Update app.py

app.py

@@ -1,105 +1,244 @@
 import streamlit as st
-import requests
 from gtts import gTTS
 from urllib.parse import urlparse, parse_qs
-from youtube_transcript_api import YouTubeTranscriptApi
+from youtube_transcript_api import YouTubeTranscriptApi, TranscriptUnavailable, NoTranscriptFound
 import unicodedata
 from deepmultilingualpunctuation import PunctuationModel
 from transformers import pipeline
-
-
-def summarize_video(url):
-  if "watch" in url:
-    pass
-  else:
-    url = url.replace("youtu.be/", "www.youtube.com/watch?v=")
-
-  parsed_url = urlparse(url)
-  video_id = parse_qs(parsed_url.query)['v'][0]
-
-  # Get the transcript 
-  transcript = YouTubeTranscriptApi.get_transcript(video_id)
-
-  # Combining all the lists into on unique list
-  text = []
-  for i in range(0, len(transcript)):
-      text.append(transcript[i]["text"])
-
-  # Join list items into one paragraph
-  video_transcript = " ".join(text)
-  print("Text transcript created")
-
-  print(video_transcript)
-
-  # Text normalization 
-  my_string = unicodedata.normalize('NFKD', video_transcript)
-  print("Text normalized")
-
-
-  # Add punctuation 
-  model = PunctuationModel()
-  result = model.restore_punctuation(video_transcript)
-  print("Punctuation restored")
-
-  # SUMMARIZATION 
-
-  # instantiate the summarization pipeline
-  summarization_pipeline = pipeline(
-      "summarization", 
-      model="t5-base", # you can choose a different model, depending on your requirements
-      tokenizer="t5-base" # you can choose a different tokenizer, depending on your requirements
-  )
-
-  # define the input text to summarize
-  input_text = result
-
-  # split the input text into smaller chunks
-  chunk_size = 5000
-  chunks = [input_text[i:i+chunk_size] for i in range(0, len(input_text), chunk_size)]
-
-  # summarize each chunk separately
-  summaries = []
-  for chunk in chunks:
-      summary = summarization_pipeline(chunk, max_length=200, min_length=30, do_sample=False)
-      summaries.append(summary[0]['summary_text'])
-
-  # combine the summaries of all chunks into a single summary
-  final_summary = " ".join(summaries)
-
-  # print the generated summary
-  return final_summary
-
-# Define the Streamlit app
-st.title("YouTube Summarizer")
-
-# Define the input form
-form = st.form(key="input_form")
-
-# Get the video ID from the URL
-video_url = form.text_input("Enter a YouTube video URL")
-
-# Submit button
-submit_button = form.form_submit_button("Summarize Video")
-
-# Handle form submissions
-if submit_button:
-    # Call the summarize_video function to get the summary
-    summary = summarize_video(video_url)
-
-    # Display the summary to the user
-    st.subheader("Summary")
-    st.write(summary)
-
-    # Convert text summary into audio
-    tts = gTTS(summary)
-    print("converting text to audio")
-    tts.save('Summary.mp3')
-
-    # Download audio transcript 
-    with open('Summary.mp3', 'rb') as f:
-        st.download_button('Download mp3', f, file_name='Summary.mp3')
-
-
-
-
-    
+import io
+import re
+
+# =============================================================================
+# CACHE MODELS - Agar tidak reload setiap kali form disubmit
+# =============================================================================
+@st.cache_resource
+def load_punctuation_model():
+    """Load punctuation model once and cache it"""
+    return PunctuationModel("oliverguhr/fullstop-punctuation-multilingual--large")
+
+@st.cache_resource
+def load_summarization_pipeline():
+    """Load summarization pipeline once and cache it"""
+    return pipeline(
+        "summarization",
+        model="t5-base",
+        tokenizer="t5-base",
+        device=0 if st.runtime.get_option("server.headless") == False and hasattr(st, 'runtime') else -1  # CPU fallback
+    )
+
+# =============================================================================
+# HELPER FUNCTIONS
+# =============================================================================
+def extract_video_id(url):
+    """Extract video ID from various YouTube URL formats"""
+    # Handle short URLs: youtu.be/VIDEO_ID
+    if "youtu.be" in url:
+        parsed = urlparse(url)
+        return parsed.path.lstrip('/')
+    
+    # Handle standard URLs: youtube.com/watch?v=VIDEO_ID
+    if "watch" in url:
+        parsed = urlparse(url)
+        params = parse_qs(parsed.query)
