app.py

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
import os

# Fetch Hugging Face token from environment variables
token = os.getenv("HUGGING_FACE_HUB_TOKEN")
if not token:
    raise ValueError("Hugging Face token not found. Please add it as a secret in your Hugging Face Space.")

# Base model and fine-tuned model names
base_model_name = "meta-llama/Meta-Llama-3-8B"
fine_tuned_model_name = "VinitT/Sanskrit-llama"

# Load the base model, fine-tuned model, and tokenizer
try:
    print("Loading base model...")
    base_model = AutoModelForCausalLM.from_pretrained(base_model_name, use_auth_token=token)

    print("Loading fine-tuned model...")
    model = PeftModel.from_pretrained(base_model, fine_tuned_model_name, use_auth_token=token)

    print("Loading tokenizer...")
    tokenizer = AutoTokenizer.from_pretrained(base_model_name, use_auth_token=token)

except Exception as e:
    raise RuntimeError(f"Error loading the model or tokenizer: {e}")

# Function to generate text using the model
def generate_text(input_text):
    try:
        # Tokenize the input
        inputs = tokenizer(input_text, return_tensors="pt")

        # Generate response
        outputs = model.generate(**inputs, max_length=200, num_return_sequences=1)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)

        return response
    except Exception as e:
        return f"Error during generation: {e}"

# Create Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("## Sanskrit Text Generation with Fine-Tuned LLaMA")
    gr.Markdown("### Enter your Sanskrit prompt below and generate text using the fine-tuned LLaMA model.")
    
    input_text = gr.Textbox(label="Enter your prompt in Sanskrit", placeholder="Type your text here...")
    output_text = gr.Textbox(label="Generated Text", interactive=False)
    generate_btn = gr.Button("Generate")
    
    generate_btn.click(fn=generate_text, inputs=input_text, outputs=output_text)

# Run the Gradio app
if __name__ == "__main__":
    demo.launch()