analytics/deep_dive_agentic.py

# deep_dive_agentic.py

"""
Agentic analytical code generation + execution engine using Hugging Face

FLOW:
User Question
↓
LLM generates pandas code
↓
Python executes code safely
↓
LLM interprets results
↓
Return code + interpretation

Environment:
export HUGGINGFACE_API_TOKEN=...

FIXES APPLIED (v2):
- FIX 1: exec() now uses a single merged namespace dict so result variables
         are reliably written back (Python bug with separate globals/locals).
- FIX 2: Smart result detection — scans namespace for any new DataFrame/Series
         instead of relying on hardcoded variable names (result_1, final_result).
- FIX 3: _fix_pandas_compatibility() is now actually called before exec().
"""

# ---------------------------------------------------
# IMPORTS
# ---------------------------------------------------

import pandas as pd
import json
import os
import re

try:
    from huggingface_hub import InferenceClient
except ImportError as exc:
    raise ImportError(
        "huggingface_hub is required. Install with `pip install huggingface-hub`."
    ) from exc

from analytics.performance_analysis import generate_metric_view

# ---------------------------------------------------
# HF CONFIG
# ---------------------------------------------------

HF_CODE_MODEL_ID = os.environ.get("HF_MODEL_ID", "Qwen/Qwen2.5-Coder-7B-Instruct")
HF_MODEL_ID = os.environ.get("HF_MODEL_ID", "Qwen/Qwen2.5-7B-Instruct")
HF_TOKEN = os.environ.get("HUGGINGFACE_API_TOKEN")


# ---------------------------------------------------
# HELPER: GET INFERENCE CLIENT
# ---------------------------------------------------

def _get_hf_client():
    if not HF_TOKEN:
        raise RuntimeError(
            "HUGGINGFACE_API_TOKEN is required. Set it in your environment."
        )
    return InferenceClient(token=HF_TOKEN)


# ---------------------------------------------------
# HELPER: EXTRACT JSON FROM LLM RESPONSE
# ---------------------------------------------------

def _extract_json(text: str):
    match = re.search(r"\{.*\}", text, re.S)
    if not match:
        return None
    payload = match.group(0)
    try:
        return json.loads(payload)
    except json.JSONDecodeError:
        try:
            cleaned = re.sub(r"[\n\r]+", " ", payload)
            cleaned = re.sub(r"(['\"])?([a-zA-Z0-9_]+)(['\"])?\s*:\s*", r'"\2": ', cleaned)
            return json.loads(cleaned)
        except Exception:
            return None


# ---------------------------------------------------
# HELPER: FIX COMMON PANDAS COMPATIBILITY ISSUES
# ---------------------------------------------------

def _fix_pandas_compatibility(code: str) -> str:
    """
    Fix common pandas API compatibility issues in generated code.
    Handles version differences between pandas versions.
    """
    # Fix: .reset_index(name=...) -> .reset_index(names=[...])
    code = re.sub(
        r"\.reset_index\(name=(['\"])([^'\"]+)\1\)",
        r".reset_index(names=[\1\2\1])",
        code
    )

    # Fix: .reset_index(name= with variable
    code = re.sub(
        r"\.reset_index\(name=([a-zA-Z_][a-zA-Z0-9_]*)\)",
        r".reset_index(names=[\1])",
        code
    )

    # Fix: df.append() deprecated in newer pandas -> pd.concat()
    code = re.sub(
        r"(\w+)\.append\((\w+),\s*ignore_index=True\)",
        r"pd.concat([\1, \2], ignore_index=True)",
        code
    )

    return code


# ---------------------------------------------------
# STEP 1: CODE GENERATION
# ---------------------------------------------------

def generate_analysis_requirements(
    question: str,
    acq: pd.DataFrame,
    perf: pd.DataFrame,
    master_df: pd.DataFrame
):
    """
    LLM breaks down question into 1-3 structured analytics requirements.
    Each requirement includes a description and executable pandas code.
    """

    client = _get_hf_client()