+        return params.get('v', [None])[0]
+    
+    # Handle embed URLs: youtube.com/embed/VIDEO_ID
+    if "embed" in url:
+        parsed = urlparse(url)
+        return parsed.path.split('/')[-1]
+    
+    # Handle short URLs without protocol
+    if re.match(r'^[a-zA-Z0-9_-]{11}$', url.strip()):
+        return url.strip()
+    
+    return None
+
+def get_transcript_text(video_id, language='en'):
+    """Fetch and combine transcript text from YouTube"""
+    try:
+        # Try to get transcript in preferred language, fallback to any available
+        transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
+        transcript = transcript_list.find_transcript([language])
+    except (TranscriptUnavailable, NoTranscriptFound):
+        # Fallback: get any available transcript
+        transcript = YouTubeTranscriptApi.list_transcripts(video_id).find_generated_transcript(['en'])
+    
+    transcript_data = transcript.fetch()
+    # Combine all text segments
+    text_segments = [item["text"] for item in transcript_data]
+    return " ".join(text_segments)
+
+def chunk_text(text, max_chars=4000):
+    """Split text into chunks respecting sentence boundaries"""
+    chunks = []
+    current_chunk = ""
+    
+    # Split by sentences (basic approach)
+    sentences = re.split(r'(?<=[.!?])\s+', text)
+    
+    for sentence in sentences:
+        if len(current_chunk) + len(sentence) <= max_chars:
+            current_chunk += sentence + " "
+        else:
+            if current_chunk:
+                chunks.append(current_chunk.strip())
+            current_chunk = sentence + " "
+    
+    if current_chunk:
+        chunks.append(current_chunk.strip())
+    
+    return chunks if chunks else [text]  # Fallback if no sentences found
+
+def normalize_text(text):
+    """Normalize unicode characters in text"""
+    return unicodedata.normalize('NFKD', text)
+
+# =============================================================================
+# MAIN SUMMARIZATION FUNCTION
+# =============================================================================
+def summarize_video(url, language='en'):
+    """Main function to summarize YouTube video"""
+    
+    # Extract video ID
+    video_id = extract_video_id(url)
+    if not video_id:
+        raise ValueError("Invalid YouTube URL. Please check the link and try again.")
+    
+    # Get transcript
+    with st.spinner("📝 Mengambil transkrip video..."):
+        video_transcript = get_transcript_text(video_id, language)
+    
+    if not video_transcript or len(video_transcript.strip()) < 50:
+        raise ValueError("Transkrip tidak ditemukan atau terlalu pendek. Video mungkin tidak memiliki subtitle.")
+    
+    # Normalize text
+    normalized_text = normalize_text(video_transcript)
+    
+    # Add punctuation
+    with st.spinner("✏️ Menambahkan tanda baca..."):
+        punctuation_model = load_punctuation_model()
+        punctuated_text = punctuation_model.restore_punctuation(normalized_text)
+    
+    # Summarization
+    with st.spinner("🤖 Meringkas konten..."):
+        summarizer = load_summarization_pipeline()
+        
+        # T5-base max input: ~512 tokens (~2000-3000 chars safe limit)
+        chunks = chunk_text(punctuated_text, max_chars=3000)
+        summaries = []
+        
+        for i, chunk in enumerate(chunks):
+            # T5 expects prefix "summarize: " for some versions
+            input_text = f"summarize: {chunk}" if "t5" in "t5-base" else chunk
+            
+            summary_result = summarizer(
+                input_text,
+                max_length=150,
+                min_length=30,
+                do_sample=False,
+                truncation=True
+            )
+            summaries.append(summary_result[0]['summary_text'])
+            st.progress(min((i + 1) / len(chunks), 1.0))
+        
+        final_summary = " ".join(summaries)
+    
+    return final_summary
+
+# =============================================================================
+# STREAMLIT APP
+# =============================================================================
+def main():
+    st.set_page_config(page_title="YouTube Summarizer", page_icon="🎬", layout="centered")
+    
+    st.title("🎬 YouTube Video Summarizer")
+    st.markdown("""
+        Masukkan URL video YouTube untuk mendapatkan ringkasan otomatis berbasis AI.
