Update app.py

app.py

@@ -2,15 +2,17 @@ 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
@@ -41,6 +43,7 @@ except Exception as e:
     logger.error(f"Error initializing OpenAI client: {e}")
     raise e
 
+
 # Load knowledge base
 def load_knowledge_base():
     try:
@@ -53,6 +56,7 @@ def load_knowledge_base():
         logger.error(f"Error loading knowledge base: {e}")
         raise e
 
+
 knowledge_base = load_knowledge_base()
 
 # Initialize embeddings and FAISS index
@@ -63,6 +67,7 @@ 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:
@@ -72,6 +77,7 @@ 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:
@@ -91,6 +97,7 @@ def google_search(query):
         logger.error(f"General Error: {e}")
         return ["Error occurred during Google search"]
 
+
 # RAG response function
 def rag_response(query):
     try:
@@ -104,16 +111,36 @@ 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 = """
@@ -133,7 +160,7 @@ prompt_template = ChatPromptTemplate.from_messages(
 
 # Create Langchain Agent with specific model and temperature
 try:
-    llm = ChatOpenAI(model="gpt-4o", temperature=0.5)
+    llm = ChatOpenAI(model="gpt-4o", temperature=0.5)  # Set temperature to 0.5
     llm_with_tools = llm.bind_tools(tools)
 except Exception as e:
     logger.error(f"Error creating Langchain Agent: {e}")
@@ -161,6 +188,7 @@ except Exception as e:
 # Initialize chat history
 chat_history = []
 
+
 def chatbot_response(message, history):
     try:
         # Generate response using the agent executor
@@ -179,12 +207,39 @@ def chatbot_response(message, history):
         logger.error(f"Error generating chatbot response: {e}")
         return "Error occurred during response generation"
 
-# Streamlit application
 
-# Input field for user query
-query = st.text_input("Enter your query:", "")
+# # Define CSS for Gradio interface
+# CSS = """
+# .contain { display: flex; flex-direction: column; height: 100vh; }
+# #component-0 { height: 90%; }
+# """
 
-if query:
-    response = chatbot_response(query, chat_history)
-    st.write("**Response:**")
-    st.write(response)
+# # 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