    # Build detailed column descriptions
    acq_cols = {
        "account_id": "unique account identifier",
        "booking_date": "when account was originated",
        "booking_vintage": "year-month of origination (YYYY-MM)",
        "fico_band": "FICO score bracket (e.g., 700-750, 750-800)",
        "sourcing_channel": "acquisition channel (e.g., Online, Branch, Broker)",
        "city_tier": "city classification (Tier-1, Tier-2, Tier-3)",
        "occupation_type": "borrower occupation category",
        "credit_limit": "approved credit line amount"
    }

    perf_cols = {
        "account_id": "unique account identifier",
        "reporting_month": "month of performance observation (YYYY-MM)",
        "mob": "months on books (age of account in months)",
        "dpd": "days past due (0, 30, 60, 90+)",
        "balance": "current outstanding balance",
        "ncl_amount": "net charge-off amount (dollars)",
        "payment": "payment amount in period"
    }

    prompt = (
        # ================================================================
        # SECTION 1: ROLE & OBJECTIVE
        # ================================================================
        "You are a senior retail credit risk analyst with 15+ years of experience "
        "managing consumer credit portfolios. Your job is to analyze a user question, "
        "plan the right analytical approach, write executable pandas code, and return "
        "a structured JSON plan.\n\n"

        "You have access to credit portfolio data. You will generate up to 3 pandas "
        "code blocks (requirements) to answer the question. Each requirement produces "
        "one result table. A comparison of two periods can and should be done in ONE "
        "requirement by building a single table with both periods as columns plus a "
        "delta column — do not waste two requirements on what can be one clean table.\n\n"

        # ================================================================
        # SECTION 2: DATA DICTIONARY
        # ================================================================
        "================================================================\n"
        "SECTION 2: DATA DICTIONARY\n"
        "================================================================\n\n"

        "THREE dataframes are available. Use master_df for any analysis that needs "
        "both acquisition attributes and performance metrics together.\n\n"

        "acq — acquisition data (one row per account):\n"
        " - account_id       : unique account identifier (string)\n"
        " - booking_date     : origination date (datetime)\n"
        " - booking_vintage  : origination month as STRING in format 'YYYY-MM' e.g. '2024-07'\n"
        " - fico_band        : FICO score bracket (string) e.g. '700-750', '750-800'\n"
        " - sourcing_channel : acquisition channel (string) e.g. 'Online', 'Branch', 'Broker'\n"
        " - city_tier        : city classification (string) e.g. 'Tier-1', 'Tier-2', 'Tier-3'\n"
        " - occupation_type  : borrower occupation (string) e.g. 'Salaried', 'Self-Employed'\n"
        " - credit_limit     : approved credit line in currency units (float)\n\n"

        "perf — monthly performance data (one row per account per month):\n"
        " - account_id       : unique account identifier (string)\n"
        " - reporting_month  : observation month as STRING in format 'YYYY-MM' e.g. '2024-10'\n"
        " - mob              : months on books — integer age of account (1, 2, 3 ... 12+)\n"
        " - dpd              : days past due at that mob (integer: 0, 30, 60, 90)\n"
        " - balance          : outstanding balance at that mob (float)\n"
        " - ncl_amount       : net charge-off amount at that mob (float, 0 if not charged off)\n"
        " - payment          : payment made in that month (float)\n\n"

        "master_df — perf LEFT JOINED with acq on account_id. Contains ALL columns above.\n"
        "IMPORTANT: Always use master_df when you need both segment columns AND performance columns.\n\n"

        "SEGMENT COLUMNS available for groupby in master_df:\n"
        " - booking_vintage, fico_band, sourcing_channel, city_tier, occupation_type\n\n"

        # ================================================================
        # SECTION 3: METRIC DEFINITIONS & EXACT FORMULAS
        # ================================================================
        "================================================================\n"
        "SECTION 3: METRIC DEFINITIONS & EXACT FORMULAS\n"
        "================================================================\n\n"

