Update app.py

app.py

@@ -1,40 +1,32 @@
 import gradio as gr
 from transformers import pipeline
-import spaces
 import torch
 
-zero = torch.Tensor([0]).cuda()
-print(zero.device)
-
-
 # Check if GPU is available for FP16 inference
 device = 0 if torch.cuda.is_available() else -1
 torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-# Load Pipelines with FP16 (if GPU available)
-summarization = pipeline("summarization", model="facebook/bart-large-cnn", device=device)
-
+# Load Summarization Pipeline
+summarization = pipeline("summarization", model="facebook/bart-large-cnn", device=device, torch_dtype=torch_dtype)
 
-@spaces.GPU
 def summarize_text(text):
+    # Ensure the input is not too short for summarization
+    if len(text.split()) < 30:
+        return "Please provide a longer text (at least 30 words) for better summarization."
+    
     output = summarization(text, max_length=100, min_length=30, do_sample=False)
     return output[0]['summary_text']
 
-
-
-
 # Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 Transformers Pipeline with FP16 Inference")
+    gr.Markdown("# 🤖 Text Summarization with FP16 Inference")
     
-
     with gr.Tab("Text Summarization"):
-        summary_input = gr.Textbox(label="Text to Summarize ", lines=5, placeholder="Paste long text here...")
-        summary_btn = gr.Button("Summarize")
+        summary_input = gr.Textbox(label="Text to Summarize", lines=10, placeholder="Paste long text here...")
         summary_output = gr.Textbox(label="Summary")
-        summary_btn.click(summarize_text, inputs=summary_input, outputs=summary_output)
-
-   
+        summary_btn = gr.Button("Summarize")
+        summary_btn.click(fn=summarize_text, inputs=summary_input, outputs=summary_output)
 
 # Launch App
 demo.launch()
+