Create app.py

app.py

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+import torch
+import whisper
+from transformers import pipeline
+import gradio as gr
+import concurrent.futures
+
+# ✅ Load models once (Prevents reloading in every function call)
+print("Loading models...")
+whisper_model = whisper.load_model("small")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+question_generator = pipeline("text2text-generation", model="google/flan-t5-large")
+print("Models loaded successfully!")
+
+def transcribe_audio(audio_path):
+    result = whisper_model.transcribe(audio_path)
+    return result["text"]
+
+def summarize_text(text):
+    text_chunks = [text[i:i+1024] for i in range(0, len(text), 1024)]  
+    summaries = summarizer(text_chunks, max_length=200, min_length=50, do_sample=False)
+    return " ".join([s['summary_text'] for s in summaries])
+
+def generate_questions(text):
+    text_chunks = [text[i:i+1024] for i in range(0, len(text), 1024)]
+    questions = []
+
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        future_questions = [
+            executor.submit(
+                lambda chunk: question_generator(
+                    f"Generate some meaningful questions based on the topic from following passage: {chunk}", 
+                    max_length=100, num_return_sequences=3, do_sample=True
+                ),
+                chunk
+            ) for chunk in text_chunks
+        ]
+        
+        for future in future_questions:
+            generated = future.result()
+            questions.extend([q['generated_text'] for q in generated])
+
+    return "\n".join(questions)
+
+def process_audio(audio_path):
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        transcribe_future = executor.submit(transcribe_audio, audio_path)
+        transcript = transcribe_future.result()
+        
+        summarize_future = executor.submit(summarize_text, transcript)
+        questions_future = executor.submit(generate_questions, transcript)
+        
+        summary = summarize_future.result()
+        questions = questions_future.result()
+    
+    combined_text = f"📝 Transcription:\n{transcript}\n\n📜 Summary:\n{summary}\n\n🤔 Practice Questions:\n{questions}"
+    file_path = "lecture_summary.txt"
+
+    with open(file_path, "w", encoding="utf-8") as f:
+        f.write(combined_text)
+
+    return transcript, summary, questions, file_path
+
+def gradio_interface(audio):
+    return process_audio(audio)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎙 LectureGenie: Transcribe, Summarize & Quiz")
+    gr.Markdown("Upload a lecture audio file. The system will **transcribe**, **summarize**, and **generate questions** automatically.")
+
+    audio_input = gr.Audio(type="filepath", label="🎤 Upload Audio File", interactive=True, elem_classes="small-audio-box")
+    submit_btn = gr.Button("Submit", elem_id="submit-btn")
+
+    with gr.Row():
+        with gr.Column():
+            transcript_box = gr.Textbox(label="📝 Transcription", lines=10, interactive=False, show_copy_button=True)
+
+        with gr.Column():
+            summary_box = gr.Textbox(label="📜 Summary", lines=10, interactive=False, show_copy_button=True)
+
+        with gr.Column():
+            questions_box = gr.Textbox(label="🤔 Practice Questions", lines=10, interactive=False, show_copy_button=True)
+
+    download_btn = gr.File(label="📥 Download All", interactive=False, visible=False)
+    download_button = gr.Button("📥 Download", elem_id="download-btn")
+
+    submit_btn.click(
+        gradio_interface,
+        inputs=[audio_input],
+        outputs=[transcript_box, summary_box, questions_box, download_btn]
+    )
+
+    download_button.click(lambda x: x, inputs=[download_btn], outputs=[download_btn])
+
+demo.launch()