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import gradio as gr
from huggingface_hub import InferenceClient
import os
import re
# --- Configuration ---
API_TOKEN = os.getenv("HF_TOKEN", None)
MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
# --- Initialize Inference Client ---
try:
print(f"Initializing Inference Client for model: {MODEL}")
client = InferenceClient(model=MODEL, token=API_TOKEN) if API_TOKEN else InferenceClient(model=MODEL)
except Exception as e:
raise gr.Error(f"Failed to initialize model client. Error: {e}")
# --- Core Code Generation Function ---
def generate_code(
prompt: str,
backend_choice: str,
max_tokens: int,
temperature: float,
top_p: float,
):
print(f"Generating code for: {prompt[:100]}... | Backend: {backend_choice}")
# --- Dynamically Build System Message ---
system_message = (
"you are an ai that is supposed to generate websites, you must not say anything except giving code , "
"user can select backend like static , flask , nodejs only , you should always keep the website sfw and minimal errors, "
"you must create an index.html following the user prompt, "
"if the user asks you create an code that's not about an website you should say "
"'hey there! am here to create websites for you unfortunately am programmed to not create codes! otherwise I would go on the naughty list :-(', "
"your code always must have no useless comments you should only add comments where users are required to modify the code."
)
user_prompt = f"USER_PROMPT = {prompt}\nUSER_BACKEND = {backend_choice}"
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": user_prompt}
]
response_stream = ""
full_response = ""
try:
stream = client.chat_completion(
messages=messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
)
for message in stream:
token = message.choices[0].delta.content
if isinstance(token, str):
response_stream += token
full_response += token
yield response_stream
cleaned_response = full_response.strip()
cleaned_response = re.sub(r"^\s*```[a-z]*\s*\n?", "", cleaned_response)
cleaned_response = re.sub(r"\n?\s*```\s*$", "", cleaned_response)
cleaned_response = re.sub(r"<\s*\|?\s*(user|assistant)\s*\|?\s*>", "", cleaned_response, flags=re.IGNORECASE)
common_phrases = [
"Here is the code:", "Okay, here is the code:", "Here's the code:",
"Sure, here is the code you requested:", "Let me know if you need anything else."
]
for phrase in common_phrases:
if cleaned_response.lower().startswith(phrase.lower()):
cleaned_response = cleaned_response[len(phrase):].lstrip()
yield cleaned_response.strip()
except Exception as e:
yield f"## Error\n\nFailed to generate code.\n**Reason:** {e}"
# --- Build Gradio Interface ---
with gr.Blocks(css=".gradio-container { max-width: 90% !important; }") as demo:
gr.Markdown("# ✨ Website Code Generator ✨")
gr.Markdown(
"Describe the website you want. The AI will generate a **single-file** `index.html` website.\n\n"
"**Rules:**\n"
"- Backend hint (Static / Flask / Node.js).\n"
"- Always fully SFW and minimal errors.\n"
"- Only generates websites. No other codes.\n"
"- Minimal necessary comments only."
)
with gr.Row():
with gr.Column(scale=2):
prompt_input = gr.Textbox(
label="Website Description",
placeholder="e.g., A simple landing page with a hero section and contact form.",
lines=6,
)
backend_radio = gr.Radio(
["Static", "Flask", "Node.js"],
label="Backend Context",
value="Static",
info="Hint only. Always generates only index.html."
)
generate_button = gr.Button("✨ Generate Website Code", variant="primary")
with gr.Column(scale=3):
code_output = gr.Code(
label="Generated index.html",
language="html",
lines=30,
interactive=False,
)
with gr.Accordion("Advanced Settings", open=False):
max_tokens_slider = gr.Slider(
minimum=512,
maximum=4096,
value=3072,
step=256,
label="Max New Tokens"
)
temperature_slider = gr.Slider(
minimum=0.1, maximum=1.2, value=0.7, step=0.1, label="Temperature"
)
top_p_slider = gr.Slider(
minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-P"
)
generate_button.click(
fn=generate_code,
inputs=[prompt_input, backend_radio, max_tokens_slider, temperature_slider, top_p_slider],
outputs=code_output,
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()