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

# Load model and tokenizer from Hugging Face Hub
model_id = "deepseek-ai/deepseek-coder-1.3b-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)

def generate_code(prompt):
    if not prompt.strip():
        return "⚠ Please enter a valid prompt."
    
    inputs = tokenizer(prompt, return_tensors="pt")
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

demo = gr.Interface(fn=generate_code, 
                    inputs=gr.Textbox(lines=5, label="Enter Prompt"), 
                    outputs="text", 
                    title="Code Generator using DeepSeek")

demo.launch()