+        Mendukung video dengan subtitle/closed caption.
+    """)
+    
+    # Input form
+    with st.form(key="summarizer_form"):
+        video_url = st.text_input(
+            "🔗 URL Video YouTube",
+            placeholder="https://www.youtube.com/watch?v=..."
+        )
+        language = st.selectbox(
+            "🌐 Bahasa Transkrip (opsional)",
+            options=['en', 'id', 'es', 'fr', 'de', 'pt', 'auto'],
+            index=0,
+            help="Pilih bahasa transkrip. 'auto' akan mencoba mendeteksi otomatis."
+        )
+        col1, col2 = st.columns([1, 3])
+        with col1:
+            submit_button = st.form_submit_button("🚀 Ringkas", use_container_width=True)
+    
+    # Process submission
+    if submit_button:
+        if not video_url.strip():
+            st.error("⚠️ Harap masukkan URL video YouTube yang valid.")
+            return
+        
+        try:
+            # Generate summary
+            summary = summarize_video(video_url, language if language != 'auto' else 'en')
+            
+            # Display results
+            st.success("✅ Ringkasan berhasil dibuat!")
+            st.subheader("📄 Hasil Ringkasan")
+            st.markdown(f"> {summary}")
+            
+            # Text-to-Speech
+            with st.spinner("🔊 Membuat audio..."):
+                # Detect language for gTTS (simplified: default to 'en')
+                tts_lang = 'id' if any(kata in summary.lower() for kata in ['dan', 'yang', 'di', 'ke']) else 'en'
+                tts = gTTS(text=summary, lang=tts_lang, slow=False)
+                
+                # Convert to bytes for download (no file I/O)
+                audio_buffer = io.BytesIO()
+                tts.write_to_fp(audio_buffer)
+                audio_buffer.seek(0)
+            
+            # Download button
+            st.download_button(
+                label="📥 Download Ringkasan Audio (MP3)",
+                data=audio_buffer,
+                file_name="youtube_summary.mp3",
+                mime="audio/mpeg",
+                use_container_width=True
+            )
+            
+            # Copy summary to clipboard hint
+            st.code(summary, language="text")
+            st.caption("💡 Tip: Klik teks di atas untuk menyalin ringkasan.")
+            
+        except Exception as e:
+            st.error(f"❌ Terjadi kesalahan: {str(e)}")
+            with st.expander("🔍 Detail Error (untuk debugging)"):
+                st.exception(e)
+    
+    # Sidebar info
+    with st.sidebar:
+        st.header("ℹ️ Informasi")
+        st.markdown("""
+        **Fitur:**
+        - ✅ Ekstrak transkrip otomatis
+        - ✅ Penambahan tanda baca AI
+        - ✅ Ringkasan multi-bahasa
+        - ✅ Export ke audio MP3
+        
+        **Batasan:**
+        - Video harus memiliki subtitle/closed caption
+        - Durasi video sangat panjang mungkin diproses per bagian
+        - Model T5-base optimal untuk teks bahasa Inggris
+        
+        **Tips:**
+        - Gunakan video dengan subtitle resmi untuk hasil terbaik
+        - Untuk video bahasa Indonesia, pastikan subtitle tersedia
+        """)
+        
+        st.markdown("---")
+        st.caption("Dibuat dengan ❤️ menggunakan Streamlit & Hugging Face")
+
+if __name__ == "__main__":
+    main()