Update app.py

app.py

@@ -2,11 +2,12 @@
 """
 Financial Health Score Service (FastAPI)
 
-FINAL VERSION:
-- Uses GPT-4o-mini with JSON schema
-- Universal-safe JSON parsing (supports all SDK formats)
-- Correct OpenAI Chat Completions API
-- Bulletproof error handling
+FINAL + CREDIT CARD VERSION:
+- Budget score
+- Transaction score
+- Credit card utilization & repayment score
+- GPT-4o-mini with JSON schema
+- Universal JSON parsing
 """
 
 import json
@@ -21,7 +22,7 @@ from pydantic import BaseModel
 from pymongo import MongoClient
 
 # ===========================
-# ENV & CLIENT SETUP
+# ENV SETUP
 # ===========================
 load_dotenv()
 
@@ -29,21 +30,19 @@ MONGO_URI = os.getenv("MONGO_URI")
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
 if not MONGO_URI:
-    raise RuntimeError("❌ MONGO_URI missing in environment variables")
+    raise RuntimeError("❌ MONGO_URI missing")
 
 if not OPENAI_API_KEY:
-    raise RuntimeError("❌ OPENAI_API_KEY missing in environment variables")
+    raise RuntimeError("❌ OPENAI_API_KEY missing")
 
 mongo_client = MongoClient(MONGO_URI)
 default_db = mongo_client.get_default_database()
-if default_db is None:
-    raise RuntimeError("Unable to determine default database from MONGO_URI")
 
-budget_collection = default_db["budget"]
+budget_collection = default_db["budgets"]
 transaction_collection = default_db["transactions"]
 currencies_collection = default_db["currencies"]
+creditcards_collection = default_db["creditcards"]
 
-# OpenAI Client
 openai = OpenAI(api_key=OPENAI_API_KEY)
 
 app = FastAPI(title="Financial Health Score Service")
@@ -99,24 +98,17 @@ def normalize_transactions(txns):
         date_val = txn.get("date")
         date_str = date_val.date().isoformat() if isinstance(date_val, datetime) else None
 
-        # Resolve currency
         currency_code = None
         currency_id = txn.get("currency")
 
         try:
             if isinstance(currency_id, ObjectId):
                 doc = currencies_collection.find_one({"_id": currency_id})
-            elif isinstance(currency_id, dict) and "$oid" in currency_id:
-                doc = currencies_collection.find_one({"_id": ObjectId(currency_id["$oid"])})
-            elif isinstance(currency_id, str):
+            else:
                 try:
                     doc = currencies_collection.find_one({"_id": ObjectId(currency_id)})
                 except:
-                    doc = currencies_collection.find_one({"code": currency_id}) or \
-                          currencies_collection.find_one({"currency": currency_id})
-            else:
-                doc = None
-
+                    doc = None
             if doc:
                 currency_code = doc.get("code") or doc.get("currency")
         except:
@@ -131,36 +123,73 @@ def normalize_transactions(txns):
     return out
 
 
-def build_prompt(budgets, transactions):
-    return f"""
-You are a financial wellness expert. Use the user's budgets and last 30 days of transactions
-to compute a financial health score from 0 to 100.
+def normalize_creditcards(cards):
+    out = []
+    for c in cards:
+        try:
+            start_date = c.get("billing_cycle", {}).get("start_date")
+            end_date = c.get("billing_cycle", {}).get("end_date")
+            start_date = start_date.date().isoformat() if isinstance(start_date, datetime) else None
+            end_date = end_date.date().isoformat() if isinstance(end_date, datetime) else None
+
+            due_date = c.get("due_date")
+            due_date = due_date.date().isoformat() if isinstance(due_date, datetime) else None
+        except:
+            start_date = end_date = due_date = None
+
+        out.append({
+            "card_name": c.get("card_name"),
+            "credit_limit": safe_number(c.get("credit_limit")),
+            "current_balance": safe_number(c.get("current_balance")),
+            "total_due_amount": safe_number(c.get("total_due_amount")),
+            "minimum_due": safe_number(c.get("minimum_due")),
+            "billing_cycle_start": start_date,
+            "billing_cycle_end": end_date,
+            "due_date": due_date,
+        })
+    return out
+
 
