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c6d67ac | 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 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | import streamlit as st
import requests
import json
import os
# Configure the API URL. In local dev it is 127.0.0.1.
# Via docker-compose, this is overridden to http://api:8000/chat/stream via ENV.
API_URL = os.getenv("API_URL", "http://127.0.0.1:8000/chat/stream")
st.set_page_config(page_title="FinAgent Portfolio", page_icon="📈", layout="wide")
st.title("📈 🤖 FinAgent: Autonomous Financial AI")
with st.sidebar:
st.markdown("### 👨💻 About this Agent")
st.markdown(
"This application uses **LangGraph** to construct a deterministic multi-agent state machine. "
"The **Planner Agent** parses the query, while the **Supervisor** appropriately routes tasks "
"to specialized **Quant, Fundamental, Sentiment, and Earnings Agents**.\n\n"
"Finally, the **Summarizer** compiles a comprehensive Investment Memo."
)
st.divider()
# Automatically survey the indexed ChromeDB vector stores
available_tickers = []
earnings_tickers = []
if os.path.exists("./chroma_db"):
for d in os.listdir("./chroma_db"):
if d.endswith("_10k"):
available_tickers.append(d.replace("_10k", ""))
elif d.endswith("_earnings"):
earnings_tickers.append(d.replace("_earnings", ""))
if available_tickers:
st.markdown("### 📚 Supported 10-K Data")
st.markdown("Deep RAG (Fundamental SEC filings) currently verified & compiled for:")
cols = st.columns(4)
for i, t in enumerate(sorted(available_tickers)):
cols[i % 4].code(t)
if earnings_tickers:
st.markdown("### 🎙️ Earnings Call Data")
st.markdown("Ingested earnings-call transcripts available for:")
cols = st.columns(4)
for i, t in enumerate(sorted(earnings_tickers)):
cols[i % 4].code(t)
st.divider()
st.markdown("### ⚡ Recruiter Quick-Test")
st.markdown("Try one of these example queries to see the multi-agent graph in action:")
if st.button("🍎 Apple Financial Overview"):
st.session_state.example_query = "What is the price, sentiment, and recent 10-K risks for Apple (AAPL)?"
if st.button("🏎️ Tesla Breaking Sentiment"):
st.session_state.example_query = "What is the latest news sentiment for TSLA?"
if st.button("💻 MSFT vs GOOGL"):
st.session_state.example_query = "Compare the current stock performance of Microsoft and Google."
if st.button("🎙️ Earnings Call Analysis"):
st.session_state.example_query = "Analyze the latest earnings call for Apple (AAPL) — compare management tone in prepared remarks vs Q&A and show keyword trends."
st.divider()
st.caption("Powered by Llama-3.1-8B via Groq")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat history on screen
for message in st.session_state.messages:
with st.chat_message(message["role"]):
if message["role"] == "assistant" and "steps" in message and message["steps"]:
# Render past steps in a collapsed status box
total_time = message.get("total_latency", 0)
title = f"✅ Investment Memo Generated! (Total Latency: {total_time}s)" if total_time else "✅ Investment Memo Generated!"
with st.status(title, expanded=False):
for step in message["steps"]:
lat = step.get('step_latency', 0)
lat_str = f"({lat}s) " if lat else ""
st.write(f"**[{step['node']}]** {lat_str}{step['content']}")
st.markdown(message["content"])
# Unconditionally render the chat_input so it NEVER disappears from the UI
chat_val = st.chat_input("Ask about any stock ticker (e.g. AAPL, TSLA, NVDA)...")
if "example_query" in st.session_state and st.session_state.example_query:
prompt = st.session_state.example_query
st.session_state.example_query = "" # Reset
else:
prompt = chat_val
if prompt:
# Render user message
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
# We will stream the intermediate node outputs into a dynamic expanding status box
status_box = st.status("🧠 Consulting Specialized AI Agents...", expanded=True)
final_memo_placeholder = st.empty()
try:
# Stream the response via POST request
with requests.post(API_URL, json={"query": prompt}, stream=True) as response:
response.raise_for_status()
final_memo = ""
session_steps = []
# Consume Server-Sent Events (SSE)
for line in response.iter_lines():
if line:
decoded_line = line.decode('utf-8')
if decoded_line.startswith("data: "):
data_str = decoded_line[len("data: "):]
try:
data = json.loads(data_str)
node = data.get("node")
content = data.get("content")
step_latency = data.get("step_latency", 0)
total_latency = data.get("total_latency", 0)
if node == "Summarizer":
# The final node returns the full markdown report
final_memo = content
final_memo_placeholder.markdown(final_memo)
status_box.update(label=f"✅ Investment Memo Generated! (Total Latency: {total_latency}s)", state="complete", expanded=False)
else:
# Show what the different agents (Quant, Sentiment, etc.) are calculating
lat_str = f"({step_latency}s) " if step_latency else ""
status_box.write(f"**[{node}]** {lat_str}{content}")
session_steps.append({
"node": node,
"content": content,
"step_latency": step_latency,
"total_latency": total_latency
})
except json.JSONDecodeError:
pass
# Save the final memo and intermediate steps to history
if final_memo:
final_total_latency = session_steps[-1].get("total_latency", 0) if session_steps else 0
st.session_state.messages.append({
"role": "assistant",
"content": final_memo,
"steps": session_steps,
"total_latency": final_total_latency
})
except requests.exceptions.RequestException as e:
status_box.update(label="❌ Connection Error", state="error", expanded=False)
st.error(f"Failed to connect to the backend FastAPI server: {e}")
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