first commit

app.py

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+import gradio as gr
+from transformers import pipeline
+from gtts import gTTS
+
+def audio(text):
+    # Summarize the input text using the Hugging Face model
+    # Load the pre-trained summarization model from Hugging Face
+    summarizer =  pipeline("summarization", model="facebook/bart-large-cnn")
+    summary = summarizer(text, do_sample=False)[0]["summary_text"]
+    # Convert the summary to audio using Google Text-to-Speech
+    tts = gTTS(summary)
+    tts.save("summary.mp3")
+    return "summary.mp3"
+
+def text_summary(text):
+    # Summarize the input text using the Hugging Face model
+    # Load the pre-trained summarization model from Hugging Face
+    summarizer =  pipeline("summarization", model="facebook/bart-large-cnn")
+    summary = summarizer(text, do_sample=False)[0]["summary_text"]
+    return summary
+
+# using streamlit to create a web app to display the summary or play the audio
+
+import streamlit as st
+
+st.title("📌 Your Personal Audio Summary")
+text = st.text_input("Enter text to summarize")
+
+#choose between text summary or audio summary
+option = st.selectbox("Choose between text summary  or audio summary", ("📃Text Summary", "🗣Audio Summary"))
+
+if st.button("Summarize"):
+    if option == "📃Text Summary":
+        summary = text_summary(text)
+        st.write(summary)
+    if option == "🗣Audio Summary":
+        file_path = audio(text)
+        st.audio(file_path)

requirement.txt

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+streamlit
+transformers
+gtts