Create app.py

app.py

@@ -0,0 +1,71 @@
+import gradio as gr
+import os
+import subprocess
+from transformers import pipeline
+from langdetect import detect
+
+def download_audio(youtube_url):
+    output_file = "audio.mp3"
+    # Remove old file if exists
+    if os.path.exists(output_file):
+        os.remove(output_file)
+    cmd = [
+        "yt-dlp", "-x", "--audio-format", "mp3", "-o", output_file, youtube_url
+    ]
+    subprocess.run(cmd, check=True)
+    return output_file
+
+def process_youtube(youtube_url):
+    try:
+        audio_path = download_audio(youtube_url)
+    except Exception as e:
+        return "Error downloading audio: " + str(e), "", "", ""
+    try:
+        asr = pipeline("automatic-speech-recognition", model="openai/whisper-large")
+        result = asr(audio_path)
+        transcript = result["text"]
+    except Exception as e:
+        return "Error in transcription: " + str(e), "", "", ""
+    try:
+        detected_lang = detect(transcript)
+    except Exception:
+        detected_lang = "unknown"
+    lang_map = {'en': 'English', 'hi': 'Hindi', 'ta': 'Tamil'}
+    lang_text = lang_map.get(detected_lang, detected_lang)
+    transcript_en = transcript
+    if detected_lang != "en":
+        try:
+            asr_translate = pipeline(
+                "automatic-speech-recognition",
+                model="openai/whisper-large",
+                task="translate"
+            )
+            result_translate = asr_translate(audio_path)
+            transcript_en = result_translate["text"]
+        except Exception as e:
+            transcript_en = f"Error translating: {e}"
+    try:
+        summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+        summary = summarizer(transcript_en, max_length=100, min_length=30, do_sample=False)
+        summary_text = summary[0]["summary_text"]
+    except Exception as e:
+        summary_text = f"Error summarizing: {e}"
+    if os.path.exists(audio_path):
+        os.remove(audio_path)
+    return lang_text, transcript, transcript_en, summary_text
+
+with gr.Blocks() as demo:
+    gr.Markdown("## YouTube Transcript, Translation & Summary (Powered by Whisper + Hugging Face)")
+    url_input = gr.Textbox(label="YouTube URL")
+    btn = gr.Button("Process")
+    lang_out = gr.Textbox(label="Detected Language")
+    transcript_out = gr.Textbox(label="Original Transcript")
+    transcript_en_out = gr.Textbox(label="English Transcript (if translated)")
+    summary_out = gr.Textbox(label="Summary")
+    btn.click(
+        process_youtube,
+        inputs=[url_input],
+        outputs=[lang_out, transcript_out, transcript_en_out, summary_out]
+    )
+
+demo.launch()