| import gradio as gr | |
| import torch | |
| from faster_whisper import WhisperModel | |
| import yt_dlp | |
| from openai import OpenAI | |
| import os | |
| import json | |
| import time | |
| import uuid | |
| print("Initializing transcription model (faster-whisper)...") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| compute_type = "float16" if device == "cuda" else "int8" | |
| model_size = "large-v3-turbo" | |
| model = WhisperModel(model_size, device=device, compute_type=compute_type) | |
| print("Transcription model loaded successfully.") | |
| def download_youtube_audio(url: str) -> str: | |
| unique_id = uuid.uuid4() | |
| output_template = f'{unique_id}.%(ext)s' | |
| final_filepath = f'{unique_id}.mp3' | |
| ydl_opts = { | |
| 'format': 'bestaudio/best', | |
| 'postprocessors': [{'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3', 'preferredquality': '192'}], | |
| 'outtmpl': output_template, | |
| 'quiet': True, | |
| 'overwrite': True, | |
| } | |
| with yt_dlp.YoutubeDL(ydl_opts) as ydl: | |
| ydl.download([url]) | |
| return final_filepath | |
| def transcribe_and_summarize(audio_file: str, youtube_url: str): | |
| log_history = "" | |
| def log(message): | |
| nonlocal log_history | |
| timestamp = time.strftime("%H:%M:%S") | |
| log_history += f"[{timestamp}] {message}\n" | |
| return log_history | |
| loading_message = "⏳ Generating summary..." | |
| yield log("Process started."), "", "" | |
| api_key = os.getenv('TYPHOON_API') | |
| if not api_key: | |
| yield log("TYPHOON_API environment variable not set."), "", gr.Markdown("## Error\nAPI key missing") | |
| return | |
| if audio_file is None and not youtube_url: | |
| raise gr.Error("Please upload an audio file or provide a YouTube link.") | |
| filepath = "" | |
| is_downloaded = False | |
| try: | |
| if youtube_url: | |
| yield log("Downloading YouTube audio..."), "", "" | |
| filepath = download_youtube_audio(youtube_url) | |
| is_downloaded = True | |
| yield log(f"Downloaded to {filepath}"), "", "" | |
| else: | |
| filepath = audio_file | |
| yield log("Transcription started (Language: Thai)..."), "", "" | |
| segments, info = model.transcribe(filepath, beam_size=5, language="th", task="transcribe") | |
| transcribed_text = "" | |
| for segment in segments: | |
| line = f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text.strip()}" | |
| transcribed_text += segment.text + " " | |
| yield log(line), transcribed_text, "" | |
| yield log("Transcription complete."), transcribed_text, "" | |
| yield log("Sending to AI for summarization..."), transcribed_text, loading_message | |
| client = OpenAI(api_key=api_key, base_url="https://api.opentyphoon.ai/v1") | |
| system_prompt = f"""You are an automated system that converts transcripts into a blog post. | |
| Your ONLY function is to output a valid JSON object. All text values in the JSON MUST be in the Thai language. | |
| หน้าที่เดียวของคุณคือการส่งออกอ็อบเจกต์ JSON ที่ถูกต้อง โดยค่าที่เป็นข้อความทั้งหมดต้องเป็นภาษาไทยเท่านั้น | |
| Do NOT write any explanations. The response MUST start with `{{` and end with `}}`. | |
| The JSON object must have the following structure: | |
| {{ | |
| "title": "หัวข้อบทความที่น่าสนใจและเกี่ยวข้อง (เป็นภาษาไทย)", | |
| "key_takeaway": "สรุปใจความสำคัญของเนื้อหาทั้งหมดในหนึ่งย่อหน้า (เป็นภาษาไทย)", | |
| "main_ideas": [ | |
| "ประเด็นหลักหรือใจความสำคัญ (เป็นภาษาไทย)", | |
| "ประเด็นหลักถัดไป...", | |
| "และต่อไปเรื่อยๆ..." | |
| ], | |
| "conclusion": "ย่อหน้าสรุปปิดท้าย (เป็นภาษาไทย)" | |
| }}""" | |
| response = client.chat.completions.create( | |
| model="typhoon-v2.1-12b-instruct", | |
| messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": transcribed_text}], | |
| max_tokens=2048, | |
| temperature=0.7 | |
| ) | |
| summary_json_string = response.choices[0].message.content | |
