updating again

app.py

@@ -1,59 +1,30 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
 
-# Define the model name
-model_name = "Qwen/Qwen2.5-1.5B-Instruct"
-
-# Load the model and tokenizer
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
+# 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)
 
-# Function to generate a response
+# Function to generate a response from GPT-2
 def generate_response(prompt):
-    if not prompt:
-        return "Please enter a prompt."
-
-    # Create the messages for chat-based model
-    messages = [
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": prompt}
-    ]
-    
-    # Format the input for the model
-    text = tokenizer.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
-    )
-    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-    # Generate model response
-    generated_ids = model.generate(
-        **model_inputs,
-        max_new_tokens=512
-    )
-    
-    # Decode and return the response
-    generated_ids = [
-        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-    ]
-    
-    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    
+    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("AI Text Generator")
+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!")
 
-prompt = st.text_area("Enter your prompt:", placeholder="Type your question or prompt here...")
+# Text input for the user prompt
+user_input = st.text_input("You:", "")
 
-if st.button("Generate Response"):
-    with st.spinner("Generating response..."):
-        response = generate_response(prompt)
-    st.text_area("Model Response:", value=response, height=200, disabled=True)
+if user_input:
+    # Adding context to the prompt, making GPT-2 respond like a mentor
+    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)
 

requirements.txt

@@ -1,4 +1,3 @@
 streamlit
 transformers==4.31.0
 torch
-accelerate>=0.26.0