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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import json
# Load MagicoderS-CL-7B model
MODEL_NAME = "ise-uiuc/Magicoder-S-CL-7B"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
# System prompt
SYSTEM_INSTRUCTION = (
"You are a helpful assistant that writes UI code. "
"Respond only in JSON format with the following keys: "
"\"filename\", \"html\", \"css\", and \"js\". "
"Do not include explanations. Just return a JSON object."
)
def generate_code(user_prompt):
prompt = f"{SYSTEM_INSTRUCTION}\nPrompt: {user_prompt}"
inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7,
top_p=0.9,
do_sample=True
)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
# Try to parse JSON
try:
result = json.loads(decoded)
except json.JSONDecodeError:
# fallback if output is not valid JSON
result = {
"filename": "index.html",
"html": decoded,
"css": "",
"js": ""
}
return result
# Gradio Interface
iface = gr.Interface(
fn=generate_code,
inputs=gr.Textbox(lines=2, placeholder="Describe the UI you want..."),
outputs="json",
title="UI Code Generator",
description="Enter a prompt to generate HTML, CSS, and JS code in structured JSON format."
)
if __name__ == "__main__":
iface.launch()
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