switch to simple app

app.py

@@ -1,31 +1,12 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load GPT-2 model and tokenizer
-model_name = "gpt2"  # You can replace with a different version of GPT-2 if needed
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Title of the app
+st.title("Simple Streamlit App")
 
-# Function to generate a response from GPT-2
-def generate_response(prompt):
-    # Only generate a response based on the given prompt
-    inputs = tokenizer(prompt, return_tensors="pt")
-    output = model.generate(inputs['input_ids'], max_length=150)
-    response = tokenizer.decode(output[0], skip_special_tokens=True)
-    return response
-
-# Streamlit UI
-st.title("GPT-2 Data Structures Mentor")
-
-# Instruction for the chatbot role
-st.write("This chatbot is your mentor to help you with learning Data Structures. Ask questions about arrays, linked lists, stacks, queues, trees, graphs, and other related topics!")
-
-# Text input for the user prompt
-user_input = st.text_input("You:", "")
+# User input for text
+user_input = st.text_input("Enter some text:")
 
+# Display the input text
 if user_input:
-    # Adding context to the prompt, but only once for the first input
-    prompt = f"You are a mentor teaching data structures. Answer the following question: {user_input}"
-    response = generate_response(prompt)
-    st.text_area("Mentor's Response:", value=response, height=200, disabled=True)
+    st.write(f"You entered: {user_input}")