import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, GPTQConfig
import torch

# Initialize model and tokenizer
MODEL_NAME = "TheBloke/deepseek-coder-1.3b-instruct-GPTQ"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)

# Configure GPTQ for inference
quantization_config = GPTQConfig(
    bits=4,          # 4-bit quantization
    dataset="c4",    # Required dummy dataset for config
    model_seqlen=2048  # Match model's maximum context length
)

# Load model with CPU optimizations
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    quantization_config=quantization_config,
    torch_dtype=torch.float32,  # CPU-friendly precision
    low_cpu_mem_usage=True,
    offload_folder="offload",   # Disk offloading for large layers
    offload_state_dict=True     # Memory-efficient state loading
)

def generate_text(prompt, max_length=150, temperature=0.7):
    """Generate text with optimized inference settings"""
    inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=max_length,
            temperature=temperature,
            pad_token_id=tokenizer.eos_token_id,
            num_beams=1,          # Single-beam for minimal memory
            do_sample=True,       # Enable sampling for creativity
            top_p=0.95,           # Nucleus sampling
            repetition_penalty=1.1  # Reduce repetition
        )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Gradio Interface with Enhanced UX
with gr.Blocks(theme="soft", css=".gr-box {border-radius: 10px}") as demo:
    gr.Markdown("""
    # 🧠 DeepSeek Coder 1.3B Text Generator
    *Optimized for CPU execution on HuggingFace Free Tier*
    """)
    
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(
                label="Input Prompt",
                placeholder="Enter your programming/code-related question...",
                lines=5,
                max_lines=10,
                elem_classes=["monospace"]
            )
            with gr.Row():
                max_length = gr.Slider(50, 500, value=150, label="Max Length", step=10)
                temperature = gr.Slider(0.1, 1.0, value=0.7, label="Creativity", step=0.05)
            submit = gr.Button("🚀 Generate", variant="primary")
        
        output = gr.Textbox(
            label="Generated Output",
            lines=12,
            max_lines=20,
            elem_classes=["monospace"]
        )
    
    submit.click(
        fn=generate_text,
        inputs=[prompt, max_length, temperature],
        outputs=output
    )
    
    gr.Examples(
        examples=[
            ["Write a Python function to calculate Fibonacci numbers"],
            ["Explain the difference between list and tuples in Python"],
            ["Create a simple Flask API endpoint for user registration"]
        ],
        fn=generate_text,
        inputs=[prompt, max_length, temperature],
        outputs=output,
        cache_examples=False
    )

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