Create app.py

app.py

@@ -0,0 +1,40 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load models and tokenizers using Hugging Face's pipeline
+# @st.cache_data()
+def load_pipelines():
+    bart_pipeline = pipeline("summarization", model="facebook/bart-large-cnn")
+    t5_pipeline = pipeline("summarization", model="t5-large")
+    pegasus_pipeline = pipeline("summarization", model="google/pegasus-cnn_dailymail")
+    
+    return {
+        'BART': bart_pipeline,
+        'T5': t5_pipeline,
+        'Pegasus': pegasus_pipeline,
+    }
+
+prompt = """
+Summarize the below paragraph
+"""
+
+# Streamlit app layout
+st.title("Text Summarization with Pre-trained Models (BART, T5, Pegasus)")
+
+text_input = st.text_area("Enter text to summarize:")
+
+if st.button("Generate Summary"):
+    if text_input:
+        pipelines = load_pipelines()
+        summaries = {}
+        for model_name, pipeline in pipelines.items():
+            summary = pipeline(f"{prompt}\n{text_input}", max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)[0]['summary_text']
+            summaries[model_name] = summary
+
+        st.subheader("Summaries")
+        for model_name, summary in summaries.items():
+            st.write(f"**{model_name}**")
+            st.write(summary.replace('<n>', ''))
+            st.write("---")
+    else:
+        st.error("Please enter text to summarize.")