Update app.py

app.py

@@ -5,31 +5,42 @@ from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_openai import ChatOpenAI
 import streamlit as st
 
-# set the API-keys for OpenAI and Tavily
-os.environ['OPENAI_API_KEY'] 
-os.environ['TAVILY_API_KEY'] 
+# Securely set API keys (e.g., in a .streamlit/secrets.toml file or environment variables)
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+TAVILY_API_KEY = os.getenv('TAVILY_API_KEY')
 
-# create GPT-3.5 instance
-llm=ChatOpenAI(model_name="gpt-4o",temperature=0.7, max_tokens=4096)
+if not OPENAI_API_KEY or not TAVILY_API_KEY:
+    st.error("API keys for OpenAI and Tavily are not set. Please configure them properly.")
+    st.stop()
 
-# use Tavily as the tool for websearching
+# Create GPT-3.5 instance
+llm = ChatOpenAI(model_name="gpt-4o", temperature=0.7, max_tokens=4096)
+
+# Use Tavily as the tool for web searching
 tools = [TavilySearchResults(max_results=1)]
 
-# following the Langchain documentation
-prompt = hub.pull("hwchase17/openai-tools-agent")
+# Retrieve the agent prompt from LangChain Hub
+try:
+    prompt = hub.pull("hwchase17/openai-tools-agent")
+except Exception as e:
+    st.error(f"Failed to load prompt from hub: {e}")
+    st.stop()
 
-# construct the OpenAI tools agent
+# Construct the OpenAI tools agent
 agent = create_openai_tools_agent(llm, tools, prompt)
 
-# create the agent executor by passing in the agent and tools
+# Create the agent executor by passing in the agent and tools
 agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
 
-# create webapp
-st.title('Chatgpt')
-input = st.text_input('What do you want to know?')
+# Build the Streamlit web app
+st.title("ChatGPT with Web Search")
 
-# show response if a prompt is entered
-if input:
-    response=agent_executor.invoke({"input": {input}})
+user_input = st.text_input("What do you want to know?")
 
-    st.write(response['output'])
+# Process input and display response
+if user_input:
+    try:
+        response = agent_executor.invoke({"input": user_input})
+        st.write(response.get("output", "No output received. Please try again."))
+    except Exception as e:
+        st.error(f"Error processing your request: {e}")