Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Load the tokenizer and model
+# Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
 model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
 
@@ -9,48 +9,24 @@ def generate_summary(text):
     inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True)
     summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False)
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    
-    # Post-process the summary to include only specific points
-    important_points = extract_important_points(summary)
-    
-    return important_points
+    return summary
 
-def extract_important_points(summary):
-    # Filter the summary for sentences containing keywords related to change requests or important points
-    keywords = ["change", "request", "important", "needs", "must", "critical", "required", "suggested"]
-    filtered_lines = [line for line in summary.split('. ') if any(keyword in line.lower() for keyword in keywords)]
-    return '. '.join(filtered_lines)
+def save_to_history(input_text, summary_text):
+    with open("history.txt", "a") as file:
+        file.write(f"Input: {input_text}\nSummary: {summary_text}\n\n")
 
-# Initialize session state for input history if it doesn't exist
-if 'input_history' not in st.session_state:
-    st.session_state['input_history'] = []
+# Streamlit app
+st.set_page_config(page_title='Text Summarization App')
+st.title('Text Summarization App')
 
-# Streamlit interface
-st.title("Text Summarization App")
+txt_input = st.text_area('Enter your text', '', height=200)
 
-# User text input
-user_input = st.text_area("Enter the text you want to summarize", height=200)
-
-if st.button("Generate Summary"):
-    if user_input:
-        with st.spinner("Generating summary..."):
-            summary = generate_summary(user_input)
-            
-        # Save the input and summary to the session state history
-        st.session_state['input_history'].append({"input": user_input, "summary": summary})
-        
-        st.subheader("Filtered Summary:")
-        st.write(summary)
+if st.button('Generate Summary'):
+    if txt_input:
+        with st.spinner('Generating summary...'):
+            summary = generate_summary(txt_input)
+            save_to_history(txt_input, summary)
+            st.subheader('Generated Summary:')
+            st.write(summary)
     else:
-        st.warning("Please enter text to summarize.")
-
-# Display the history of inputs and summaries
-if st.session_state['input_history']:
-    st.subheader("History")
-    for i, entry in enumerate(st.session_state['input_history']):
-        st.write(f"**Input {i+1}:** {entry['input']}")
-        st.write(f"**Summary {i+1}:** {entry['summary']}")
-        st.write("---")
-
-# Instructions for using the app
-st.write("Enter your text in the box above and click 'Generate Summary' to get a summarized version of your text.")
+        st.warning('Please enter some text to summarize.')