| if summary_json_string.strip().startswith("```json"): | |
| summary_json_string = summary_json_string.strip()[7:-4].strip() | |
| data = json.loads(summary_json_string) | |
| title = data.get("title", "Title Not Found") | |
| key_takeaway = data.get("key_takeaway", "") | |
| main_ideas = data.get("main_ideas", []) | |
| conclusion = data.get("conclusion", "") | |
| summary_markdown = f"# {title}\n\n<p>{key_takeaway}</p>\n\n## Key Ideas\n\n<ul>" | |
| for idea in main_ideas: | |
| summary_markdown += f"<li>{idea}</li>" | |
| summary_markdown += f"</ul>\n\n## Conclusion\n\n<p>{conclusion}</p>" | |
| yield log("Summarization complete."), transcribed_text, summary_markdown | |
| finally: | |
| if is_downloaded and os.path.exists(filepath): | |
| os.remove(filepath) | |
| def update_video_preview(url): | |
| if not url: | |
| return gr.update(value=None, visible=False) | |
| video_id = None | |
| try: | |
| if "youtube.com/shorts/" in url: | |
| video_id = url.split("/shorts/")[1].split("?")[0] | |
| elif "watch?v=" in url: | |
| video_id = url.split("watch?v=")[1].split("&")[0] | |
| elif "youtu.be/" in url: | |
| video_id = url.split("youtu.be/")[1].split("?")[0] | |
| except IndexError: | |
| pass | |
| if video_id: | |
| embed_url = f"https://www.youtube.com/embed/{video_id}" | |
| iframe_html = f'<iframe width="100%" height="315" src="{embed_url}" frameborder="0" allowfullscreen></iframe>' | |
| return gr.update(value=iframe_html, visible=True) | |
| return gr.update(value=None, visible=False) | |
| css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Sarabun:wght@400;700&display=swap'); | |
| .blog-output { font-family: 'Sarabun', sans-serif; line-height: 1.8; max-width: 800px; margin: auto; padding: 2rem; border-radius: 12px; background-color: #ffffff; border: 1px solid #e5e7eb; } | |
| .blog-output h1 { font-size: 2.2em; font-weight: 700; border-bottom: 2px solid #f3f4f6; padding-bottom: 15px; margin-bottom: 25px; color: #111827; } | |
| .blog-output h2 { font-size: 1.6em; font-weight: 700; margin-top: 40px; margin-bottom: 20px; color: #1f2937; } | |
| .blog-output p { font-size: 1.1em; margin-bottom: 20px; color: #374151; } | |
| .blog-output ul { padding-left: 25px; list-style-type: disc; } | |
| .blog-output li { margin-bottom: 12px; padding-left: 5px; } | |
| """ | |
| with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"), css=css) as demo: | |
| gr.Markdown("# 🎙️ Audio to Blog Summarizer ✒️") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| with gr.Tabs(): | |
| with gr.TabItem("⬆️ Upload Audio"): | |
| audio_file_input = gr.Audio(label="Upload Audio File", type="filepath") | |
| with gr.TabItem("🔗 YouTube Link"): | |
| youtube_url_input = gr.Textbox(label="YouTube URL", placeholder="Paste a YouTube link here...") | |
| submit_button = gr.Button("🚀 Generate Blog Post", variant="primary") | |
| video_preview = gr.HTML(visible=False) | |
| with gr.Accordion("📝 View Process Log", open=True): | |
| log_output = gr.Textbox(label="Log", interactive=False, lines=10) | |
| with gr.Column(scale=2): | |
| gr.Markdown("## ✨ Article Output") | |
| blog_summary_output = gr.Markdown(elem_classes=["blog-output"]) | |
| with gr.Accordion("📜 View Full Transcription", open=False): | |
| transcription_output = gr.Textbox(label="Full Text", interactive=False, lines=10) | |
| submit_button.click(fn=transcribe_and_summarize, | |
| inputs=[audio_file_input, youtube_url_input], | |
| outputs=[log_output, transcription_output, blog_summary_output]) | |
| youtube_url_input.change(fn=update_video_preview, | |
| inputs=youtube_url_input, | |
| outputs=video_preview) | |
| demo.load(fn=update_video_preview, | |
| inputs=youtube_url_input, | |
| outputs=video_preview) | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) | |