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import gradio as gr
import torch
import logging
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Load model & tokenizer
MODEL_NAME = "ubiodee/Cardano_plutus"
try:
logger.info("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
logger.info("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map="auto",
torch_dtype=torch.float16,
low_cpu_mem_usage=True
)
model.eval()
logger.info("Model and tokenizer loaded successfully.")
except Exception as e:
logger.error(f"Error loading model or tokenizer: {str(e)}")
raise
# Prompt template to guide the model (simple, since no model card details)
def format_prompt(user_prompt):
return f"User: {user_prompt}\nAssistant:"
# Response function with proper streaming
def generate_response(user_prompt):
try:
logger.info("Processing prompt...")
prompt = format_prompt(user_prompt)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
# Use streamer for token-by-token generation
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = {
**inputs,
"streamer": streamer,
"max_new_tokens": 300, # Increased slightly for completeness
"do_sample": True, # Revert to sampling to avoid repetition
"temperature": 0.1,
"top_p": 0.1,
"eos_token_id": tokenizer.eos_token_id,
"pad_token_id": tokenizer.pad_token_id
}
# Run generation in a separate thread to avoid blocking
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
generated_text = ""
for new_text in streamer:
generated_text += new_text
yield generated_text.strip()
logger.info("Response generated successfully.")
except Exception as e:
logger.error(f"Error during generation: {str(e)}")
yield f"Error: {str(e)}"
# Gradio UI
demo = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(
label="Enter your prompt",
lines=4,
placeholder="Ask about Plutus or Cardano..."
),
outputs=gr.Textbox(label="Model Response"),
title="Cardano Plutus AI Assistant",
description="Your Cardano AI Builder..",
allow_flagging="never"
)
# Launch the app
try:
logger.info("Launching Gradio interface...")
demo.launch()
except Exception as e:
logger.error(f"Error launching Gradio: {str(e)}")
raise
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