Update app.py

app.py

@@ -1,65 +1,66 @@
 import os
 import gradio as gr
-import requests
 import tempfile
 import yt_dlp
+from pydub import AudioSegment
 from groq import Groq
 from huggingface_hub import InferenceClient
 
-# ----------------------------
 # ✅ Environment Variables
-# ----------------------------
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
 
 if not GROQ_API_KEY or not HUGGINGFACE_API_TOKEN:
-    raise EnvironmentError("Please set GROQ_API_KEY and HUGGINGFACE_API_TOKEN in Hugging Face settings.")
+    raise EnvironmentError("Please set GROQ_API_KEY and HUGGINGFACE_API_TOKEN.")
 
-# ----------------------------
-# ✅ Initialize Clients
-# ----------------------------
 groq_client = Groq(api_key=GROQ_API_KEY)
 hf_client = InferenceClient(token=HUGGINGFACE_API_TOKEN)
 
-# ----------------------------
 # ✅ Download YouTube Audio
-# ----------------------------
 def download_youtube_audio(youtube_url):
-    try:
-        with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp_file:
-            ydl_opts = {
-                'format': 'bestaudio/best',
-                'outtmpl': tmp_file.name,
-                'quiet': True,
-                'postprocessors': [{
-                    'key': 'FFmpegExtractAudio',
-                    'preferredcodec': 'mp3',
-                    'preferredquality': '192',
-                }],
-            }
-            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-                ydl.download([youtube_url])
-            return tmp_file.name
-    except Exception as e:
-        return f"❌ Error downloading audio: {e}"
-
-# ----------------------------
-# ✅ Transcribe with Groq Whisper
-# ----------------------------
+    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp_file:
+        ydl_opts = {
+            "format": "bestaudio/best",
+            "outtmpl": tmp_file.name,
+            "quiet": True,
+            "postprocessors": [{
+                "key": "FFmpegExtractAudio",
+                "preferredcodec": "mp3",
+                "preferredquality": "192",
+            }],
+        }
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            ydl.download([youtube_url])
+        return tmp_file.name
+
+# ✅ Split long audio into chunks (max 5 mins)
+def split_audio(file_path, max_duration_ms=5*60*1000):
+    audio = AudioSegment.from_file(file_path)
+    chunks = []
+    for i in range(0, len(audio), max_duration_ms):
+        chunk = audio[i:i + max_duration_ms]
+        temp_chunk = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
+        chunk.export(temp_chunk.name, format="mp3")
+        chunks.append(temp_chunk.name)
+    return chunks
+
+# ✅ Transcribe with Groq (chunk-wise)
 def transcribe_audio(audio_path):
     try:
-        with open(audio_path, "rb") as f:
-            transcription = groq_client.audio.transcriptions.create(
-                model="whisper-large-v3",
-                file=f
-            )
-        return transcription.text
+        chunks = split_audio(audio_path)
+        transcript = ""
+        for i, chunk in enumerate(chunks):
+            with open(chunk, "rb") as f:
+                response = groq_client.audio.transcriptions.create(
+                    model="whisper-large-v3",
+                    file=f
+                )
+            transcript += response.text + "\n"
+        return transcript.strip()
     except Exception as e:
         return f"❌ Error during transcription: {e}"
 
-# ----------------------------
-# ✅ Summarize in English or Urdu
-# ----------------------------
+# ✅ Summarize
 def summarize_text(text, lang):
     try:
         if lang == "English":
@@ -67,71 +68,49 @@ def summarize_text(text, lang):
             prompt = f"Summarize the following text in English:\n\n{text}"
         else:
             model = "facebook/mbart-large-50-many-to-many-mmt"
-            prompt = f"مندرجہ ذیل انگریزی متن کا جامع اردو خلاصہ لکھیں:\n\n{text}"
+            prompt = f"مندرجہ ذیل عبارت کا جامع اردو خلاصہ تحریر کریں:\n\n{text}"
 
         output = hf_client.text_generation(
             model=model,
             prompt=prompt,
             max_new_tokens=250,
             temperature=0.7,
-            do_sample=False,
         )
         return output
     except Exception as e:
         return f"❌ Error during summarization: {e}"
 
-# ----------------------------
-# ✅ Main Function: YouTube or File
-# ----------------------------
+# ✅ Main Pipeline
 def process_input(youtube_url, audio_file, lang):
-    audio_path = None
-
-    # Step 1: Determine source
     if youtube_url:
         audio_path = download_youtube_audio(youtube_url)
-        if "❌" in audio_path:
-            return audio_path, "", ""
     elif audio_file:
         audio_path = audio_file
     else:
-        return "❌ Please upload audio or paste a YouTube link.", "", ""
+        return "❌ Please upload an audio file or paste a YouTube link.", "", ""
 
-    # Step 2: Transcription
     transcript = transcribe_audio(audio_path)
     if transcript.startswith("❌"):
         return transcript, "", ""
 
-    # Step 3: Summarization
     summary = summarize_text(transcript, lang)
-
     return "✅ Transcription Completed!", transcript, summary
 
-# ----------------------------
-# ✅ Gradio UI
-# ----------------------------
-with gr.Blocks(title="🎧 Audio & YouTube Transcriber + Summarizer") as app:
-    gr.Markdown("## 🎧 English/Urdu Audio Summarizer\nUpload an audio file **or** paste a YouTube link below:")
+# ✅ Gradio Interface
+with gr.Blocks(title="🎧 Urdu/English Audio Summarizer") as app:
+    gr.Markdown("## 🎧 Urdu & English Audio Summarizer\nUpload audio or paste YouTube link below:")
 
     with gr.Row():
         youtube_link = gr.Textbox(label="📺 YouTube Link (optional)")
-        language_choice = gr.Dropdown(
-            ["English", "Urdu"],
-            label="🌐 Choose Summary Language",
-            value="English"
-        )
+        language_choice = gr.Dropdown(["English", "Urdu"], value="English", label="🌐 Summary Language")
 
     audio_input = gr.Audio(type="filepath", label="🎙️ Upload Audio File (optional)")
     btn = gr.Button("🚀 Transcribe & Summarize")
 
-    with gr.Row():
-        status = gr.Textbox(label="Status")
+    status = gr.Textbox(label="Status")
     transcript_box = gr.Textbox(label="📝 Transcription", lines=8)
     summary_box = gr.Textbox(label="🧩 Summary", lines=8)
 
-    btn.click(
-        fn=process_input,
-        inputs=[youtube_link, audio_input, language_choice],
-        outputs=[status, transcript_box, summary_box]
-    )
+    btn.click(process_input, [youtube_link, audio_input, language_choice], [status, transcript_box, summary_box])
 
 app.launch()