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| from __future__ import annotations | |
| import gradio as gr | |
| import pandas as pd | |
| from llm_provider import CHAT_LLM, SUMMARY_NOTICE | |
| from mcp import mcp_fetch_sanctions, mcp_fetch_high_risk_mcc | |
| from threat_intel import ThreatIntel | |
| from ttp_guard import TTPGuard, GuardDecision, default_guard | |
| from modules.transactions import prepare_transactions, detect_transactions | |
| from modules.kyc import prepare_kyc, detect_kyc | |
| from modules.sanctions import prepare_sanctions, detect_sanctions, DEMO_SANCTIONS | |
| from modules.credit import prepare_credit, detect_credit | |
| from agent import build_agent | |
| from tools import build_tools | |
| from langchain.schema import SystemMessage, HumanMessage | |
| # ---------- Summarizer ---------- | |
| SUMMARY_SYS = "You are a helpful Fraud/Risk analyst. Be concise (<120 words), list key counts, drivers, and data quality caveats." | |
| def summarize_ai(context: str) -> str: | |
| if CHAT_LLM is None: | |
| return SUMMARY_NOTICE | |
| # Guard summaries as well (low severity just annotate) | |
| decision = default_guard.inspect_input(context) | |
| if decision.action == GuardDecision.BLOCK: | |
| return f"Blocked by TTP Guard: {decision.reason}" | |
| try: | |
| out = CHAT_LLM.invoke([SystemMessage(content=SUMMARY_SYS), HumanMessage(content=context[:4000])]) | |
| return getattr(out, "content", str(out)) | |
| except Exception: | |
| return SUMMARY_NOTICE | |
| # ---------- TI + Guard singletons ---------- | |
| TI = ThreatIntel.load() # pulls MCP envs if set, else defaults | |
| GUARD = default_guard | |
| # ---------- Pipelines (tabs) ---------- | |
| def run_transactions(file): | |
| try: | |
| from validation import _read_csv_any | |
| df = _read_csv_any(file) | |
| clean, issues, quality, colmap = prepare_transactions(df) | |
| mcc_list = mcp_fetch_high_risk_mcc() or TI.high_risk_mcc | |
| flagged, stats = detect_transactions(clean, colmap, mcc_list) | |
| ctx = f"[Transactions]\n{stats}\nQuality: {quality}\nHead:\n{clean.head(5).to_csv(index=False)}\nFlagged:\n{flagged.head(5).to_csv(index=False)}" | |
| ai = summarize_ai(ctx) | |
| return ai, stats, flagged, issues | |
| except Exception as e: | |
| return f"Error: {e}", "Validation failed.", pd.DataFrame(), pd.DataFrame() | |
| def run_kyc(file): | |
| try: | |
| from validation import _read_csv_any | |
| df = _read_csv_any(file) | |
| clean, issues, quality, colmap = prepare_kyc(df) | |
| flagged, stats = detect_kyc(clean, colmap) | |
| ctx = f"[KYC]\n{stats}\nQuality: {quality}\nHead:\n{clean.head(5).to_csv(index=False)}\nFlagged:\n{flagged.head(5).to_csv(index=False)}" | |
| ai = summarize_ai(ctx) | |
| return ai, stats, flagged, issues | |
| except Exception as e: | |
| return f"Error: {e}", "Validation failed.", pd.DataFrame(), pd.DataFrame() | |
| def run_sanctions(customers_file, sanctions_file): | |
| try: | |
| from validation import _read_csv_any | |
| df = _read_csv_any(customers_file) | |
| clean, issues, quality, colmap = prepare_sanctions(df) | |
| sanc_df = mcp_fetch_sanctions() or ( _read_csv_any(sanctions_file) if sanctions_file else None ) or TI.sanctions_df or DEMO_SANCTIONS | |
| flagged, stats = detect_sanctions(clean, colmap, sanc_df) | |
| ctx = f"[Sanctions]\n{stats}\nQuality: {quality}\nHead:\n{clean.head(5).to_csv(index=False)}\nMatches:\n{flagged.head(5).to_csv(index=False)}" | |
| ai = summarize_ai(ctx) | |
| return ai, stats, flagged, issues | |
| except Exception as e: | |
| return f"Error: {e}", "Validation failed.", pd.DataFrame(), pd.DataFrame() | |
| def run_credit(file): | |
| try: | |
| from validation import _read_csv_any | |
| df = _read_csv_any(file) | |
| clean, issues, quality, colmap = prepare_credit(df) | |
| flagged, stats = detect_credit(clean, colmap) | |
| ctx = f"[Credit]\n{stats}\nQuality: {quality}\nHead:\n{clean.head(5).to_csv(index=False)}\nFlagged:\n{flagged.head(5).to_csv(index=False)}" | |
| ai = summarize_ai(ctx) | |
| return ai, stats, flagged, issues | |
| except Exception as e: | |
| return f"Error: {e}", "Validation failed.", pd.DataFrame(), pd.DataFrame() | |
| # ---------- Agent & tools ---------- | |
| TOOLS = build_tools() | |
| AGENT = build_agent(TOOLS, GUARD) | |
