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import streamlit as st
import pymupdf
import re
import traceback
import faiss
import numpy as np
import requests
from rank_bm25 import BM25Okapi
from sentence_transformers import SentenceTransformer
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_groq import ChatGroq
import torch
import os
from langgraph.graph import StateGraph
from typing import TypedDict, List
# Set up Streamlit page
st.set_page_config(page_title="Financial Insights Chatbot", page_icon="📊", layout="wide")
GROQ_API_KEY = "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"
ALPHA_VANTAGE_API_KEY = "BUJB69F41Y4ESEGX"
device = "cuda" if torch.cuda.is_available() else "cpu"
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
ALPHA_VANTAGE_API_KEY = os.getenv("ALPHA_VANTAGE_API_KEY")
temp_llm = ChatGroq(temperature=0, model="llama3-70b-8192", api_key=GROQ_API_KEY)
st.success("✅ LLM initialized successfully. Using llama3-70b-8192")
embedding_model = SentenceTransformer("baconnier/Finance2_embedding_small_en-V1.5", device=device)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
class AgentState(TypedDict):
extracted_data: str
compliance_issues: str
risk_analysis: str
report: str
audit_log: List[str]
approval_status: str
def extract_data(state: AgentState) -> AgentState:
"""Extracts financial data from PDFs or APIs."""
if state.get("extracted_data"):
return state
return {**state, "extracted_data": "Extracted financial data here."}
def validate_compliance(state: AgentState) -> AgentState:
"""Validates financial data against compliance rules."""
return {**state, "compliance_issues": "No issues found."}
def assess_risk(state: AgentState) -> AgentState:
"""Analyzes potential financial risks."""
return {**state, "risk_analysis": "Low risk identified."}
def generate_report(state: AgentState) -> AgentState:
"""Generates a financial compliance report."""
report = f"Report:\nData: {state['extracted_data']}\nCompliance: {state['compliance_issues']}\nRisk: {state['risk_analysis']}"
return {**state, "report": report}
def log_audit(state: AgentState) -> AgentState:
"""Maintains an audit trail of changes."""
return {**state, "audit_log": state.get("audit_log", []) + ["Report generated"]}
def approve_report(state: AgentState) -> AgentState:
"""Sends the report for approval."""
return {**state, "approval_status": "Approved"}
def display_dashboard(state: AgentState):
"""Shows the final insights on Streamlit UI."""
st.markdown("### 📊 Final Dashboard")
st.write(state)
workflow = StateGraph(AgentState)
workflow.add_node("extract_data", extract_data)
workflow.add_node("validate_compliance", validate_compliance)
workflow.add_node("assess_risk", assess_risk)
workflow.add_node("generate_report", generate_report)
workflow.add_node("log_audit", log_audit)
workflow.add_node("approve_report", approve_report)
workflow.add_edge("extract_data", "validate_compliance")
workflow.add_edge("validate_compliance", "assess_risk")
workflow.add_edge("assess_risk", "generate_report")
workflow.add_edge("generate_report", "log_audit")
workflow.add_edge("log_audit", "approve_report")
workflow.set_entry_point("extract_data")
workflow.set_termination_nodes(["approve_report"])
if st.button("Run Multi-Agent Workflow"):
output = workflow.run({})
display_dashboard(output) |