Provided More Model For Selection

app.py

@@ -1,32 +1,62 @@
 import streamlit as st
 from transformers import pipeline
 
-# Available summarization models (you can expand this list)
+# Assume you have fine-tuned models and their names are listed here
 available_models = [
     "facebook/t5-small",
     "google/pegasus-xsum",
     "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
 ]
 
 @st.cache_resource
 def load_summarizer(model_name):
     """Loads the summarization pipeline for a given model."""
-    summarizer = pipeline("summarization", model=model_name)
-    return summarizer
+    try:
+        summarizer = pipeline("summarization", model=model_name)
+        return summarizer
+    except Exception as e:
+        st.error(f"Error loading model {model_name}: {e}")
+        return None
 
-st.title("Text Summarization App")
+st.title("Advanced 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)
 
+# Parameters for controlling summarization
+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.")
+repetition_penalty = st.sidebar.slider("Repetition Penalty:", min_value=1.0, max_value=2.5, value=1.0, step=0.01, help="Penalizes repeated tokens to improve coherence.")
+num_beams = st.sidebar.slider("Number of Beams (for beam search):", min_value=1, max_value=10, value=1, help="More beams improve quality but increase computation.")
+do_sample = st.sidebar.checkbox("Enable Sampling?", value=False, help="Whether to use sampling; set to False for deterministic output.")
+
 if st.button("Summarize"):
     if text_to_summarize:
-        with st.spinner(f"Summarizing using {selected_model}..."):
-            summarizer = load_summarizer(selected_model)
-            summary = summarizer(text_to_summarize, max_length=150, min_length=30, do_sample=False)[0]['summary_text']
-        st.subheader("Summary:")
-        st.write(summary)
+        summarizer = load_summarizer(selected_model)
+        if summarizer:
+            with st.spinner(f"Summarizing using {selected_model}..."):
+                try:
+                    summary = summarizer(
+                        text_to_summarize,
+                        max_length=max_length,
+                        min_length=min_length,
+                        do_sample=do_sample,
+                        temperature=temperature if do_sample else None,
+                        repetition_penalty=repetition_penalty,
+                        num_beams=num_beams if not do_sample else 1, # Beam search is usually not used with sampling
+                        early_stopping=True,
+                    )[0]['summary_text']
+                    st.subheader("Summary:")
+                    st.write(summary)
+                except Exception as e:
+                    st.error(f"Error during summarization: {e}")
+        else:
+            st.warning("Failed to load the selected model.")
     else:
         st.warning("Please enter some text to summarize.")
 
@@ -34,5 +64,6 @@ 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 models."
+    "pre-trained and potentially fine-tuned models. Experiment with "
+    "the parameters in the sidebar to control the summarization process."
 )