())
+
+                        # Process the Excel file
+                        fn_process_training_excel(lv_file_name,ui_domain_name,ui_status_message,sm)
+                    else:
+                        # Display an error message if the training file is not uploaded
+                        sm.fn_display_status_messages("Please upload the training file.", "Error", ui_status_message)
+            
+            # Mapping data to trained domain
+            else:
+                
+                # Get all file names that end with ".pak" from the "db" folder
+                lv_trained_domain_name =  [filename[:-len('_index.faiss')] for filename in os.listdir('db') if filename.endswith('_index.faiss')]
+                
+                # Selectbox for domain selection
+                ui_training_vector_db = st.selectbox("Domain", lv_trained_domain_name)
+    
+                # Destination Mapping File 
+                ui_destination_file = st.file_uploader("Destination File", type=["xlsx"], accept_multiple_files=False, key="training_file")
+
+                # Button to submit the form
+                if st.button("Submit"):
+                    if ui_destination_file is not None:
+                        lv_file_name = "storage/"+ui_destination_file.name
+
+                        # Saving File
+                        with open(lv_file_name, "wb") as f:
+                            f.write(ui_destination_file.getvalue())
+                        
+                        # Process the Excel file
+                        fn_process_mapping_excel(lv_file_name,ui_training_vector_db,ui_status_message,sm)
+
+    except Exception as e:
+        # Display an error message if there is an exception
+        sm.fn_display_status_messages(f"Error: {e}", "Error", ui_status_message)