| def agent_reply(history, user_msg: str): | |
| # Guard the incoming user message before tool routing | |
| decision = GUARD.inspect_input(user_msg) | |
| if decision.action == GuardDecision.BLOCK: | |
| return f"❌ Blocked by TTP Guard: {decision.reason}" | |
| try: | |
| looks_like_csv = ("," in user_msg) and ("\n" in user_msg) and (user_msg.count(",") >= 2) | |
| prompt = f"CSV snippet detected. Decide tool and analyze:\n\n{user_msg}" if looks_like_csv else user_msg | |
| res = AGENT.invoke(prompt) | |
| if isinstance(res, dict) and "output" in res: | |
| return res["output"] | |
| return str(res) | |
| except Exception as e: | |
| return f"Agent error: {e}" | |
| # ---------- UI ---------- | |
| with gr.Blocks(title="Fraud Detector Analyst — LangChain + Fireworks + MCP") as demo: | |
| gr.Markdown("# 🛡️ Fraud Detector Analyst — LangChain + Fireworks + MCP") | |
| with gr.Tabs(): | |
| with gr.Tab("Transactions"): | |
| f = gr.File(file_types=[".csv"], label="Transactions CSV", type="binary") | |
| ai = gr.Textbox(label="AI Summary (requires inference)", value=SUMMARY_NOTICE, lines=6) | |
| st = gr.Textbox(label="Stats", lines=3) | |
| flagged = gr.Dataframe(label="Flagged Transactions") | |
| issues = gr.Dataframe(label="Data Quality Issues (row, field, issue, value)") | |
| f.upload(run_transactions, inputs=[f], outputs=[ai, st, flagged, issues]) | |
| with gr.Tab("KYC"): | |
| f = gr.File(file_types=[".csv"], label="KYC CSV", type="binary") | |
| ai = gr.Textbox(label="AI Summary (requires inference)", value=SUMMARY_NOTICE, lines=6) | |
| st = gr.Textbox(label="Stats", lines=3) | |
| flagged = gr.Dataframe(label="Flagged KYC Rows") | |
| issues = gr.Dataframe(label="Data Quality Issues") | |
| f.upload(run_kyc, inputs=[f], outputs=[ai, st, flagged, issues]) | |
| with gr.Tab("Sanctions/PEP"): | |
| cust = gr.File(file_types=[".csv"], label="Customers CSV", type="binary") | |
| sanc = gr.File(file_types=[".csv"], label="Sanctions/PEP CSV (optional)", type="binary") | |
| ai = gr.Textbox(label="AI Summary (requires inference)", value=SUMMARY_NOTICE, lines=6) | |
| st = gr.Textbox(label="Stats", lines=3) | |
| flagged = gr.Dataframe(label="Matches") | |
| issues = gr.Dataframe(label="Data Quality Issues") | |
| cust.upload(run_sanctions, inputs=[cust, sanc], outputs=[ai, st, flagged, issues]) | |
| sanc.upload(run_sanctions, inputs=[cust, sanc], outputs=[ai, st, flagged, issues]) | |
| with gr.Tab("Credit Risk"): | |
| f = gr.File(file_types=[".csv"], label="Credit CSV", type="binary") | |
| ai = gr.Textbox(label="AI Summary (requires inference)", value=SUMMARY_NOTICE, lines=6) | |
| st = gr.Textbox(label="Stats", lines=3) | |
| flagged = gr.Dataframe(label="Flagged Applicants") | |
| issues = gr.Dataframe(label="Data Quality Issues") | |
| f.upload(run_credit, inputs=[f], outputs=[ai, st, flagged, issues]) | |
| with gr.Tab("AI Consultant (Agent)"): | |
| chatbot = gr.Chatbot(type="messages", label="Fraud AI Consultant") | |
| user_in = gr.Textbox(label="Message or CSV snippet") | |
| send_btn = gr.Button("Send") | |
| def _chat_fn(history, msg): | |
| reply = agent_reply(history, msg) | |
| history = (history or []) + [{"role":"user","content":msg}, {"role":"assistant","content":reply}] | |
| return history, "" | |
| send_btn.click(_chat_fn, inputs=[chatbot, user_in], outputs=[chatbot, user_in]) | |
| with gr.Tab("Security & TI"): | |
| gr.Markdown("**TTP Guard policy & latest indicators**") | |
| gr.JSON(value=GUARD.describe_policy()) | |
| gr.Dataframe(value=TI.sanctions_df.head(10) if TI.sanctions_df is not None else pd.DataFrame({"note":["demo sanctions used"]}), | |
| label="Sanctions (sample)") | |
| gr.Dataframe(value=pd.DataFrame({"high_risk_mcc": TI.high_risk_mcc}), | |
| label="High-risk MCC (current)") | |
| gr.Markdown( | |
| "### ⚙️ Configure\n" | |
| "- `FIREWORKS_API_KEY` **or** `HF_TOKEN` (provider routing to Fireworks)\n" | |
| "- `FW_PRIMARY_MODEL` (default openai/gpt-oss-20b), `FW_SECONDARY_MODEL` (default Qwen/Qwen3-Coder-30B-A3B-Instruct)\n" | |
| "- MCP (optional): `ENABLE_MCP=1`, `MCP_SANCTIONS_URL`, `MCP_HIGH_RISK_MCC_URL`, `MCP_AUTH_HEADER`\n" | |
| "- TTP guard thresholds: `TTP_BLOCK_LEVEL` (default 3)\n" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860) |