Update app.py

app.py

@@ -10,26 +10,46 @@ qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distil
 
 stored_transcript = ""
 
-def transcribe_and_summarize(video_file):
+def transcribe_from_video(video_file):
     global stored_transcript
-
     if video_file is None:
-        return "Error: No file provided.", ""
-
+        return "Error: No video file provided.", ""
+    
     try:
         video = VideoFileClip(video_file)
         audio_path = "temp_audio.wav"
         video.audio.write_audiofile(audio_path, codec='pcm_s16le')
 
         transcription_result = asr(audio_path, return_timestamps=True)
-        transcribed_text = " ".join([segment['text'] for segment in transcription_result['chunks']])
+        transcribed_text = " ".join([chunk["text"] for chunk in transcription_result["chunks"]])
+        stored_transcript = transcribed_text
+
+        if len(transcribed_text.split()) < 50:
+            summarized_text = "Text too short to summarize."
+        else:
+            summary = summarizer(transcribed_text, max_length=500, min_length=100, do_sample=False)
+            summarized_text = summary[0]["summary_text"]
+
+        return transcribed_text, summarized_text
+
+    except Exception as e:
+        return f"Error: {str(e)}", ""
+
+def transcribe_from_audio(audio_file):
+    global stored_transcript
+    if audio_file is None:
+        return "Error: No audio recorded.", ""
+
+    try:
+        transcription_result = asr(audio_file, return_timestamps=True)
+        transcribed_text = " ".join([chunk["text"] for chunk in transcription_result["chunks"]])
         stored_transcript = transcribed_text
 
         if len(transcribed_text.split()) < 50:
             summarized_text = "Text too short to summarize."
         else:
-            summary_result = summarizer(transcribed_text, max_length=500, min_length=100, do_sample=False)
-            summarized_text = summary_result[0]['summary_text']
+            summary = summarizer(transcribed_text, max_length=500, min_length=100, do_sample=False)
+            summarized_text = summary[0]["summary_text"]
 
         return transcribed_text, summarized_text
 
@@ -39,34 +59,43 @@ def transcribe_and_summarize(video_file):
 def answer_question(question):
     global stored_transcript
     if not stored_transcript:
-        return "Please transcribe a video first."
+        return "Please transcribe a video or record audio first."
     result = qa_pipeline(question=question, context=stored_transcript)
-    return result['answer']
+    return result["answer"]
 
+# UI
 with gr.Blocks(css="""
 body { background-color: black !important; }
 .gradio-container { color: #FFFF33 !important; }
 button { background-color: #FFFF33 !important; color: black !important; border: none !important; }
-input, textarea, .gr-textbox, .gr-video { background-color: #111 !important; color: #FFFF33 !important; border-color: #FFFF33 !important; }
+input, textarea, .gr-textbox, .gr-video, .gr-audio { background-color: #111 !important; color: #FFFF33 !important; border-color: #FFFF33 !important; }
 """) as iface:
-    gr.HTML("<h1 style='color:#FFFF33'>🎥 Video Transcriber, Summarizer & Q&A Tool</h1>")
-    gr.HTML("<p style='color:#CCCC33'>Upload a video to get a transcript, summary, and ask questions about its content.</p>")
+    gr.HTML("<h1 style='color:#FFFF33'>🎤 Video & Voice Transcriber, Summarizer & Q&A</h1>")
+    gr.HTML("<p style='color:#CCCC33'>Upload a video or record speech to get transcript, summary, and ask questions.</p>")
 
-    with gr.Tab("📝 Transcription & Summary"):
+    with gr.Tab("🎥 Video Upload"):
         video_input = gr.Video(label="Upload Video (.mp4)", interactive=True)
-        transcribe_btn = gr.Button("🚀 Transcribe and Summarize")
-        transcribed_text = gr.Textbox(label="Transcribed Text", lines=8, interactive=False)
-        summarized_text = gr.Textbox(label="Summarized Text", lines=8, interactive=False)
+        transcribe_btn = gr.Button("🚀 Transcribe from Video")
+        transcribed_text_v = gr.Textbox(label="Transcribed Text", lines=8, interactive=False)
+        summarized_text_v = gr.Textbox(label="Summarized Text", lines=8, interactive=False)
+
+        transcribe_btn.click(fn=transcribe_from_video, inputs=video_input, outputs=[transcribed_text_v, summarized_text_v])
+
+    with gr.Tab("🎙️ Record Speech"):
+        audio_input = gr.Audio(source="microphone", type="filepath", label="Record Audio")
+        record_btn = gr.Button("🎧 Transcribe from Audio")
+        transcribed_text_a = gr.Textbox(label="Transcribed Text", lines=8, interactive=False)
+        summarized_text_a = gr.Textbox(label="Summarized Text", lines=8, interactive=False)
 
-        transcribe_btn.click(fn=transcribe_and_summarize, inputs=video_input, outputs=[transcribed_text, summarized_text])
+        record_btn.click(fn=transcribe_from_audio, inputs=audio_input, outputs=[transcribed_text_a, summarized_text_a])
 
     with gr.Tab("❓ Ask Questions"):
-        question_input = gr.Textbox(label="Ask a question based on the transcript", placeholder="E.g., What is the main topic?")
+        question_input = gr.Textbox(label="Ask a question about the transcript", placeholder="E.g., What was the main topic?")
         ask_btn = gr.Button("🔍 Get Answer")
         answer_output = gr.Textbox(label="Answer", interactive=False)
 
         ask_btn.click(fn=answer_question, inputs=question_input, outputs=answer_output)
 
-# Launch app
+# Launch
 port = int(os.environ.get('PORT1', 7860))
 iface.launch(share=True, server_port=port)