Updated

app.py

@@ -1,19 +1,22 @@
 import streamlit as st
 from transformers import pipeline
 
-# Assume you have fine-tuned models and their names are listed here
 available_models = [
     "facebook/t5-small",
+    "facebook/t5-base",
+    "facebook/t5-large",
     "google/pegasus-xsum",
+    "google/pegasus-cnn_dailymail",
     "sshleifer/distilbart-cnn-12-6",
-    "your_fine_tuned_news_model",  # Replace with your fine-tuned model name
-    "your_fine_tuned_long_doc_model", # Replace with another fine-tuned model name
-    # Add more of your fine-tuned models here
+    "allenai/led-base-16384",
+    "google/mt5-small",
+    "google/mt5-base",
+    # Add more models as needed
 ]
 
 @st.cache_resource
 def load_summarizer(model_name):
-    """Loads the summarization pipeline for a given model."""
+    """Loads the summarization pipeline for a given model from Hugging Face."""
     try:
         summarizer = pipeline("summarization", model=model_name)
         return summarizer
@@ -21,13 +24,13 @@ def load_summarizer(model_name):
         st.error(f"Error loading model {model_name}: {e}")
         return None
 
-st.title("Advanced Text Summarization App")
+st.title("Hugging Face Text Summarization App")
 
 text_to_summarize = st.text_area("Enter text to summarize:", height=300)
 
-selected_model = st.selectbox("Choose a summarization model:", available_models)
+selected_model = st.selectbox("Choose a summarization model from Hugging Face:", available_models)
 
-# Parameters for controlling summarization
+st.sidebar.header("Summarization Parameters")
 max_length = st.sidebar.slider("Max Summary Length:", min_value=50, max_value=500, value=150)
 min_length = st.sidebar.slider("Min Summary Length:", min_value=10, max_value=250, value=30)
 temperature = st.sidebar.slider("Temperature (for sampling):", min_value=0.0, max_value=1.0, value=0.0, step=0.01, help="Higher values make the output more random.")
@@ -63,7 +66,8 @@ if st.button("Summarize"):
 st.sidebar.header("About")
 st.sidebar.info(
     "This app uses the `transformers` library from Hugging Face "
-    "to perform text summarization. You can select from various "
-    "pre-trained and potentially fine-tuned models. Experiment with "
-    "the parameters in the sidebar to control the summarization process."
+    "to perform text summarization. You can select from a variety of "
+    "pre-trained models available on the Hugging Face Model Hub. "
+    "Experiment with the parameters in the sidebar to control the "
+    "summarization process."
 )