Update app.py

app.py

@@ -1,36 +1,31 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import os
 
-# Set the path to your Google Drive folder
-model_path = '/content/drive/My Drive/bart_samsum'
+# Replace with your model name on Hugging Face Hub
+model_name = "your-username/your-model-name"
 
-# Check if the model path exists
-if not os.path.exists(model_path):
-    st.error(f"The path {model_path} does not exist. Please check the path.")
-else:
-    # Load the tokenizer and model from Google Drive
-    tokenizer = AutoTokenizer.from_pretrained(model_path)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+# Load the tokenizer and model from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-    # Streamlit app UI
-    st.title("BART Summarization Model")
+# Streamlit app UI
+st.title("BART Summarization Model")
 
-    input_text = st.text_area("Input Text", "Enter text here...")
+input_text = st.text_area("Input Text", "Enter text here...")
 
-    if st.button("Generate Summary"):
-        if not input_text.strip():
-            st.warning("Please enter some text to summarize.")
-        else:
-            # Tokenize and generate summary
-            inputs = tokenizer(input_text, return_tensors="pt")
-            summary_ids = model.generate(inputs["input_ids"], max_length=150, num_beams=4, early_stopping=True)
-            summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-            
-            # Display the summary
-            st.subheader("Generated Summary")
-            st.write(summary)
+if st.button("Generate Summary"):
+    if not input_text.strip():
+        st.warning("Please enter some text to summarize.")
+    else:
+        # Tokenize and generate summary
+        inputs = tokenizer(input_text, return_tensors="pt")
+        summary_ids = model.generate(inputs["input_ids"], max_length=150, num_beams=4, early_stopping=True)
+        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+        
+        # Display the summary
+        st.subheader("Generated Summary")
+        st.write(summary)
 
-    # Optionally, you can add a section to display model information or statistics
-    st.sidebar.title("Model Information")
-    st.sidebar.write("This app uses a fine-tuned BART model for summarization.")
+# Optionally, you can add a section to display model information or statistics
+st.sidebar.title("Model Information")
+st.sidebar.write("This app uses a fine-tuned BART model for summarization.")