-Rules:
-- ALWAYS prefix currency amounts with currency code (e.g., "INR 10,000").
-- Keep explanation short (1–2 sentences).
-- Score must reflect spending, budget control, and overall balance.
-- Respond ONLY in the JSON schema required below.
+def build_prompt(budgets, transactions, creditcards):
+    return f"""
+You are a financial health scoring engine.
+
+Compute a score (0–100) considering:
+1. Budgets usage
+2. Spending patterns from recent transactions (last 30 days)
+3. Credit card health:
+   - utilization ratio (balance / credit_limit)
+   - due amount status
+   - repayment behavior
+   - risk from minimum-due payments
+   - future risk if due_date is near
+
+Scoring rules:
+- A lower credit utilization (<30%) increases score.
+- High utilization (>70%) lowers score sharply.
+- Overdue or large due amounts reduce score.
+- Good budget management increases score.
+- Overspending reduces score.
+- Make explanation short (2 sentences max).
+- MUST follow JSON schema.
 
 Budgets:
 {json.dumps(budgets, indent=2)}
 
 Transactions:
 {json.dumps(transactions, indent=2)}
+
+CreditCards:
+{json.dumps(creditcards, indent=2)}
 """
 
 
 # ===========================
 # ROUTES
 # ===========================
-@app.get("/")
-def root():
-    return {"status": "Financial Health Score service running"}
-
-
 @app.post("/financial-score")
 def financial_score(payload: ScoreRequest):
-    # Validate ObjectId
+    # Validate ID
     try:
         user_id = ObjectId(payload.userId)
     except:
@@ -175,23 +204,24 @@ def financial_score(payload: ScoreRequest):
     txns_raw = list(
         transaction_collection.find(
             {"user": user_id, "date": {"$gte": thirty_days_ago}}
-        ).sort("date", -1).limit(150)
+        ).sort("date", -1).limit(200)
     )
     transactions = normalize_transactions(txns_raw)
 
-    # If no data
-    if not budgets and not transactions:
+    # Fetch credit cards
+    cards_raw = list(creditcards_collection.find({"user_id": user_id}))
+    creditcards = normalize_creditcards(cards_raw)
+
+    # No data
+    if not budgets and not transactions and not creditcards:
         return {
             "userId": payload.userId,
             "score": 0,
-            "explanation": "No budgets or recent transactions found."
+            "explanation": "No financial data found."
         }
 
-    prompt = build_prompt(budgets, transactions)
+    prompt = build_prompt(budgets, transactions, creditcards)
 
-    # ===========================
-    # GPT-4o-mini WITH JSON SCHEMA
-    # ===========================
     try:
         response = openai.chat.completions.create(
             model="gpt-4o-mini",
@@ -212,24 +242,20 @@ def financial_score(payload: ScoreRequest):
                 }
             },
             messages=[
-                {"role": "system", "content": "You are a financial scoring engine."},
+                {"role": "system", "content": "You calculate financial health."},
                 {"role": "user", "content": prompt}
             ]
         )
     except Exception as exc:
         raise HTTPException(status_code=502, detail=f"OpenAI request failed: {exc}")
 
-    # ===========================
-    # UNIVERSAL JSON PARSING (supports ALL SDK formats)
-    # ===========================
+    # UNIVERSAL JSON PARSING
     try:
         content = response.choices[0].message.content
 
-        # Case A — list of dicts (new SDK)
         if isinstance(content, list):
             raw_json = content[0].get("text", content[0])
         else:
-            # Case B — plain string (older SDK)
             raw_json = content
 
         if not isinstance(raw_json, str):
@@ -243,13 +269,11 @@ def financial_score(payload: ScoreRequest):
             detail=f"Could not parse JSON output: {e}"
         )
 
