app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load your text generation model and tokenizer
pipe = pipeline("text-generation", model="mostafaHaydar/model_test")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Set up the Streamlit app
st.title("Text Generation with LLaMA 3")

# Text input from the user
prompt = st.text_area("Enter your prompt:")

# Generate text when the user clicks the button
if st.button("Generate"):
    if prompt:
        # Tokenize and generate text
        inputs = tokenizer(prompt, return_tensors="pt")
        output = model.generate(**inputs, max_length=150)  # Adjust max_length as needed
        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

        # Display the generated text
        st.subheader("Generated Text:")
        st.write(generated_text)
    else:
        st.warning("Please enter a prompt to generate text.")