import logging import os import re import warnings from pathlib import Path import gradio as gr import requests import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline, Pipeline from templates import starting_app_code, update_iframe_js, copy_snippet_js, download_code_js, load_js, DemoType, \ copy_share_link_js # Filter the UserWarning raised by the audio component. warnings.filterwarnings("ignore", message='Trying to convert audio automatically from int32 to 16-bit int format') logging.basicConfig( level=logging.INFO, # Set the logging level to INFO or any other desired level format="%(asctime)s - %(message)s", # Define the log message format datefmt="%Y-%m-%d %H:%M:%S", # Define the timestamp format ) logger = logging.getLogger("my_logger") HF_TOKEN = os.getenv("HF_TOKEN") if not HF_TOKEN: raise Exception("HF_TOKEN environment variable is required to call remote API.") API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta" headers = {"Authorization": f"Bearer {HF_TOKEN}"} def init_speech_to_text_model() -> Pipeline: device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-medium.en" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) return pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, torch_dtype=torch_dtype, device=device, ) whisper_pipe = init_speech_to_text_model() code_pattern = re.compile(r'```python\n(.*?)```', re.DOTALL) def query(payload: dict): response = requests.post(API_URL, headers=headers, json=payload) return response.json() def generate_text(code: str, prompt: str) -> (str, str, str): logger.info(f"Calling API with prompt:\n{prompt}") prompt = f"```python\n{code}```\nGiven the code above return only updated code for the following request:\n{prompt}\n<|assistant|>" params = {"max_new_tokens": 512} output = query({"inputs": prompt, "parameters": params}) if 'error' in output: logger.warning(f'Language model call failed: {output["error"]}') raise gr.Warning(f'Language model call failed: {output["error"]}') logger.info(f'API RESPONSE\n{output[0]["generated_text"]}') assistant_reply = output[0]["generated_text"].split('<|assistant|>')[1] match = re.search(code_pattern, assistant_reply) if not match: return assistant_reply, code, None new_code = match.group(1) logger.info(f'NEW CODE:\nnew_code') return assistant_reply, new_code, None def transcribe(audio: str) -> (str, str): result = whisper_pipe(audio) return result["text"], None def link_copy_notify(code: str, requirements: str): gr.Info("Share link copied!") def copy_notify(code: str, requirements: str): gr.Info("App code snippet copied!") def add_hotkeys() -> str: return Path("hotkeys.js").read_text() def apply_query_params(code: str, request: gr.Request) -> (str, str): params = dict(request.query_params) return params.get('code') or code, params.get('requirements') or '' def update_state(requirements: [str], error: str): return '\n'.join(sorted(requirements)), error with gr.Blocks() as demo: gr.Markdown("

KiteWind 🪁🍃

") gr.Markdown( "