        "--- METRIC 1: Yr1 NCL Rate (Year 1 Net Charge-Off Rate) ---\n"
        "Definition: The annualised net charge-off rate for a vintage cohort over its first 12 months.\n"
        "What it measures: Credit loss intensity. Higher is worse.\n"
        "Formula:\n"
        "  Yr1 NCL Rate = sum(ncl_amount where mob IN 1..12) / (sum(balance where mob IN 1..12) / 12) x 100\n"
        "Steps in pandas:\n"
        "  Step 1 — Filter master_df for selected vintages AND mob between 1 and 12\n"
        "  Step 2 — sum ncl_amount across all rows in that filter\n"
        "  Step 3 — sum balance across all rows in that filter, then divide by 12\n"
        "  Step 4 — divide Step 2 by Step 3, multiply by 100\n"
        "Pandas recipe (single vintage group):\n"
        "  df_v = master_df[master_df['booking_vintage'].isin(vintages) & master_df['mob'].between(1,12)]\n"
        "  ncl_rate = (df_v['ncl_amount'].sum() / (df_v['balance'].sum() / 12)) * 100\n\n"

        "--- METRIC 2: 30+@3 (Early Delinquency Rate at MOB 3) ---\n"
        "Definition: Share of accounts that are 30+ days past due at exactly month 3.\n"
        "What it measures: Early stress signal. Higher is worse.\n"
        "Formula:\n"
        "  30+@3 = count(accounts where mob==3 AND dpd>=30) / count(accounts where mob==3) x 100\n"
        "Pandas recipe:\n"
        "  df_m3 = master_df[master_df['booking_vintage'].isin(vintages) & (master_df['mob']==3)]\n"
        "  rate_30_3 = (df_m3['dpd'] >= 30).sum() / len(df_m3) * 100\n\n"

        "--- METRIC 3: 30+@6 (Delinquency Rate at MOB 6) ---\n"
        "Definition: Share of accounts that are 30+ days past due at exactly month 6.\n"
        "Formula:\n"
        "  30+@6 = count(accounts where mob==6 AND dpd>=30) / count(accounts where mob==6) x 100\n"
        "Pandas recipe:\n"
        "  df_m6 = master_df[master_df['booking_vintage'].isin(vintages) & (master_df['mob']==6)]\n"
        "  rate_30_6 = (df_m6['dpd'] >= 30).sum() / len(df_m6) * 100\n\n"

        "--- METRIC 4: 60+@6 (Severe Delinquency Rate at MOB 6) ---\n"
        "Definition: Share of accounts that are 60+ days past due at exactly month 6.\n"
        "Formula:\n"
        "  60+@6 = count(accounts where mob==6 AND dpd>=60) / count(accounts where mob==6) x 100\n"
        "Pandas recipe:\n"
        "  rate_60_6 = (df_m6['dpd'] >= 60).sum() / len(df_m6) * 100\n\n"

        "--- RISK BENCHMARKS (flag in output) ---\n"
        "Metric    | Green (Good) | Amber (Watch) | Red (Bad)\n"
        "30+@3     | < 3%         | 3% - 6%       | > 6%\n"
        "30+@6     | < 5%         | 5% - 9%       | > 9%\n"
        "60+@6     | < 2%         | 2% - 4%       | > 4%\n"
        "Yr1 NCL   | < 3%         | 3% - 6%       | > 6%\n\n"

        # ================================================================
        # SECTION 4: DATE & VINTAGE FILTERING RECIPES
        # ================================================================
        "================================================================\n"
        "SECTION 4: DATE & VINTAGE FILTERING RECIPES\n"
        "================================================================\n\n"

        "CRITICAL: booking_vintage and reporting_month are STRINGS in 'YYYY-MM' format.\n"
        "Never use .dt accessor or pd.Grouper on these columns — they are not datetime.\n"
        "Always filter using string operations or .isin() as shown below.\n\n"

        "Single vintage:\n"
        "  master_df[master_df['booking_vintage'] == '2024-10']\n\n"

        "Full year:\n"
        "  master_df[master_df['booking_vintage'].str.startswith('2024')]\n\n"

        "Quarter (Q1=Jan-Mar, Q2=Apr-Jun, Q3=Jul-Sep, Q4=Oct-Dec):\n"
        "  q3_2024 = ['2024-07','2024-08','2024-09']\n"
        "  master_df[master_df['booking_vintage'].isin(q3_2024)]\n\n"

        "Half year (H1=Jan-Jun, H2=Jul-Dec):\n"
        "  h1_2024 = ['2024-01','2024-02','2024-03','2024-04','2024-05','2024-06']\n"
        "  h2_2024 = ['2024-07','2024-08','2024-09','2024-10','2024-11','2024-12']\n\n"

