Update app.py

app.py

@@ -1,10 +1,16 @@
 import gradio as gr
-
 from PyPDF2 import PdfReader
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 from gtts import gTTS
 from io import BytesIO
 
+# Check if running in IPython environment
+try:
+    from IPython.display import Audio
+    ipython_available = True
+except ImportError:
+    ipython_available = False
+
 # Define model and tokenizer
 model_name = "ArtifactAI/led_large_16384_arxiv_summarization"
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
@@ -33,17 +39,29 @@ def summarize_pdf_abstract(pdf_data):
     outputs = model.generate(**inputs)
     summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-    speech = gTTS(summary, lang="en")
-    speech_bytes = speech.get_wav_data()
+    if ipython_available:
+        speech = gTTS(summary, lang="en")
+        speech_bytes = speech.get_wav_data()
+    else:
+        speech_bytes = None
 
     return {"summary": summary, "audio": speech_bytes}
 
-# Create Gradio interface
-interface = gr.Interface(
-    fn=summarize_pdf_abstract,
-    inputs=[gr.File(label="Upload PDF", mimetypes=["application/pdf"])],
-    outputs=[gr.Text(label="One-sentence summary"), gr.Audio(label="Summary audio")],
-)
+# Modify the Gradio interface based on IPython availability
+if ipython_available:
+    # If running in IPython, include the Audio component
+    interface = gr.Interface(
+        fn=summarize_pdf_abstract,
+        inputs=[gr.File(label="Upload PDF", mimetypes=["application/pdf"])],
+        outputs=[gr.Text(label="One-sentence summary"), gr.Audio(label="Summary audio")],
+    )
+else:
+    # If not running in IPython, exclude the Audio component
+    interface = gr.Interface(
+        fn=summarize_pdf_abstract,
+        inputs=[gr.File(label="Upload PDF", mimetypes=["application/pdf"])],
+        outputs=[gr.Text(label="One-sentence summary")],
+    )
 
 # Launch the Hugging Face Space
 interface.launch(title="PDF Abstract Summarizer")