app.py

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

model_name = "deepseek-ai/deepseek-coder-1.3b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_code(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=300,
        pad_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

examples = [
    "Create a Python function to reverse a string.",
    "Write a JavaScript function that returns the factorial of a number.",
    "Build a simple HTML page with a form and a submit button.",
    "Create a Python script to fetch weather data using an API."
]

with gr.Blocks() as demo:
    gr.Markdown("## 💻 Generate Code with DeepSeek")
    prompt = gr.Textbox(label="Enter your prompt", lines=4, scale=2)
    output = gr.Textbox(label="Generated code", lines=10)
    gen_button = gr.Button("Generate")
    gen_button.click(fn=generate_code, inputs=prompt, outputs=output)
    gr.Examples(examples=examples, inputs=prompt)

demo.launch()