app.py

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"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Function to generate a response
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]
    
    return response

# Streamlit UI
st.title("AI Text Generator")

prompt = st.text_area("Enter your prompt:", placeholder="Type your question or prompt here...")

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)