Update app.py

app.py

@@ -1,6 +1,5 @@
 import os
 import requests
-import time
 import streamlit as st
 
 # Get the Hugging Face API Token from environment variables
@@ -26,23 +25,6 @@ def query_model(api_url, payload):
     response = requests.post(api_url, headers=HEADERS, json=payload)
     return response.json()
 
-def count_tokens(text):
-    return len(text.split())
-
-MAX_TOKENS_PER_MINUTE = 1000
-token_count = 0
-start_time = time.time()
-
-def handle_token_limit(text):
-    global token_count, start_time
-    current_time = time.time()
-    if current_time - start_time > 60:
-        token_count = 0
-        start_time = current_time
-    token_count += count_tokens(text)
-    if token_count > MAX_TOKENS_PER_MINUTE:
-        raise ValueError("Token limit exceeded. Please wait before sending more messages.")
-
 def add_message_to_conversation(user_message, bot_message, model_name):
     st.session_state.conversation.append((user_message, bot_message, model_name))
 
@@ -74,7 +56,6 @@ question = st.text_input("Question", placeholder="Enter your question here...")
 # Handle user input and LLM response
 if st.button("Send") and question:
     try:
-        handle_token_limit(question)  # Check token limit before processing
         with st.spinner("Waiting for the model to respond..."):
             chat_history = " ".join(st.session_state.model_history[llm_selection]) + f"User: {question}\n"
             if llm_selection == "Mistral-8x7B":
@@ -111,7 +92,6 @@ if st.button("Send") and question:
                 response = query_model(GEMMA_27B_IT_API_URL, {"inputs": chat_history})
                 answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
 
-            handle_token_limit(answer)  # Check token limit for output
             add_message_to_conversation(question, answer, llm_selection)
             st.session_state.model_history[llm_selection].append(f"User: {question}\n{llm_selection}: {answer}\n")
     except ValueError as e: