Update app.py

app.py

@@ -2,12 +2,16 @@ import streamlit as st
 import os
 from groq import Groq  # Ensure Groq library supports this usage
 from dotenv import load_dotenv
+
+# Load environment variables from .env file
 load_dotenv()
 
-os.getenv("GROQ_API_KEY")
+# Retrieve Groq API key from environment variables
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 
+# Define function to fetch response
 def fetch_response(user_input):
-    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+    client = Groq(api_key=GROQ_API_KEY)
     chat_completion = client.chat.completions.create(
         messages=[
             {"role": "system", "content": "you are a helpful assistant. Take the input from the users and try to provide as detailed response as possible. Provide proper expamples to help the user. Try to mention references or provide citations to make it more detail oriented."},
@@ -17,19 +21,16 @@ def fetch_response(user_input):
         stream=False
     )
     return chat_completion.choices[0].message.content
-    # for each in chat_completion:
-    #     yield (each.choices[0].delta.content) 
-    #     #end=""
-
 
-def fetch_response(text_input):
-    # Implement your function to fetch response here
-    return "Response: " + text_input  # For demonstration purposes
-
-st.title("Fastest AI Chatbot By DL Titan")
+# Streamlit app
+st.title("Fastest AI Chatbot By DL Titans")
 st.write("Ask a question and get a response.")
 
-text_input = st.text_input("Enter your question here:")
-if st.button("Submit"):
-    response = fetch_response(text_input)
-    st.write(response)
+# Text input for user's question
+user_input = st.text_input("Enter your question here:")
+
+# Button to trigger response
+if st.button("Get Response"):
+    # Fetch and display response
+    response = fetch_response(user_input)
+    st.write("Response:", response)