        "Comparison in ONE table (e.g. 2024 vs 2025 full year):\n"
        "  v2024 = master_df[master_df['booking_vintage'].str.startswith('2024') & master_df['mob'].between(1,12)]\n"
        "  v2025 = master_df[master_df['booking_vintage'].str.startswith('2025') & master_df['mob'].between(1,12)]\n"
        "  ncl_2024 = (v2024['ncl_amount'].sum() / (v2024['balance'].sum() / 12)) * 100\n"
        "  ncl_2025 = (v2025['ncl_amount'].sum() / (v2025['balance'].sum() / 12)) * 100\n"
        "  result_1 = pd.DataFrame({'Period':['2024','2025'], 'Yr1_NCL_Rate':[ncl_2024, ncl_2025]})\n"
        "  result_1['Delta_vs_2024'] = result_1['Yr1_NCL_Rate'] - result_1['Yr1_NCL_Rate'].iloc[0]\n\n"

        "Segment-level comparison in ONE table:\n"
        "  # Compute metric per segment for each period, merge into one table\n"
        "  def ncl_rate(df): return (df['ncl_amount'].sum() / (df['balance'].sum() / 12)) * 100\n"
        "  seg_2024 = v2024.groupby('fico_band').apply(ncl_rate).reset_index()\n"
        "  seg_2024.columns = ['fico_band','NCL_2024']\n"
        "  seg_2025 = v2025.groupby('fico_band').apply(ncl_rate).reset_index()\n"
        "  seg_2025.columns = ['fico_band','NCL_2025']\n"
        "  result_2 = seg_2024.merge(seg_2025, on='fico_band')\n"
        "  result_2['Delta'] = result_2['NCL_2025'] - result_2['NCL_2024']\n"
        "  result_2 = result_2.sort_values('NCL_2025', ascending=False)\n\n"

        # ================================================================
        # SECTION 5: QUESTION TYPE GUIDE
        # ================================================================
        "================================================================\n"
        "SECTION 5: QUESTION TYPE GUIDE — HOW TO PLAN YOUR REQUIREMENTS\n"
        "================================================================\n\n"

        "Read the question carefully and identify which type it is. Then plan accordingly.\n\n"

        "TYPE 1 — DIRECT COMPARISON (e.g. 'compare NCL 2024 vs 2025', 'how did Q3 perform vs Q4')\n"
        "  Planning approach:\n"
        "  - Identify the two periods being compared\n"
        "  - Identify which metrics are relevant (if not stated, use Yr1 NCL + 30+@3 as default)\n"
        "  - Req 1: Overall metric comparison — ONE table with [Period, Metric, Delta]\n"
        "  - Req 2 (optional): Same comparison broken down by most relevant segment\n"
        "  - Req 3 (optional): Second segment breakdown or second metric family\n"
        "  - DO NOT use two requirements to compute the same thing for two periods separately.\n"
        "    Merge them into ONE table.\n\n"

        "TYPE 2 — FOCUSED EXPLORATION (e.g. 'analyse FICO band performance', 'how is Tier-2 doing')\n"
        "  Planning approach:\n"
        "  - Identify the segment of interest\n"
        "  - Identify the time window (if not stated, use last 4 available quarters)\n"
        "  - Req 1: Metric summary across that segment — all values ranked worst to best\n"
        "  - Req 2: Trend over time for the highest-risk sub-segments identified in Req 1\n"
        "  - Req 3 (optional): Cross-segment comparison (e.g. FICO x channel interaction)\n\n"

        "TYPE 3 — OPEN / DIAGNOSTIC (e.g. 'what is wrong with the portfolio', 'give me a full view')\n"
        "  These questions require broad scanning across all segments and metrics simultaneously.\n"
        "  This level of analysis requires significantly more compute, parallel execution, and\n"
        "  multiple LLM reasoning loops that are beyond the current system design.\n"
        "  ACTION: Return a single requirement that computes a high-level portfolio scorecard\n"
        "  (all 4 metrics for last 2 years), and include a note in the description explaining\n"
        "  that a full diagnostic requires an advanced multi-agent setup.\n\n"

