Update app.py

app.py

@@ -0,0 +1,88 @@
+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")
+
+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)