app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from huggingface_hub import login

# Authenticate with Hugging Face using the environment variable
huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
login(token=huggingface_token)

# Load the tokenizer and model from Hugging Face
@st.cache_resource
def load_model():
    model_name = "meta-llama/Meta-Llama-3.1-70B-Instruct"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
    return tokenizer, model

tokenizer, model = load_model()

# Supported languages
languages = ['English', 'French', 'Spanish', 'Hindi', 'Punjabi']

# Streamlit app
def main():
    st.title("Language Translator")

    # User input for input language
    input_language = st.selectbox("Select Input Language", languages)

    # User input for output language
    output_language = st.selectbox("Select Output Language", languages)

    # Text input box for user to input text
    input_text = st.text_area("Enter the text to translate")

    if st.button("Translate"):
        if input_text.strip() == "":
            st.error("Please enter some text to translate.")
        elif input_language == output_language:
            st.warning("Input and output languages are the same. Please select different languages.")
        else:
            # Perform translation
            translation = translate_text(input_text, input_language, output_language)
            st.success("Translation:")
            st.write(translation)

# Function to translate text using the LLaMA model
def translate_text(text, input_language, output_language):
    prompt = f"Translate the following from {input_language} to {output_language}:\n\n{text}"
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=200)
    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return translation

if __name__ == "__main__":
    main()