        # ================================================================
        # SECTION 6: WORKED EXAMPLES
        # ================================================================
        "================================================================\n"
        "SECTION 6: WORKED EXAMPLES\n"
        "================================================================\n\n"

        "EXAMPLE A — TYPE 1 COMPARISON:\n"
        "User question: 'Compare Yr1 NCL for 2024 and 2025 across FICO bands'\n"
        "Planning:\n"
        "  - Two periods: 2024 full year, 2025 full year\n"
        "  - Metric: Yr1 NCL\n"
        "  - Segment: fico_band\n"
        "  - Req 1: Overall NCL rate for 2024 vs 2025 in one table\n"
        "  - Req 2: NCL rate by fico_band for 2024 vs 2025 with delta, sorted worst first\n"
        "Expected output shape for Req 2:\n"
        "  fico_band | NCL_2024 | NCL_2025 | Delta\n"
        "  600-650   | 8.2      | 7.1      | -1.1   (improvement)\n"
        "  650-700   | 5.4      | 4.8      | -0.6\n"
        "  700-750   | 3.1      | 2.9      | -0.2\n"
        "  750-800   | 1.8      | 1.5      | -0.3\n\n"

        "EXAMPLE B — TYPE 2 EXPLORATION:\n"
        "User question: 'How are different sourcing channels performing on early delinquency'\n"
        "Planning:\n"
        "  - Segment: sourcing_channel\n"
        "  - Metrics: 30+@3 and 30+@6 (early delinquency family)\n"
        "  - Time window: last 4 quarters available in data\n"
        "  - Req 1: 30+@3 and 30+@6 rates per channel, sorted worst first\n"
        "  - Req 2: Trend of 30+@3 by channel across last 4 quarters (one row per quarter)\n"
        "Expected output shape for Req 1:\n"
        "  sourcing_channel | rate_30_3 | rate_30_6 | risk_flag\n"
        "  Broker           | 7.8       | 11.2      | RED\n"
        "  Online           | 4.1       | 6.8       | AMBER\n"
        "  Branch           | 2.3       | 4.1       | GREEN\n\n"

        # ================================================================
        # SECTION 7: CODE GENERATION RULES
        # ================================================================
        "================================================================\n"
        "SECTION 7: CODE GENERATION RULES\n"
        "================================================================\n\n"

        "1. Always store final result in variable named exactly result_1, result_2, or result_3\n"
        "   matching the sequence number of the requirement.\n\n"

        "2. Always use master_df when analysis needs both segment + performance columns.\n"
        "   Use acq only for acquisition-only analysis (e.g. credit limit distribution).\n"
        "   Use perf only for portfolio-wide performance with no segmentation.\n\n"

        "3. booking_vintage and reporting_month are strings. Never use .dt on them.\n"
        "   Filter with == or .isin() or .str.startswith() only.\n\n"

        "4. Column names are EXACTLY as listed in Section 2. Do not guess or invent column names.\n"
        "   If a column does not exist in the listed schema, do not use it.\n\n"

        "5. For comparisons: build ONE merged table with both periods as columns + delta.\n"
        "   Do not produce two separate DataFrames for two periods.\n\n"

        "6. Add a risk_flag column where relevant using benchmarks from Section 3:\n"
        "   df['risk_flag'] = pd.cut(df['rate'], bins=[0,3,6,100], labels=['GREEN','AMBER','RED'])\n\n"

        "7. Sort final result by the primary risk metric descending (worst first).\n\n"

        "8. In JSON, the code string must use \\n for newlines and escape all internal quotes.\n"
        "   Do not put raw newlines inside the JSON string value.\n\n"

        "9. Keep code focused. No print statements. No plots. No file I/O.\n\n"

        # ================================================================
        # SECTION 8: JSON OUTPUT FORMAT
        # ================================================================
        "================================================================\n"
        "SECTION 8: OUTPUT FORMAT — RETURN ONLY THIS JSON, NOTHING ELSE\n"
        "================================================================\n\n"

        "{\n"
        '  "requirements": [\n'
        '    {\n'
        '      "sequence": 1,\n'
        '      "title": "Short descriptive title",\n'
        '      "description": "What this analysis does, why it answers the question, what the output table shows",\n'
        '      "code": "# pandas code here\\nresult_1 = ..."\n'
        '    }\n'
        '  ]\n'
        "}\n\n"

