update major files

README.md

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+---
+title: Jumps
+emoji: 👀
+colorFrom: yellow
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.44.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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)

requirements.txt

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+gradio
+torch
+faster-whisper
+yt-dlp
+openai

test.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "81d301b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detected language 'th' with probability 0.993038\n",
+      "[0.00s -> 6.72s] เช่น ลุงแดงบอกว่า การเล่นเนี่ย สมมุติเล่นคอร์ดสี่ คอร์ดสี่อย่างงี้\n",
+      "[6.72s -> 11.88s] คอร์ดสี่อย่างงี้ มันถูกทั้งหมด แต่เวลาเอาไปใช้งานจริงจริง\n",
+      "[11.88s -> 15.60s] มันจะทําอย่างนั้นไม่ได้ มันต้องเลือกเอาว่าเล่นอะไรที่มันดีที่สุด\n",
+      "[15.60s -> 19.50s] เออ ลูกหลานลองฟังเสียงคอร์ด เงื้อเสียงมันต่างกัน\n",
+      "[19.50s -> 23.10s] ฟังแบบนี้มันกําแก่งนะ เนี้ย\n",
+      "[24.78s -> 30.58s] แล้วแขมเล่นไปต้องคอยระวัง ระวังไอ้สายห้ากับหกด้วย\n",
+      "[30.58s -> 32.98s] เดี๋ยวมันจะวิ่งออกมากวนกัน เพราะปลิ๊กมันขบยาก\n",
+      "[32.98s -> 35.54s] เดี๋ยวมันปลายไปโดนนิดหนึ่ง มันก็ออกแล้ว\n",
+      "[35.54s -> 40.58s] เราจะดิดหกสายฟังให้ดีนะลูกหลาย ถ้าจับแบบนี้\n",
+      "[40.58s -> 45.98s] บอร์ด ดัง บอร์ด เห็นไหม เล่นแบบนี้ก็เล่นในทั่วไป\n",
+      "[45.98s -> 50.18s] เสียงแรงต่างมา ไม่ผิดนะ แต่ก็ดีแบบนั้น เอาดี ๆ เลย\n",
+      "[50.18s -> 54.50s] บอร์ด ชัดเจน บอร์ด เห็นไหม แล้วดีดกันเลย\n"
+     ]
+    }
+   ],
+   "source": [
+    "from faster_whisper import WhisperModel\n",
+    "\n",
+    "model_size = \"large-v3\"\n",
+    "\n",
+    "model = WhisperModel(model_size, device=\"cpu\", compute_type=\"int8\")\n",
+    "\n",
+    "segments, info = model.transcribe(\"bacfd788-dd5c-4ff3-851a-45bbf742acd5.mp3\", beam_size=5)\n",
+    "\n",
+    "print(\"Detected language '%s' with probability %f\" % (info.language, info.language_probability))\n",
+    "\n",
+    "for segment in segments:\n",
+    "    print(\"[%.2fs -> %.2fs] %s\" % (segment.start, segment.end, segment.text))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Jumps",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}