Update app.py

app.py

@@ -11,7 +11,14 @@ import os
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
 
-bart_billsum = keras_nlp.models.BartSeq2SeqLM.from_preset("hf://Grey01/bart_billsum")
+preprocessor = keras_nlp.models.BartSeq2SeqLMPreprocessor.from_preset(
+    "hf://Grey01/bart_billsum",
+    encoder_sequence_length=512,
+    decoder_sequence_length=128,
+)
+
+bart_billsum = keras_nlp.models.BartSeq2SeqLM.from_preset("hf://Grey01/bart_billsum", preprocessor=preprocessor)
+
 
 st.title("SummarizeIt")
 
@@ -19,7 +26,7 @@ st.title("SummarizeIt")
 uploaded_file = st.file_uploader("Choose a file", type=["pdf", "txt", "docx"])
 
 # Text extraction
-text = []
+text = ''
 if uploaded_file is not None:
     if uploaded_file.type == "application/pdf":
         pdf_reader = PyPDF2.PdfReader(uploaded_file)
@@ -32,20 +39,13 @@ if uploaded_file is not None:
 # Text input for direct text entry
 user_input = st.text_area("Or paste your text here:")
 if user_input:
-    text.append(user_input)
+    text = user_input
 else:
-    text.append(text)  # Prioritize user input over file
+    text = text
 
 def generate_text(model, input_texts, max_length=500, print_time_taken=False):
-    # Convert input_texts to a list if it's a Dataset
-    chunks = [input_texts[i:i+512] for i in range(0, len(input_texts), 512)]
-    #initialize an empty list to store summaries
-    summaries = []
-    # generate summaries for each chunk
-    for chunk in chunks:
-        # Assuming your model's generate method can handle a batch of inputs
-        summary = model.generate(input_texts, max_length=max_length)
-        summaries.append(summary)
+    summary = model.generate(input_texts, max_length=max_length)
+    summaries.append(summary)
     return summary
 
 generated_summaries = generate_text(