app.py

import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import hf_hub_download
import json

# Cache for model and tokenizer
MODEL = None
TOKENIZER = None

def initialize():
    global MODEL, TOKENIZER
    
    if MODEL is None:
        print("Loading model and tokenizer...")
        model_id = "jatingocodeo/SmolLM2"
        
        try:
            # Download model files from HF Hub
            config_path = hf_hub_download(repo_id=model_id, filename="config.json")
            
            # Load tokenizer
            print("Loading tokenizer...")
            TOKENIZER = AutoTokenizer.from_pretrained(model_id)
            
            # Add special tokens if needed
            special_tokens = {
                'pad_token': '[PAD]',
                'eos_token': '</s>',
                'bos_token': '<s>'
            }
            TOKENIZER.add_special_tokens(special_tokens)
            
            # Load model
            print("Loading model...")
            MODEL = AutoModelForCausalLM.from_pretrained(
                model_id,
                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
                trust_remote_code=True,
                low_cpu_mem_usage=True
            )
            
            # Move model to device
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            MODEL.to(device)
            
            print(f"Model loaded successfully on {device}")
            
        except Exception as e:
            print(f"Error initializing: {str(e)}")
            raise

def generate_text(prompt, max_length=100, temperature=0.7, top_k=50):
    # Initialize if not already done
    if MODEL is None:
        try:
            initialize()
        except Exception as e:
            return f"Failed to initialize model: {str(e)}"
    
    try:
        # Process prompt
        if not prompt.strip():
            return "Please enter a prompt."
        
        # Add BOS token if needed
        if not prompt.startswith(TOKENIZER.bos_token):
            prompt = TOKENIZER.bos_token + prompt
        
        # Encode prompt
        input_ids = TOKENIZER.encode(prompt, return_tensors="pt", truncation=True, max_length=2048)
        input_ids = input_ids.to(MODEL.device)
        
        # Generate
        with torch.no_grad():
            outputs = MODEL.generate(
                input_ids,
                max_length=min(max_length + len(input_ids[0]), 2048),
                temperature=max(0.1, min(temperature, 1.0)),  # Clamp temperature
                top_k=max(1, min(top_k, 100)),  # Clamp top_k
                do_sample=True if temperature > 0 else False,
                num_return_sequences=1,
                pad_token_id=TOKENIZER.pad_token_id,
                eos_token_id=TOKENIZER.eos_token_id,
            )
        
        # Decode and return
        generated_text = TOKENIZER.decode(outputs[0], skip_special_tokens=True)
        return generated_text.strip()
        
    except Exception as e:
        import traceback
        traceback.print_exc()
        return f"Error during text generation: {str(e)}"

# Create Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your prompt here...", lines=2),
        gr.Slider(minimum=10, maximum=200, value=100, step=1, label="Max Length"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=1, maximum=100, value=50, step=1, label="Top K"),
    ],
    outputs=gr.Textbox(label="Generated Text", lines=5),
    title="SmolLM2 Text Generator",
    description="Generate text using the fine-tuned SmolLM2 model. Adjust parameters to control the generation.",
    examples=[
        ["Once upon a time", 100, 0.7, 50],
        ["The quick brown fox", 150, 0.8, 40],
    ],
    allow_flagging="never"
)

# Initialize on startup
try:
    initialize()
except Exception as e:
    print(f"Warning: Model initialization failed: {str(e)}")
    print("Model will be initialized on first request")

if __name__ == "__main__":
    iface.launch()