Upload deep_dive_agentic (1).py

analytics/deep_dive_agentic (1).py

@@ -0,0 +1,551 @@
+# 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_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 = (
+        "You are a senior retail credit risk analyst with 15+ years of portfolio management experience.\n\n"
+        "Your task:\n"
+        "1. Analyze the user's analytical question deeply\n"
+        "2. Determine 1-3 specific analytics requirements needed to fully answer the question\n"
+        "3. For EACH requirement, generate executable pandas code\n"
+        "4. Return ONLY valid JSON, no other text\n\n"
+
+        "AVAILABLE DATA:\n"
+        "- acq: acquisition data with columns: " + ", ".join(acq_cols.keys()) + "\n"
+        "- perf: performance data with columns: " + ", ".join(perf_cols.keys()) + "\n"
+        "- master_df: merged acq+perf, includes all above columns\n\n"
+
+        "COLUMN DESCRIPTIONS:\n"
+        "Acquisition (acq):\n"
+        + "\n".join([f" - {k}: {v}" for k, v in acq_cols.items()]) + "\n\n"
+        "Performance (perf):\n"
+        + "\n".join([f" - {k}: {v}" for k, v in perf_cols.items()]) + "\n\n"
+
+        "Available Risk Metrics via generate_metric_view(df, metric_name, group_col):\n"
+        " - 30+@3 (30+ dpd at 3 months)\n"
+        " - 30+@6 (30+ dpd at 6 months)\n"
+        " - 60+@6 (60+ dpd at 6 months)\n"
+        " - Yr1 NCL (Year 1 net charge-off rate)\n\n"
+
+        "CODE GENERATION RULES:\n"
+        "- Generate pandas code ONLY\n"
+        "- IMPORTANT: Always store your final result in a variable named exactly 'result_1', 'result_2', or 'result_3' matching the sequence number\n"
+        "- Use meaningful intermediate variable names (e.g., vintage_analysis, segment_summary)\n"
+        "- Focus on GROUP BY aggregations for insights\n"
+        "- Calculate rates as dollars/total (percentage)\n"
+        "- Sort by risk metrics (descending) to identify worst segments\n"
+        "- Add brief comments for clarity\n"
+        "- NO markdown, NO explanations outside JSON\n\n"
+
+        "JSON STRUCTURE:\n"
+        "{\n"
+        ' "requirements": [\n'
+        '   {\n'
+        '     "sequence": 1,\n'
+        '     "title": "Analysis title",\n'
+        '     "description": "What this code does and why it matters",\n'
+        '     "code": "pandas code here — must assign final result to result_1"\n'
+        "   }\n"
+        " ]\n"
+        "}\n\n"
+
+        "User Question:\n" + question
+    )
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a senior credit risk analyst who generates pandas code for portfolio analytics. "
+                "Return ONLY valid JSON. Always store the final result in result_1, result_2, or result_3."
+            )
+        },
+        {"role": "user", "content": prompt}
+    ]
+
+    response = client.chat.completions.create(
+        model=HF_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"
+        "Your task:\n"
+        "Synthesize the analytical results and provide comprehensive risk insights.\n\n"
+
+        "Focus on:\n"
+        "- Key findings and patterns across all analyses\n"
+        "- Risk deterioration or improvement trends\n"
+        "- Vintage/segment concentration issues and implications\n"
+        "- Root causes of observed patterns\n"
+        "- Unusual trends, anomalies, or red flags\n"
+        "- Actionable recommendations for portfolio management\n"
+        "- Comparative risk assessment (which segments/vintages are most/least risky)\n\n"
+
+        "Guidelines:\n"
+        "- Be analytical and specific (not generic)\n"
+        "- Focus on business implications, not just statistics\n"
+        "- Avoid repeating raw tables; interpret the meaning\n"
+        "- Provide 3-5 key insights\n"
+        "- Suggest next investigative steps if needed\n\n"
+
+        "User's Original Question:\n" + question + "\n\n"
+
+        "Analyses Performed:\n" + analyses_text + "\n\n"
+
+        "Provide your senior analyst interpretation:"
+    )
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a senior credit risk analyst providing executive insights from portfolio analytics."
+        },
+        {"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
+    }