Upload app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+
+from huggingface_hub import login
+from dotenv import load_dotenv
+import os
+
+# Load the environment variables from the .env file
+load_dotenv()
+
+# Retrieve the token from the .env file
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+
+# Log in using the retrieved token
+login(token=huggingface_token)
+
+# Available models for summarization
+models = {
+    "T5": "Sandaruth/T5_Full_Fine_Tuned_FINDSUM",
+    "BERT": "bert-base-uncased",  # Note: BERT isn't designed for summarization; you can change this
+    "LongT5": "google/long-t5-local-base",
+    "Pegasus": "google/pegasus-xsum"
+}
+
+# Streamlit app layout
+st.title("Summarization with Multiple Models")
+
+# Dropdown to select the model
+model_choice = st.selectbox("Select a model for summarization", models.keys())
+
+# Text area for input
+input_text = st.text_area("Enter the long text you want to summarize", height=300)
+
+# Button to generate the summary
+if st.button("Generate Summary"):
+    # Load the selected model and summarizer pipeline
+    summarizer = pipeline("summarization", model=models[model_choice])
+    
+    if input_text:
+        # Generate the summary
+        summary = summarizer(input_text, max_length=150, min_length=30, do_sample=False)
+        
+        # Display the summary
+        st.subheader("Generated Summary")
+        st.write(summary[0]['summary_text'])
+    else:
+        st.write("Please enter text to summarize!")