diff --git "a/app.py" "b/app.py"
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+++ "b/app.py"
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+# -*- coding: utf-8 -*-
+"""Q_A_Whisper_summarize.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+ https://colab.research.google.com/drive/1uchqsFgN6JyJX5rYfyMy3H__ThfIRGXJ
+
+**Question:**
+"How can you create a Gradio app that allows users to upload an audio file, transcribe it using the Whisper tiny model, and then summarize the transcription using Hugging Face's BART summarization model?"
+
+This question prompts the user to build the Gradio app using both Whisper for transcription and the BART model for summarization.
+
+
+"""
+
+!pip install gradio
+!pip install git+https://github.com/openai/whisper.git
+!pip install transformers
+
+import gradio as gr
+import whisper
+from transformers import pipeline
+
+# Load the tiny Whisper model
+whisper_model = whisper.load_model("tiny")
+model = whisper.load_model("base")
+
+# Load the text summarization model from Hugging Face
+summarizer = pipeline(task="summarization", model="facebook/bart-large-cnn")
+
+# Function to transcribe and summarize the audio file
+def transcribe_and_summarize(audio):
+ # Step 1: Transcribe the audio using Whisper
+ transcription_result = whisper_model.transcribe(audio)
+ transcription = transcription_result['text']
+
+ # Step 2: Summarize the transcription
+ summary = summarizer(transcription, min_length=10, max_length=100)
+ summary_text = summary[0]['summary_text']
+
+ return transcription, summary_text
+
+# Define the Gradio interface
+interface = gr.Interface(
+ fn=transcribe_and_summarize, # Function to run
+ inputs=gr.Audio(type="filepath", label="Upload your audio file"), # Input audio field
+ outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Summary")], # Output fields
+ title="Whisper Tiny Transcription and Summarization",
+ description="Upload an audio file, get the transcription from Whisper tiny model and the summarized version using Hugging Face."
+)
+
+# Launch the Gradio app
+interface.launch(debug=True)
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