feat(QuestionModel): Complete for deploy

src/streamlit_app.py

@@ -1,15 +1,23 @@
 import configparser
 import streamlit as st
 from typing import List, Optional
+from transformers import DistilBertTokenizerFast, TFDistilBertForSequenceClassification
+from langchain_core.messages import AnyMessage, AIMessage,SystemMessage, HumanMessage,AIMessageChunk
 
 from streamlitui.constants import unsdg_countries
+from llm.llm_setup import ModelSelection
+import pandas as pd
+from state.state import StateVector
+from graph.state_vector_nodes import question_model
+from graph.graph_builder import BuildGraphOptions
+import re
 class StreamlitConfigUI:
 
     """
     A Streamlit UI class that uses ConfigParser to load settings from an INI file.
     """
     
-    def __init__(self, config_file: str = "./src/streamlitui/uiconfigfile.ini"):
+    def __init__(self, config_file: str = "./streamlitui/uiconfigfile.ini"):
         """
         Initialize the UI class with configuration file.
         
@@ -68,10 +76,54 @@ class LoadStreamlitUI:
             self.user_controls["selected_usecase"]=st.selectbox("Select Usecases",usecase_options)
             self.user_controls['UN SDG Country']= st.selectbox("Choose a country (start typing to search):",options=[""] + self.unsdg_countries)
         return self.user_controls
+def display_result_on_ui( state_graph,mode="Question Refining Mode:"):
+	if mode =="Question Refining Mode:":
+			for event in graph.stream({'messages':("user",user_message)}):
+				print(event.values())
+				for value in event.values():
+					print(value['messages'])
+					with st.chat_message("user"):
+						st.write(user_message)
+					with st.chat_message("SDG AI Assistant"):
+						st.write(value["messages"].content)
 
 if __name__=='__main__':
     ui=LoadStreamlitUI()
     user_input=ui.load_streamlit_ui()
+    LLM_Selection=ModelSelection(user_input)
+    if user_input["GENAI_API_KEY"]:llm=LLM_Selection.setup_llm_model()
+    loaded_tokenizer = DistilBertTokenizerFast.from_pretrained('./train_bert/topic_classifier_model')
+    loaded_model = TFDistilBertForSequenceClassification.from_pretrained('./train_bert/topic_classifier_model')
+    df_keys=pd.read_csv('train_bert/training_data/Keyword_Patterns.csv')
+	
     if not user_input:
         st.error("Error: Failed to load user input from the UI.")
-    user_message = st.chat_input("Enter your message:")
+    #Input prompt 
+    user_message = st.chat_input("Ask me a question or give me keywords to kick off a query about a UN SDG Goal:")
+    
+    if user_message and user_input['UN SDG Country']:
+        state=StateVector(country=user_input['UN SDG Country'], seed_question=user_message, messages=[])
+
+        SmartQuestions=question_model(#StateVector=state,
+                                loaded_tokenizer=loaded_tokenizer,
+                                loaded_model=loaded_model, llm=llm, df_keys=df_keys)
+        builder=BuildGraphOptions(SmartQuestions)
+        graph=builder.build_graph()
+        with st.chat_message("assistant"):
+            intro="Hello, I am an assistant designed to help you learn about the 17 UN SDG goals listed here: https://sdgs.un.org/goals.\
+                        You can ask me about any of the goals or specific topics, and I will provide you with information and resources related to them.\
+                        I can also help you create a question related to the SDGs based on your input.\
+                        Please provide me with a topic or question related to the SDGs and select a country, and I will do my best to assist you." #, 
+            st.write(intro)
+        initial_input = {'country': user_input['UN SDG Country'], 'seed_question': user_message}
+        with st.chat_message("user"):st.write(user_message)
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            accumulated_content = ""
+            for chunk in graph.stream(initial_input, stream_mode='messages'):
+                  message, meta=chunk
+                  if isinstance(message, AIMessage):
+                        accumulated_content += message.content
+                        message_placeholder.write(accumulated_content)
+    else: 
+         print("Deep Rishsearch")