        "User Question: " + question
    )

    messages = [
        {
            "role": "system",
            "content": (
                "You are a senior credit risk analyst who writes pandas code for portfolio analytics. "
                "You MUST return ONLY valid JSON with no text before or after it. "
                "Always name final result variables exactly result_1, result_2, or result_3. "
                "booking_vintage and reporting_month are string columns in YYYY-MM format — never use .dt on them. "
                "Always use master_df when you need both segment and performance data."
            )
        },
        {"role": "user", "content": prompt}
    ]

    response = client.chat.completions.create(
        model=HF_CODE_MODEL_ID,
        messages=messages,
        max_tokens=2048,
        temperature=0.1,
        top_p=0.95
    )

    response_text = (
        response.choices[0].message.content
        if hasattr(response, "choices")
        else str(response)
    )

    # Extract JSON
    spec = _extract_json(response_text)

    if not spec:
        return {
            "success": False,
            "requirements": [],
            "error": f"Failed to parse JSON from LLM response: {response_text[:200]}",
            "raw_response": response_text
        }

    requirements = spec.get("requirements", [])

    if not requirements:
        return {
            "success": False,
            "requirements": [],
            "error": f"LLM returned no requirements. Response keys: {list(spec.keys())}",
            "raw_response": response_text[:300]
        }

    print(f"[DEBUG] Generated {len(requirements)} requirements for question: {question[:80]}")
    for i, req in enumerate(requirements, 1):
        print(f"  Req {i}: {req.get('title')}")

    return {
        "success": True,
        "requirements": requirements,
        "error": None
    }


# ---------------------------------------------------
# STEP 2: CODE EXECUTION (LOOPED)
# ---------------------------------------------------

def execute_requirement_code(
    code: str,
    acq: pd.DataFrame,
    perf: pd.DataFrame,
    master_df: pd.DataFrame,
    requirement_num: int
):
    """
    Safely execute generated pandas code for a single requirement.

    FIXES:
    - FIX 1: Single namespace dict passed to exec() so variable assignments
             are reliably captured (Python quirk with separate globals/locals).
    - FIX 2: Smart result detection — checks named keys first, then scans
             for any new DataFrame/Series, then any non-None new variable.
    - FIX 3: _fix_pandas_compatibility() called before exec().
    """

    # FIX 3: Apply pandas compatibility patches BEFORE executing
    code = _fix_pandas_compatibility(code)

    # FIX 1: Merge everything into ONE dict so exec() writes back correctly.
    # When you pass separate globals + locals to exec(), Python's bytecode
    # compiler uses STORE_FAST which writes to an internal frame and does NOT
    # update the locals dict you passed in — so result variables always come
    # back None. Using a single namespace avoids this entirely.
    namespace = {
        "pd": pd,
        "generate_metric_view": generate_metric_view,
        "__builtins__": __builtins__,
        # Data available to generated code
        "acq": acq,
        "perf": perf,
        "master_df": master_df,
    }

    # Snapshot of keys before exec so we can detect newly created variables
    keys_before = set(namespace.keys())

    try:
        print(f"[DEBUG] Executing requirement {requirement_num}...")
        print(f"[DEBUG] Code preview: {code[:120].strip()}...")

        exec(code, namespace)  # FIX 1: single namespace

        # FIX 2: Smart result detection — three priority tiers

        # --- Tier 1: expected named result variables ---
        result = None
        expected_keys = [
            f"result_{requirement_num}",
            "final_result",
            "result",
        ]
        for key in expected_keys:
            if key in namespace and namespace[key] is not None:
                result = namespace[key]
                print(f"[DEBUG] Found result in expected variable: '{key}'")
                break

        # --- Tier 2: any NEW DataFrame or Series created during exec ---
        if result is None:
            new_keys = set(namespace.keys()) - keys_before
            for key in new_keys:
                val = namespace[key]
                if isinstance(val, (pd.DataFrame, pd.Series)) and val is not None:
                    result = val
                    print(f"[DEBUG] Found result by scanning new DataFrame/Series: '{key}'")
                    break

