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from LegalRag.rag_pipeline import RAGPipeline
# =====================================================
# PAGE CONFIG
# =====================================================
st.set_page_config(
page_title="Indian Legal Assistant",
page_icon="⚖️",
layout="wide"
)
# =====================================================
# LOAD RAG ONCE
# =====================================================
@st.cache_resource
def load_rag():
return RAGPipeline()
rag = load_rag()
# =====================================================
# SESSION STATE
# =====================================================
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
# =====================================================
# SIDEBAR
# =====================================================
with st.sidebar:
st.title("⚖️ Legal RAG")
st.markdown("---")
st.success("Constitution")
st.success("BNS")
st.success("BNSS")
st.success("BSA")
st.success("Indian Kanoon")
st.markdown("---")
if st.button("🗑 Clear Chat"):
st.session_state.chat_history = []
st.rerun()
# =====================================================
# HEADER
# =====================================================
st.title(
"⚖️ Indian Legal Assistant"
)
st.caption(
"Constitution • BNS • BNSS • BSA • Indian Kanoon"
)
# =====================================================
# DISPLAY CHAT HISTORY
# =====================================================
for chat in st.session_state.chat_history:
with st.chat_message(
chat["role"]
):
st.markdown(
chat["content"]
)
# =====================================================
# USER INPUT
# =====================================================
query = st.chat_input(
"Ask a legal question..."
)
# =====================================================
# PROCESS QUERY
# =====================================================
if query:
# -----------------------------------------
# User Message
# -----------------------------------------
st.session_state.chat_history.append(
{
"role": "user",
"content": query
}
)
with st.chat_message(
"user"
):
st.markdown(
query
)
# -----------------------------------------
# Assistant Message
# -----------------------------------------
with st.chat_message(
"assistant"
):
try:
with st.spinner(
"Searching Constitution, BNS, BNSS, BSA and Case Law..."
):
result = (
rag.search(
query
)
)
answer = (
result.get(
"answer",
"No answer generated."
)
)
retrieval = (
result.get(
"retrieval",
{}
)
)
st.markdown(
answer
)
# =====================================================
# DEBUG / SOURCES
# =====================================================
with st.expander(
"📚 Retrieved Sources"
):
# -----------------------------------------
# Statutory Retrieval
# -----------------------------------------
st.subheader(
"Statutory Retrieval"
)
statutory_results = (
retrieval.get(
"statutory_results",
[]
)
)
if statutory_results:
for idx, item in enumerate(
statutory_results,
start=1
):
payload = item.payload
st.markdown(
f"""
### Result {idx}
**Document:** {payload.get('document')}
**Chunk ID:** {payload.get('chunk_id')}
"""
)
st.text(
payload.get(
"text",
""
)[:1500]
)
st.divider()
else:
st.info(
"No statutory results."
)
# -----------------------------------------
# Graph Context
# -----------------------------------------
st.subheader(
"Knowledge Graph Context"
)
graph_context = (
retrieval.get(
"graph_context",
{}
)
)
if graph_context:
for node_id, graph_data in (
graph_context.items()
):
st.markdown(
f"### {node_id}"
)
st.write(
f"Ancestors: {len(graph_data.get('ancestors', []))}"
)
st.write(
f"Children: {len(graph_data.get('children', []))}"
)
st.write(
f"References: {len(graph_data.get('references', []))}"
)
st.divider()
else:
st.info(
"No graph context."
)
# -----------------------------------------
# Judgments
# -----------------------------------------
st.subheader(
"Judgments"
)
judgments = (
retrieval.get(
"judgments",
[]
)
)
if judgments:
for idx, judgment in enumerate(
judgments,
start=1
):
st.markdown(
f"### Judgment {idx}"
)
if isinstance(
judgment,
dict
):
title = (
judgment.get(
"title"
)
or
judgment.get(
"docsource"
)
or
"Unknown Judgment"
)
st.write(
title
)
st.text(
str(
judgment
)[:3000]
)
else:
st.text(
str(
judgment
)[:3000]
)
st.divider()
else:
st.info(
"No judgments retrieved."
)
except Exception as e:
st.error(
f"Error: {str(e)}"
)
answer = (
f"System Error:\n\n{str(e)}"
)
# -----------------------------------------
# Save Assistant Response
# -----------------------------------------
st.session_state.chat_history.append(
{
"role": "assistant",
"content": answer
}
) |