Update app.py

app.py

@@ -1,28 +1,21 @@
-# Step 2: Install the latest version of Gradio
-#!pip install gradio
-
-# Step 3: Verify the installation
 import gradio as gr
+from transformers import pipeline
 
-print("Gradio version:", gr.__version__)
-
-# Step 4: Define the Gradio interface using the latest API
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+# Step 3: Define the summarization function
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
 def summarize(text):
-    tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
-    model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
-    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
-    summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
-    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-
-iface1 = gr.Interface(fn=summarize, inputs="textbox", outputs="textbox", title="Text Summarizer")
-iface2 = gr.Interface(fn=summarize, inputs="textbox", outputs="textbox", title="Text Summarizer")
+    summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
+    return summary[0]['summary_text']
 
-# Combine interfaces using the latest Gradio Blocks API
-with gr.Blocks() as demo:
-    with gr.Row():
-        iface1.render()
-        
+# Step 4: Create the Gradio interface
+iface = gr.Interface(
+    fn=summarize,
+    inputs="textbox",
+    outputs="textbox",
+    title="Text Summarizer",
+    description="Summarize text using the facebook/bart-large-cnn model from Hugging Face"
+)
 
-demo.launch()
+# Step 5: Launch the interface
+iface.launch()