Update app.py

app.py

@@ -1,10 +1,10 @@
 import streamlit as st
 from transformers import pipeline
+from concurrent.futures import ThreadPoolExecutor
 import time
 
-# Load selected model's pipeline
-@st.cache_resource
-def load_pipeline(model_name):
+# Function to load a single model
+def load_model(model_name):
     if model_name == 'BART':
         return pipeline("summarization", model="facebook/bart-large-cnn")
     elif model_name == 'T5':
@@ -12,6 +12,18 @@ def load_pipeline(model_name):
     elif model_name == 'Pegasus':
         return pipeline("summarization", model="google/pegasus-cnn_dailymail")
 
+# Function to load all models concurrently
+@st.cache_resource
+def load_all_models():
+    model_names = ['BART', 'T5', 'Pegasus']
+    models = {}
+    with ThreadPoolExecutor() as executor:
+        futures = {executor.submit(load_model, name): name for name in model_names}
+        for future in futures:
+            model_name = futures[future]
+            models[model_name] = future.result()
+    return models
+
 # Streamlit app layout
 st.title("Text Summarization with Pre-trained Models (BART, T5, Pegasus)")
 
@@ -22,19 +34,23 @@ if text_input:
     word_count = len(text_input.split())
     st.write(f"**Word Count:** {word_count}")
 
-    model_choice = st.selectbox("Choose a model:", ['BART', 'T5', 'Pegasus'])
-
-    if st.button("Generate Summary"):
-        with st.spinner(f"Generating summary using {model_choice}..."):
+    if st.button("Generate Summaries"):
+        with st.spinner("Loading models and generating summaries..."):
             start_time = time.time()
-            summarizer = load_pipeline(model_choice)
-            summary = summarizer(text_input, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)[0]['summary_text']
+            models = load_all_models()
+            summaries = {}
+            for model_name, model in models.items():
+                summary = model(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
             end_time = time.time()
-            summary_word_count = len(summary.split())
 
-        st.subheader(f"Summary using {model_choice}")
-        st.write(summary.replace('<n>', ''))
-        st.write(f"**Summary Word Count:** {summary_word_count}")
-        st.write(f"**Time Taken:** {end_time - start_time:.2f} seconds")
+        st.subheader("Summaries")
+        for model_name, summary in summaries.items():
+            summary_word_count = len(summary.split())
+            st.write(f"**{model_name}**")
+            st.write(summary.replace('<n>', ''))
+            st.write(f"**Summary Word Count:** {summary_word_count}")
+            st.write("---")
+        st.write(f"**Total Time Taken:** {end_time - start_time:.2f} seconds")
 else:
     st.error("Please enter text to summarize.")