        # --- Tier 3: any new non-None, non-private variable ---
        if result is None:
            new_keys = set(namespace.keys()) - keys_before
            for key in sorted(new_keys):  # sorted for determinism
                if key.startswith("_"):
                    continue
                val = namespace[key]
                if val is not None:
                    result = val
                    print(f"[DEBUG] Fallback: found result in new variable: '{key}'")
                    break

        if result is None:
            result = "Code executed successfully but no result variable was found in namespace."

        print(f"[DEBUG] Req {requirement_num} success. Result type: {type(result).__name__}")
        return {
            "success": True,
            "result": result,
            "error": None
        }

    except Exception as e:
        import traceback
        tb = traceback.format_exc()
        print(f"[DEBUG] Req {requirement_num} FAILED: {str(e)}")
        print(f"[DEBUG] Traceback:\n{tb}")
        return {
            "success": False,
            "result": None,
            "error": str(e)
        }


def execute_all_requirements(
    requirements: list,
    acq: pd.DataFrame,
    perf: pd.DataFrame,
    master_df: pd.DataFrame
):
    """
    Execute all requirements sequentially, building context.
    """

    print(f"[DEBUG] Starting execution of {len(requirements)} requirements")
    all_results = []
    context_text = ""

    for i, req in enumerate(requirements, 1):
        code = req.get("code", "")
        description = req.get("description", "")
        title = req.get("title", f"Analysis {i}")

        exec_result = execute_requirement_code(code, acq, perf, master_df, i)

        all_results.append({
            "sequence": i,
            "title": title,
            "description": description,
            "code": code,
            # "success" is what app.py checks via res.get("success")
            # "execution_success" kept for backward compatibility
            "success": exec_result["success"],
            "execution_success": exec_result["success"],
            "result": exec_result["result"],
            "error": exec_result.get("error")
        })

        # Build context for interpretation
        if exec_result["success"]:
            context_text += f"\nAnalysis {i} ({title}):\n{str(exec_result['result'])}\n"
        else:
            context_text += f"\nAnalysis {i} ({title}) FAILED:\n{exec_result['error']}\n"

    return all_results, context_text


# ---------------------------------------------------
# STEP 3: RESULT INTERPRETATION
# ---------------------------------------------------

def interpret_all_results(
    question: str,
    all_results: list,
    context_text: str
):
    """
    Senior risk analyst LLM interprets all results holistically.
    """

    print(f"[DEBUG] Interpreting results for {len(all_results)} analyses")
    print(f"[DEBUG] Successful executions: {sum(1 for r in all_results if r.get('success'))}")

    client = _get_hf_client()

    # Format all analyses
    analyses_text = ""
    for res in all_results:
        analyses_text += f"\n{'=' * 60}\n"
        analyses_text += f"Analysis {res['sequence']}: {res['title']}\n"
        analyses_text += f"Description: {res['description']}\n"
        analyses_text += f"{'=' * 60}\n"
        if res["success"]:
            analyses_text += f"Result:\n{str(res['result'])}\n"
        else:
            analyses_text += f"Execution Error: {res['error']}\n"

    prompt = (
        "You are a senior retail credit risk analyst with 15+ years of portfolio management experience.\n\n"

        "You have just run a set of pandas analyses on a credit portfolio. "
        "Your task is to interpret the results and deliver a structured, specific, "
        "actionable risk assessment. Do not repeat raw numbers from the tables — "
        "interpret what they mean for the business.\n\n"

        "RISK BENCHMARKS FOR REFERENCE:\n"
        "  30+@3  : Green < 3%  | Amber 3-6%  | Red > 6%\n"
        "  30+@6  : Green < 5%  | Amber 5-9%  | Red > 9%\n"
        "  60+@6  : Green < 2%  | Amber 2-4%  | Red > 4%\n"
        "  Yr1 NCL: Green < 3%  | Amber 3-6%  | Red > 6%\n\n"

        "STRUCTURE YOUR RESPONSE IN EXACTLY THESE 4 SECTIONS:\n\n"

        "1. HEADLINE FINDING (2-3 sentences)\n"
        "   The single most important thing the data shows. Be specific — name the metric, "
        "   the segment or vintage, and the direction (improving/worsening).\n\n"

