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app.py

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+import gradio as gr
+import whisper
+from transformers import pipeline
+
+# Load models
+print("Loading models...")
+whisper_model = whisper.load_model("base")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def transcribe(audio_path):
+    if audio_path is None:
+        return "Please record some audio."
+    result = whisper_model.transcribe(audio_path)
+    return result["text"]
+
+def summarize(text):
+    if not text.strip():
+        return "No transcription available to summarize."
+    summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
+    return summary[0]['summary_text']
+
+with gr.Blocks() as app:
+    gr.Markdown("## 🎙️ Real-Time Transcription & Summarization Tool\nSpeak into your mic and generate a summary.")
+
+    with gr.Row():
+        audio_input = gr.Audio(source="microphone", type="filepath", label="🎧 Record Audio")
+        transcription_output = gr.Textbox(label="📝 Transcription", lines=6, interactive=False)
+
+    transcribe_button = gr.Button("Transcribe")
+    transcribe_button.click(fn=transcribe, inputs=audio_input, outputs=transcription_output)
+
+    with gr.Row():
+        summarize_button = gr.Button("Generate Summary")
+        summary_output = gr.Textbox(label="📄 Summary", lines=6, interactive=False)
+
+    summarize_button.click(fn=summarize, inputs=transcription_output, outputs=summary_output)
+
+app.launch()

requirements.txt

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+gradio
+transformers
+torch
+torchaudio
+whisper