Update app.py

app.py

@@ -2,17 +2,15 @@ import logging
 import os
 import requests
 from dotenv import load_dotenv
-import gradio as gr
 import openai
+import streamlit as st
 from langchain_openai import ChatOpenAI
 from langchain_community.vectorstores import FAISS
 from langchain_openai import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.agents import tool, AgentExecutor
 from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
-from langchain.agents.format_scratchpad.openai_tools import (
-    format_to_openai_tool_messages,
-)
+from langchain.agents.format_scratchpad.openai_tools import format_to_openai_tool_messages
 from langchain_core.messages import AIMessage, HumanMessage
 from langchain_community.document_loaders import TextLoader
 from langchain_text_splitters import CharacterTextSplitter
@@ -43,7 +41,6 @@ except Exception as e:
     logger.error(f"Error initializing OpenAI client: {e}")
     raise e
 
-
 # Load knowledge base
 def load_knowledge_base():
     try:
@@ -56,7 +53,6 @@ def load_knowledge_base():
         logger.error(f"Error loading knowledge base: {e}")
         raise e
 
-
 knowledge_base = load_knowledge_base()
 
 # Initialize embeddings and FAISS index
@@ -67,7 +63,6 @@ except Exception as e:
     logger.error(f"Error initializing FAISS index: {e}")
     raise e
 
-
 # Define search function for knowledge base
 def search_knowledge_base(query):
     try:
@@ -77,7 +72,6 @@ def search_knowledge_base(query):
         logger.error(f"Error searching knowledge base: {e}")
         return ["Error occurred during knowledge base search"]
 
-
 # SERPER API Google Search function
 def google_search(query):
     try:
@@ -97,7 +91,6 @@ def google_search(query):
         logger.error(f"General Error: {e}")
         return ["Error occurred during Google search"]
 
-
 # RAG response function
 def rag_response(query):
     try:
@@ -111,36 +104,16 @@ def rag_response(query):
         logger.error(f"Error generating RAG response: {e}")
         return "Error occurred during RAG response generation"
 
-
 # Define tools using LangChain's `tool` decorator
 @tool
 def knowledge_base_tool(query: str):
-    """
-    Tool function to query the knowledge base and retrieve a response.
-    Args:
-        query (str): The query to search the knowledge base.
-    Returns:
-        str: The response retrieved from the knowledge base.
-    """
     return rag_response(query)
 
-
 @tool
 def google_search_tool(query: str):
-    """
-    Tool function to perform a Google search using the SERPER API.
-    Args:
-        query (str): The query to search on Google.
-    Returns:
-        list: List of snippets extracted from search results.
-    """
     return google_search(query)
 
-
-tools = [
-    knowledge_base_tool,
-    google_search_tool,
-]
+tools = [knowledge_base_tool, google_search_tool]
 
 # Create the prompt template
 prompt_message = """
@@ -160,7 +133,7 @@ prompt_template = ChatPromptTemplate.from_messages(
 
 # Create Langchain Agent with specific model and temperature
 try:
-    llm = ChatOpenAI(model="gpt-4o", temperature=0.5)  # Set temperature to 0.5
+    llm = ChatOpenAI(model="gpt-4o", temperature=0.5)
     llm_with_tools = llm.bind_tools(tools)
 except Exception as e:
     logger.error(f"Error creating Langchain Agent: {e}")
@@ -188,7 +161,6 @@ except Exception as e:
 # Initialize chat history
 chat_history = []
 
-
 def chatbot_response(message, history):
     try:
         # Generate response using the agent executor
@@ -207,39 +179,12 @@ def chatbot_response(message, history):
         logger.error(f"Error generating chatbot response: {e}")
         return "Error occurred during response generation"
 
+# Streamlit application
 
-# # Define CSS for Gradio interface
-# CSS = """
-# .contain { display: flex; flex-direction: column; height: 100vh; }
-# #component-0 { height: 90%; }
-# """
+# Input field for user query
+query = st.text_input("Enter your query:", "")
 
-# # Gradio interface
-# with gr.Blocks(css=CSS) as demo:
-
-submit_button = gr.Button("Submit")
-
-bot = gr.Chatbot()
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        "<span style='font-size:20px; font-weight:bold;'>Instant Insight-2-Action</span>",
-        visible=True,
-    )
-
-    chatbot = gr.ChatInterface(
-        fn=chatbot_response,
-        stop_btn=None,
-        retry_btn=None,
-        undo_btn=None,
-        clear_btn=None,
-        submit_btn=submit_button,
-        chatbot=bot,
-    )
-
-# Launch the Gradio app
-try:
-    demo.launch(server_name="0.0.0.0")
-except Exception as e:
-    logger.error(f"Error launching Gradio app: {e}")
-    raise e
+if query:
+    response = chatbot_response(query, chat_history)
+    st.write("**Response:**")
+    st.write(response)