        "2. KEY FINDINGS (3-5 bullet points)\n"
        "   Each bullet must:\n"
        "   - Name a specific segment, vintage, or metric (not generic statements)\n"
        "   - State the actual value and what benchmark zone it falls in (Green/Amber/Red)\n"
        "   - State whether it is improving, stable, or worsening vs the comparison period\n"
        "   Example: '• Broker channel 30+@3 is 7.8% (RED) — worsening by +2.1pp vs 2024, "
        "   suggesting deteriorating acquisition quality in this channel.'\n\n"

        "3. RED FLAGS (list only if any metric is in Amber or Red zone)\n"
        "   For each red flag:\n"
        "   - Name the segment/vintage and metric\n"
        "   - State the value and benchmark zone\n"
        "   - Give one specific business hypothesis for why this is happening\n"
        "   If no red flags: write 'No red flags — all metrics within Green benchmarks.'\n\n"

        "4. RECOMMENDATIONS (2-4 actionable items)\n"
        "   Each recommendation must be:\n"
        "   - Tied to a specific finding above (not generic advice)\n"
        "   - Actionable by a risk or credit team (tighten policy, adjust limit, investigate, monitor)\n"
        "   - Prioritised: label each as IMMEDIATE, SHORT-TERM, or MONITOR\n"
        "   Example: '[IMMEDIATE] Tighten credit bureau cut-off for Broker channel acquisitions — "
        "   30+@3 at 7.8% exceeds Red threshold and is trending upward.'\n\n"

        "User's Original Question:\n" + question + "\n\n"

        "Analyses Performed:\n" + analyses_text + "\n\n"

        "Provide your structured interpretation now:"
    )

    messages = [
        {
            "role": "system",
            "content": (
                "You are a senior credit risk analyst delivering a structured portfolio risk assessment. "
                "Be specific — always name segments, vintages, and metrics by name. "
                "Always reference benchmark zones (Green/Amber/Red). "
                "Never give generic advice. Every recommendation must trace back to a specific data finding."
            )
        },
        {"role": "user", "content": prompt}
    ]

    response = client.chat.completions.create(
        model=HF_MODEL_ID,
        messages=messages,
        max_tokens=1024,
        temperature=0.3,
        top_p=0.95
    )

    interpretation = (
        response.choices[0].message.content
        if hasattr(response, "choices")
        else str(response)
    )
    return interpretation


# ---------------------------------------------------
# MASTER ORCHESTRATOR FUNCTION
# ---------------------------------------------------

def run_deep_dive_analysis(
    question: str,
    acq: pd.DataFrame,
    perf: pd.DataFrame,
    master_df: pd.DataFrame
):
    """
    End-to-end deep dive analysis:
    1. Break question into 1-3 structured requirements
    2. Generate code for each requirement
    3. Execute each requirement's code sequentially
    4. Synthesize results and provide senior analyst interpretation
    """

    print(f"\n[DEEP DIVE START] Question: {question}")
    print(f"[DEBUG] Data shapes - Acq: {acq.shape}, Perf: {perf.shape}, Master: {master_df.shape}")

    # Step 1: Generate requirements
    print("[DEBUG] Step 1: Generating requirements...")
    req_response = generate_analysis_requirements(question, acq, perf, master_df)

    if not req_response["success"]:
        return {
            "success": False,
            "question": question,
            "requirements": [],
            "all_results": [],
            "interpretation": f"Failed to generate requirements: {req_response['error']}",
            "error": req_response["error"]
        }

    requirements = req_response["requirements"][:3]  # Cap at 3

    # Step 2 & 3: Execute all requirements
    print(f"[DEBUG] Step 2-3: Executing {len(requirements)} requirements...")
    all_results, context_text = execute_all_requirements(requirements, acq, perf, master_df)

    # Step 4: Interpret results
    print("[DEBUG] Step 4: Interpreting all results...")
    interpretation = interpret_all_results(question, all_results, context_text)
    print("[DEEP DIVE END] Analysis complete\n")

    return {
        "success": True,
        "question": question,
        "requirements": requirements,
        "all_results": all_results,
        "interpretation": interpretation,
        "error": None
    }