Chat-assisted web app creator by @gstaff

") selectedTab = gr.State(value='gradio-lite') with gr.Tab('Gradio (gradio-lite)') as gradio_lite_tab: with gr.Row(): with gr.Column(): gr.Markdown("## 1. Run your app in the browser!") gr.HTML(value='
') gr.Markdown("## 2. Customize using voice requests!") with gr.Row(): with gr.Column(): with gr.Group(): in_audio = gr.Audio(label="Record a voice request (click or press ctrl + ` to start/stop)", source='microphone', type='filepath', elem_classes=["record-btn"]) in_prompt = gr.Textbox(label="Or type a text request and press Enter", placeholder="Need an idea? Try one of these:\n- Add a button to reverse the name\n- Change the greeting to Spanish\n- Put the reversed name output into a separate textbox") out_text = gr.TextArea(label="🤖 Chat Assistant Response") clear = gr.ClearButton([in_prompt, in_audio, out_text]) with gr.Column(): gradio_code_area = gr.Code( label="App Code - You can also edit directly and then click Update App or ctrl + space", language='python', value=starting_app_code(DemoType.GRADIO)) gradio_requirements_area = gr.Code(label="App Requirements (additional modules pip installed for pyodide)") update_btn = gr.Button("Update App (Ctrl + Space)", variant="primary", elem_classes=["update-btn"]) last_error = gr.State() code_update_params = {'fn': update_state, 'inputs': [gradio_code_area, gradio_requirements_area], 'outputs': [gradio_requirements_area, last_error], '_js': update_iframe_js(DemoType.GRADIO)} gen_text_params = {'fn': generate_text, 'inputs': [gradio_code_area, in_prompt], 'outputs': [out_text, gradio_code_area]} transcribe_params = {'fn': transcribe, 'inputs': [in_audio], 'outputs': [in_prompt, in_audio]} update_btn.click(**code_update_params) in_prompt.submit(**gen_text_params).then(**code_update_params) in_audio.stop_recording(**transcribe_params).then(**gen_text_params).then(**code_update_params) with gr.Row(): with gr.Column(): gr.Markdown("## 3. Export your app to share!") share_link_btn = gr.Button("🔗 Copy share link to clipboard") share_link_btn.click(link_copy_notify, [gradio_code_area, gradio_requirements_area], None, _js=copy_share_link_js(DemoType.GRADIO)) copy_snippet_btn = gr.Button("✂️ Copy app snippet to paste into another page") copy_snippet_btn.click(copy_notify, [gradio_code_area, gradio_requirements_area], None, _js=copy_snippet_js(DemoType.GRADIO)) download_btn = gr.Button("🗎 Download app as a standalone file") download_btn.click(None, [gradio_code_area, gradio_requirements_area], None, _js=download_code_js(DemoType.GRADIO)) with gr.Row(): with gr.Column(): gr.Markdown("## Current limitations") with gr.Accordion("Click to view", open=False): gr.Markdown( "- Only gradio-lite apps using the libraries available in pyodide are supported\n- The chat hasn't been tuned on gradio library data; it may make mistakes") with gr.Tab('Streamlit (stlite)') as stlite_tab: with gr.Row(): with gr.Column(): gr.Markdown("## 1. Run your app in the browser!") gr.HTML(value='
') gr.Markdown("## 2. Customize using voice requests!") with gr.Row(): with gr.Column(): with gr.Group(): in_audio = gr.Audio(label="Record a voice request (click or press ctrl + ` to start/stop)", source='microphone', type='filepath', elem_classes=["record-btn"]) in_prompt = gr.Textbox(label="Or type a text request and press Enter", placeholder="Need an idea? Try one of these:\n- Add a button to reverse the name\n- Change the greeting to Spanish\n- Change the theme to soft") out_text = gr.TextArea(label="🤖 Chat Assistant Response") clear_btn = gr.ClearButton([in_prompt, in_audio, out_text]) with gr.Column(): stlite_code_area = gr.Code( label="App Code - You can also edit directly and then click Update App or ctrl + space", language='python', value=starting_app_code(DemoType.STREAMLIT)) requirements_area = gr.Code(label="App Requirements (additional modules pip installed for pyodide)") update_btn = gr.Button("Update App (Ctrl + Space)", variant="primary", elem_classes=["update-btn"]) code_update_params = {'fn': None, 'inputs': [stlite_code_area, requirements_area], 'outputs': None, '_js': update_iframe_js(DemoType.STREAMLIT)} gen_text_params = {'fn': generate_text, 'inputs': [stlite_code_area, in_prompt], 'outputs': [out_text, stlite_code_area]} transcribe_params = {'fn': transcribe, 'inputs': [in_audio], 'outputs': [in_prompt, in_audio]} update_btn.click(**code_update_params) in_prompt.submit(**gen_text_params).then(**code_update_params) in_audio.stop_recording(**transcribe_params).then(**gen_text_params).then(**code_update_params) with gr.Row(): with gr.Column(): gr.Markdown("## 3. Export your app to share!") copy_snippet_btn = gr.Button("✂️ Copy app snippet into paste in another page") copy_snippet_btn.click(copy_notify, [stlite_code_area, requirements_area], None, _js=copy_snippet_js(DemoType.STREAMLIT)) download_btn = gr.Button("🗎 Download app as a standalone file") download_btn.click(None, stlite_code_area, None, _js=download_code_js(DemoType.STREAMLIT)) with gr.Row(): with gr.Column(): gr.Markdown("## Current limitations") with gr.Accordion("Click to view", open=False): gr.Markdown( "- Only Streamlit apps using libraries available in pyodide are supported\n- The chat hasn't been tuned on Streamlit library data; it may make mistakes") gradio_lite_tab.select(lambda: "gradio-lite", None, selectedTab).then(None, None, None, _js=load_js(DemoType.GRADIO)) stlite_tab.select(lambda: "stlite", None, selectedTab).then(None, None, None, _js=load_js(DemoType.STREAMLIT)) demo.load(None, None, None, _js=load_js(DemoType.GRADIO)) demo.load(None, None, None, _js=add_hotkeys()) # TODO: select stlite tab and populate that code based on query param type demo.load(apply_query_params, gradio_code_area, [gradio_code_area, gradio_requirements_area]) demo.css = "footer {visibility: hidden}" if __name__ == "__main__": demo.queue().launch()