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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()
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