Update app.py

app.py

@@ -1,21 +1,34 @@
+
+
+# Step 2: Import libraries
 import gradio as gr
 from transformers import pipeline
 
-# Step 3: Define the summarization function
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+# Step 3: Define the summarization function for multiple models
+summarizers = {
+    "BART (facebook/bart-large-cnn)": pipeline("summarization", model="facebook/bart-large-cnn"),
+    "T5 (t5-small)": pipeline("summarization", model="t5-small"),
+    "Pegasus (google/pegasus-xsum)": pipeline("summarization", model="google/pegasus-xsum"),
+    "DistilBART (sshleifer/distilbart-cnn-12-6)": pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+}
 
-def summarize(text):
+def summarize(text, model_name):
+    summarizer = summarizers[model_name]
     summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
     return summary[0]['summary_text']
 
 # Step 4: Create the Gradio interface
 iface = gr.Interface(
     fn=summarize,
-    inputs="textbox",
+    inputs=[
+        gr.Textbox(lines=10, label="Input Text"),
+        gr.Dropdown(choices=list(summarizers.keys()), label="Choose Model")
+    ],
     outputs="textbox",
     title="Text Summarizer",
-    description="Summarize text using the facebook/bart-large-cnn model from Hugging Face"
+    description="Summarize text using various models from Hugging Face"
 )
 
 # Step 5: Launch the interface
-iface.launch()
+iface.launch()
+