app.py

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/Plutus_Tutor_new"

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

# Define options for dropdowns
PERSONALITY_TYPES = ["Autistic", "Dyslexic", "Expressive", "Nerd", "Visual", "Other"]
PROGRAMMING_LEVELS = ["Beginner", "Intermediate", "Professional"]
TOPICS = [
    "What is Plutus",
    "Introduction to Validation",
    "Smart Contracts",
    "Versioning in Plutus",
    "Monad",
    "Other"  # Add more as needed
]

# Prompt template to guide the model
def create_prompt(personality, level, topic):
    return f"User: Teach me about {topic} in Plutus. I am a {level} programmer with {personality} traits. Make the explanation tailored to my needs, easy to understand, and engaging.\nAssistant:"

# Response function with proper streaming
def generate_response(personality, level, topic):
    try:
        logger.info("Processing selections...")
        prompt = create_prompt(personality, level, topic)
        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": 500,
            "do_sample": True,
            "temperature": 0.4,
            "top_p": 0.5,
            "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 with dropdowns and button
with gr.Blocks(title="Cardano Plutus AI Assistant") as demo:
    gr.Markdown("### Your Personalised Plutus Tutor")
    gr.Markdown("Select your personality type, programming level, and topic, then click Generate.")
    
    personality = gr.Dropdown(
        choices=PERSONALITY_TYPES,
        label="Personality Type",
        value="Dyslexic"  # Default
    )
    level = gr.Dropdown(
        choices=PROGRAMMING_LEVELS,
        label="Programming Level",
        value="Beginner"  # Default
    )
    topic = gr.Dropdown(
        choices=TOPICS,
        label="Topic",
        value="What is Plutus"  # Default
    )
    
    generate_btn = gr.Button("Generate")
    
    output = gr.Textbox(
        label="Model Response",
        show_label=True,
        lines=10,
        placeholder="Generated content will appear here..."
    )
    
    generate_btn.click(
        fn=generate_response,
        inputs=[personality, level, topic],
        outputs=output
    )

# Launch the app
try:
    logger.info("Launching Gradio interface...")
    demo.launch()
except Exception as e:
    logger.error(f"Error launching Gradio: {str(e)}")
    raise