Update app.py

app.py

@@ -9,27 +9,29 @@ accelerator = Accelerator()
 # Print a description of the current configuration
 print("Accelerator State:", accelerator.state)
 
-# Define the path to your custom model
+# Define the path to your custom model or use a pre-trained Hugging Face model
 model_path = (
     "../Models/models--sshleifer--distilbart-cnn-12-6/snapshots"
     "/a4f8f3ea906ed274767e9906dbaede7531d660ff"
 )
 
-# Initialize the text summarization pipeline
+# Initialize the text summarization pipeline for CPU usage
 try:
     text_summary = pipeline(
         "summarization",
-        model=model_path,
-        torch_dtype=torch.bfloat16,  # Use bfloat16 for better performance on supported hardware
-        device=0 if torch.cuda.is_available() else -1  # Use GPU if available
+        model=model_path,  # Use the custom model path
+        device=-1  # Force usage of CPU
     )
 except Exception as e:
     print(f"Error initializing the summarization pipeline: {e}")
-    raise
+    print("Switching to the default model from Hugging Face.")
+    # Fallback to default model from Hugging Face if the custom model fails
+    text_summary = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", device=-1)
 
 # Define the Gradio interface function
 def summary(input_text):
     try:
+        # Generate summary using the text_summary pipeline
         output = text_summary(input_text)
         return output[0]['summary_text']
     except Exception as e:
@@ -41,11 +43,11 @@ gr.close_all()
 # Define the Gradio interface
 demo = gr.Interface(
     fn=summary,
-    inputs=[gr.Textbox(label="Input text to summarize", lines=6)],
-    outputs=[gr.Textbox(label="Summarized text", lines=4)],
+    inputs=gr.Textbox(label="Input Text to Summarize", lines=6, placeholder="Enter text here..."),
+    outputs=gr.Textbox(label="Summarized Text", lines=4),
     title="GenAIProject01: Text Summarizer",
-    description="THIS APPLICATION SUMMARIZE INPUT TEXT USING A PRE-TRAINED MODEL."
+    description="THIS APPLICATION WILL BE USED TO SUMMARIZE INPUT TEXT USING A PRE-TRAINED MODEL."
 )
 
 # Launch the Gradio app
-demo.launch()
+demo.launch(share=True)