Update app.py

app.py

@@ -1,34 +1,35 @@
 import streamlit as st
 from transformers import pipeline
 
-# Set the title of the app
-st.title("Legal Document Analysis with Hugging Face and Streamlit")
+st.title("Legal Document Analysis")
 
-# Sidebar for user inputs
-st.sidebar.header("Upload your document")
-uploaded_file = st.sidebar.file_uploader("Choose a text file", type=["txt"])
+# Sidebar for uploading the document
+st.sidebar.header("Upload Document")
+uploaded_file = st.sidebar.file_uploader("Choose a document", type=["txt", "pdf", "docx"])
 
+# Sidebar for setting summarization parameters
+st.sidebar.header("Summarization Parameters")
+max_length = st.sidebar.slider("Max Length", min_value=50, max_value=500, value=150)
+min_length = st.sidebar.slider("Min Length", min_value=10, max_value=100, value=40)
+do_sample = st.sidebar.checkbox("Use Sampling", value=False)
+
+# Main area - Display content and summary
 if uploaded_file is not None:
-    # Read the file
     text = uploaded_file.read().decode("utf-8")
-    st.subheader("Uploaded Document")
-    st.write(text)
-
-    # Initialize Hugging Face pipelines
-    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-    ner = pipeline("ner", grouped_entities=True, model="dslim/bert-base-NER")
-
-    # Summarize the document
-    with st.spinner("Generating summary..."):
-        summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
-    st.subheader("Summary")
-    st.write(summary[0]['summary_text'])
-
-    # Named Entity Recognition
-    with st.spinner("Performing Named Entity Recognition..."):
-        entities = ner(text)
-    st.subheader("Named Entities")
-    for entity in entities:
-        st.write(f"**{entity['entity_group']}**: {entity['word']} (Confidence: {entity['score']:.2f})")
+    
+    # Ensure the text is long enough
+    if len(text.split()) > min_length:
+        try:
+            # Load the model from Hugging Face
+            summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+            
+            # Summarize the document
+            summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=do_sample)
+            st.subheader("Summary:")
+            st.write(summary[0]['summary_text'])
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+    else:
+        st.warning("Text is too short to summarize.")
 else:
-    st.info("Please upload a legal document to analyze.")
+    st.info("Please upload a document to summarize.")