better chat interface

frontend/.streamlit/config.toml

@@ -1,2 +1,11 @@
 [server]
-headless = true
+headless = true
+
+# Max size, in megabytes, for files uploaded with the file_uploader.
+# Default: 200
+maxUploadSize = 200
+
+# Max size, in megabytes, of messages that can be sent via the WebSocket
+# connection.
+# Default: 200
+maxMessageSize = 3500

frontend/ui.py

@@ -1,9 +1,6 @@
-import json
 from pathlib import Path
 
-import pandas as pd
 import streamlit as st
-from streamlit_feedback import streamlit_feedback
 from ui_utils import *
 
 # Streamlit Chat Interface
@@ -12,10 +9,24 @@
 info = """
     <p style='text-align: center; color: white;'>Machine learning research should be easily accessible and reusable. OpenML is an open platform for sharing datasets, algorithms, and experiments - to learn how to learn better, together. <br>Ask me anything about OpenML or search for a dataset ... </p>
     """
+chatbot_display = "How do I do X using OpenML? / Find me a dataset about Y"
+chatbot_max_chars = 500
+
 st.set_page_config(page_title=page_title, page_icon=logo)
 st.title("OpenML AI Search")
+# message_box = st.container()
+
+with st.spinner("Loading Required Data"):
+    config_path = Path("../backend/config.json")
+    ui_loader = UILoader(config_path)
 
 # container for company description and logo
+with st.sidebar:
+    query_type = st.radio(
+        "Select Query Type", ["General Query", "Dataset", "Flow"], key="query_type_2"
+    )
+
+user_input = st.chat_input(placeholder=chatbot_display, max_chars=chatbot_max_chars)
 col1, col2 = st.columns([1, 4])
 with col1:
     st.image(logo, width=100)
@@ -24,22 +35,6 @@
         info,
         unsafe_allow_html=True,
     )
-
-with st.spinner("Loading Required Data"):
-    config_path = Path("../backend/config.json")
-    ui_loader = UILoader(config_path)
-
-# Chat input box
-user_input = ui_loader.chat_entry()
-
-ui_loader.create_chat_interface(None)
-query_type = st.selectbox("Select Query Type", ["General Query","Dataset", "Flow"], key="query_type_2")
-llm_filter = st.toggle("LLM Filter")
-# Chat interface
+ui_loader.create_chat_interface(user_input=None)
 if user_input:
-    ui_loader.create_chat_interface(
-        user_input, query_type=query_type, llm_filter=llm_filter
-    )
-    ui_loader.query_type = st.selectbox("Select Query Type", ["General Query","Dataset", "Flow"], key="query_type_3")
-    ui_loader.llm_filter = st.toggle("LLM Filter", key="llm_filter_2")
-
+    ui_loader.create_chat_interface(user_input, query_type=query_type)

frontend/ui_utils.py

@@ -37,15 +37,6 @@ def feedback_cb():
         json.dump(data, file, indent=4)
 
 
-def display_results(initial_response):
-    """
-    Description: Display the results in a DataFrame
-    """
-    # st.write("OpenML Agent: ")
-    try:
-        st.dataframe(initial_response)
-    except:
-        st.write(initial_response)
 
 class LLMResponseParser:
     """
@@ -352,11 +343,11 @@ def __init__(self, config_path):
         with open(config_path, "r") as file:
             # Load config
             self.config = json.load(file)
+        # self.message_box = message_box
 
         # Paths and display information
 
-        self.chatbot_display = "How do I do X using OpenML? / Find me a dataset about Y"
-        self.chatbot_input_max_chars = 500
+        # self.chatbot_input_max_chars = 500
 
         # Load metadata chroma database for structured query
         self.collec = load_chroma_metadata()
@@ -378,22 +369,22 @@ def __init__(self, config_path):
         if "messages" not in st.session_state:
             st.session_state.messages = []
 
-    def chat_entry(self):
-        """
-        Description: Create the chat input box with a maximum character limit
+    # def chat_entry(self):
+    #     """
+    #     Description: Create the chat input box with a maximum character limit
 
-        """
-        return st.chat_input(
-            self.chatbot_display, max_chars=self.chatbot_input_max_chars
-        )
+    #     """
+    #     return st.chat_input(
+    #         self.chatbot_display, max_chars=self.chatbot_input_max_chars
+    #     )
 
-    def create_chat_interface(self, user_input, query_type=None, llm_filter=None):
+    def create_chat_interface(self, user_input, query_type=None):
         """
         Description: Create the chat interface and display the chat history and results. Show the user input and the response from the OpenML Agent.
 
         """
         self.query_type = query_type
-        self.llm_filter = llm_filter
+        # self.llm_filter = llm_filter
         if user_input is None:
             with st.chat_message(name = "ai"):
                 st.write("OpenML Agent: ", "Hello! How can I help you today?")
@@ -412,10 +403,23 @@ def create_chat_interface(self, user_input, query_type=None, llm_filter=None):
         for message in st.session_state.messages:
             if message["role"] == "user":
                 with st.chat_message(name = "user"):
-                    display_results(message["content"])
+                    self.display_results(message["content"], "user")
             else:
                 with st.chat_message(name = "ai"):
-                    display_results(message["content"])
+                    self.display_results(message["content"], "ai")
+
+    def display_results(self,initial_response, role):
+        """
+        Description: Display the results in a DataFrame
+        """
+        # st.write("OpenML Agent: ")
+
+        try:
+            st.dataframe(initial_response)
+            # self.message_box.chat_message(role).write(st.dataframe(initial_response))
+        except:
+            st.write(initial_response)
+            # self.message_box.chat_message(role).write(initial_response)
 
     # Function to handle query processing
     def process_query_chat(self, query):