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Running
on
T4
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline | |
| import gradio as gr | |
| from gradio_webrtc import WebRTC, ReplyOnPause, AdditionalOutputs | |
| import numpy as np | |
| import os | |
| from twilio.rest import Client | |
| import base64 | |
| account_sid = os.environ.get("TWILIO_ACCOUNT_SID") | |
| auth_token = os.environ.get("TWILIO_AUTH_TOKEN") | |
| if account_sid and auth_token: | |
| client = Client(account_sid, auth_token) | |
| token = client.tokens.create() | |
| rtc_configuration = { | |
| "iceServers": token.ice_servers, | |
| "iceTransportPolicy": "relay", | |
| } | |
| else: | |
| rtc_configuration = None | |
| checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct" | |
| device = "cuda" | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint) | |
| model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) | |
| whisper = pipeline( | |
| model="openai/whisper-large-v3-turbo", device=device | |
| ) | |
| system_prompt = "You are an AI coding assistant. Your task is to write single-file HTML applications based on a user's request. You may also be asked to edit your original response. Only return the code needed to fulfill the request." | |
| user_prompt = "Please write a single-file HTML application to fulfill the following request. Only return the necessary code. Include all necessary imports and styles.\nThe message:{user_message}\nCurrent code you have written:{code}" | |
| def extract_html_content(text): | |
| """ | |
| Extract content including HTML tags. | |
| """ | |
| try: | |
| start_tag = "<html>" | |
| end_tag = "</html>" | |
| # Find positions of start and end tags | |
| start_pos = text.find(start_tag) | |
| end_pos = text.find(end_tag) | |
| # Check if both tags exist and are in correct order | |
| if start_pos == -1 or end_pos == -1 or start_pos > end_pos: | |
| return None | |
| # Extract content including tags | |
| return text[start_pos:end_pos + len(end_tag)] | |
| except Exception as e: | |
| print(f"Error processing string: {e}") | |
| return None | |
| def display_in_sandbox(code): | |
| encoded_html = base64.b64encode(code.encode('utf-8')).decode('utf-8') | |
| data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}" | |
| return f"<iframe src=\"{data_uri}\" width=\"100%\" height=\"920px\"></iframe>" | |
| def generate(user_message: tuple[int, np.ndarray], | |
| history: list[dict], | |
| code: str): | |
| audio_float32 = user_message[1].astype(np.float32) / 32768.0 | |
| msg_text = whisper({"array": audio_float32.squeeze(), "sampling_rate": user_message[0]})["text"] | |
| print("msg_text", msg_text) | |
| user_msg_formatted = user_prompt.format(user_message=msg_text, code=code) | |
| print("user_msg_formatted", user_msg_formatted) | |
| history.append({"role": "user", "content": user_msg_formatted}) | |
| input_text = tokenizer.apply_chat_template(history, tokenize=False) | |
| inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) | |
| outputs = model.generate(inputs, max_new_tokens=2048, temperature=0.2, top_p=0.9, do_sample=True) | |
| response = tokenizer.decode(outputs[0]) | |
| print("response", response) | |
| output = response[response.rindex("<|im_start|>assistant\n") + len("<|im_start|>assistant\n"):] | |
| html_code = extract_html_content(output) | |
| history.append({"role": "assistant", "content": output}) | |
| yield AdditionalOutputs(history, html_code) | |
| with gr.Blocks() as demo: | |
| history = gr.State([{"role": "system", "content": system_prompt}]) | |
| with gr.Row(): | |
| code = gr.Code(language="html", interactive=False) | |
| sandbox = gr.HTML("") | |
| with gr.Row(): | |
| webrtc = WebRTC(rtc_configuration=rtc_configuration, mode="send", modality="audio") | |
| webrtc.stream(ReplyOnPause(generate), | |
| inputs=[webrtc, history, code], | |
| outputs=[webrtc], time_limit=90) | |
| webrtc.on_additional_outputs(lambda history, code: (history, code), | |
| outputs=[history, code]) | |
| code.change(display_in_sandbox, code, sandbox, queue=False) | |
| if __name__ == "__main__": | |
| demo.launch() | |