Create app.py

app.py

@@ -0,0 +1,34 @@
+import streamlit as st
+from transformers import pipeline
+
+# Set the title of the app
+st.title("Legal Document Analysis with Hugging Face and Streamlit")
+
+# Sidebar for user inputs
+st.sidebar.header("Upload your document")
+uploaded_file = st.sidebar.file_uploader("Choose a text file", type=["txt"])
+
+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})")
+else:
+    st.info("Please upload a legal document to analyze.")