File size: 1,881 Bytes
3efe7a4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import streamlit as st
from Embeddings import GetEmbeddings
import json
# Load Agent once and cache it
@st.cache_resource
def load_agent():
agent = GetEmbeddings(config_path="config.json")
agent.run() # Build/load FAISS
agent.load_summarizer() # Load summarizer model
encoder = agent.load_encoder()
return agent, encoder
def main():
st.set_page_config(page_title="📊 Financial QA Agent", layout="wide")
st.title("📊 Financial QA Agent")
st.markdown(
"""
Ask questions about financial reports.
The system retrieves relevant sections from company reports and summarizes them into concise answers.
"""
)
# Sidebar
st.sidebar.header("⚙️ Settings")
show_debug = st.sidebar.checkbox("Show retrieved chunks", value=False)
# Load Agent
agent, encoder = load_agent()
# User Input
query = st.text_area("Enter your financial question:", height=100)
if st.button("Get Answer"):
if query.strip() == "":
st.warning("⚠️ Please enter a query.")
else:
with st.spinner("🔎 Searching and generating answer..."):
answer = agent.answer_query(query, top_k=3)
st.subheader("✅ Answer")
st.write(answer)
if show_debug:
st.subheader("📂 Retrieved Chunks (Debug)")
# Show top chunks used
q_emb = encoder.encode(query, convert_to_numpy=True).reshape(1, -1)
import faiss
faiss.normalize_L2(q_emb)
scores, idxs = agent.index.search(q_emb, k=3)
for score, idx in zip(scores[0], idxs[0]):
st.markdown(f"**Score:** {score:.4f}")
st.write(agent.metadata[idx]["text"][:500] + "...")
if __name__ == "__main__":
main()
|