-    # Score normalization
     score_val = parsed.get("score", 0)
     try:
         score_val = int(float(score_val))
     except:
         score_val = 0
-
     score_val = max(0, min(100, score_val))
 
     return {
@@ -257,3 +281,265 @@ def financial_score(payload: ScoreRequest):
         "score": score_val,
         "explanation": parsed.get("explanation")
     }
+
+
+
+# # main.py
+# """
+# Financial Health Score Service (FastAPI)
+
+# FINAL VERSION:
+# - Uses GPT-4o-mini with JSON schema
+# - Universal-safe JSON parsing (supports all SDK formats)
+# - Correct OpenAI Chat Completions API
+# - Bulletproof error handling
+# """
+
+# import json
+# import os
+# from datetime import datetime, timedelta
+
+# from bson import ObjectId
+# from dotenv import load_dotenv
+# from fastapi import FastAPI, HTTPException
+# from openai import OpenAI
+# from pydantic import BaseModel
+# from pymongo import MongoClient
+
+# # ===========================
+# # ENV & CLIENT SETUP
+# # ===========================
+# load_dotenv()
+
+# MONGO_URI = os.getenv("MONGO_URI")
+# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# if not MONGO_URI:
+#     raise RuntimeError("❌ MONGO_URI missing in environment variables")
+
+# if not OPENAI_API_KEY:
+#     raise RuntimeError("❌ OPENAI_API_KEY missing in environment variables")
+
+# mongo_client = MongoClient(MONGO_URI)
+# default_db = mongo_client.get_default_database()
+# if default_db is None:
+#     raise RuntimeError("Unable to determine default database from MONGO_URI")
+
+# budget_collection = default_db["budget"]
+# transaction_collection = default_db["transactions"]
+# currencies_collection = default_db["currencies"]
+
+# # OpenAI Client
+# openai = OpenAI(api_key=OPENAI_API_KEY)
+
+# app = FastAPI(title="Financial Health Score Service")
+
+
+# # ===========================
+# # MODELS
+# # ===========================
+# class ScoreRequest(BaseModel):
+#     userId: str
+
+
+# # ===========================
+# # HELPERS
+# # ===========================
+# def safe_number(v):
+#     try:
+#         return float(v)
+#     except:
+#         return None
+
+
+# def normalize_budgets(budgets):
+#     out = []
+#     for b in budgets:
+#         heads = []
+#         for h in b.get("headCategories", []) or []:
+#             heads.append({
+#                 "spendLimitType": h.get("spendLimitType"),
+#                 "spendAmount": safe_number(h.get("spendAmount")),
+#                 "maxAmount": safe_number(h.get("maxAmount")),
+#                 "remainingAmount": safe_number(h.get("remainingAmount")),
+#                 "notifications": h.get("notifications") or []
+#             })
+
+#         out.append({
+#             "name": b.get("name"),
+#             "status": b.get("status"),
+#             "period": b.get("period"),
+#             "maxAmount": safe_number(b.get("maxAmount")),
+#             "spendAmount": safe_number(b.get("spendAmount")),
+#             "remainingAmount": safe_number(b.get("remainingAmount")),
+#             "rollover": b.get("rollover"),
+#             "notifications": b.get("notifications") or [],
+#             "headCategories": heads,
+#         })
+#     return out
+
+
+# def normalize_transactions(txns):
+#     out = []
+#     for txn in txns:
+#         date_val = txn.get("date")
+#         date_str = date_val.date().isoformat() if isinstance(date_val, datetime) else None
+
+#         # Resolve currency
+#         currency_code = None
+#         currency_id = txn.get("currency")
+
+#         try:
+#             if isinstance(currency_id, ObjectId):
+#                 doc = currencies_collection.find_one({"_id": currency_id})
+#             elif isinstance(currency_id, dict) and "$oid" in currency_id:
+#                 doc = currencies_collection.find_one({"_id": ObjectId(currency_id["$oid"])})
+#             elif isinstance(currency_id, str):
+#                 try:
+#                     doc = currencies_collection.find_one({"_id": ObjectId(currency_id)})
+#                 except:
+#                     doc = currencies_collection.find_one({"code": currency_id}) or \
+#                           currencies_collection.find_one({"currency": currency_id})
+#             else:
+#                 doc = None
+
+#             if doc:
+#                 currency_code = doc.get("code") or doc.get("currency")
+#         except:
+#             currency_code = None
+
+#         out.append({
+#             "type": txn.get("type"),
+#             "amount": safe_number(txn.get("amount")),
+#             "currency": currency_code,
+#             "date": date_str,
+#         })
+#     return out
+
+
+# def build_prompt(budgets, transactions):
+#     return f"""
+# You are a financial wellness expert. Use the user's budgets and last 30 days of transactions
+# to compute a financial health score from 0 to 100.
+
+# Rules:
+# - ALWAYS prefix currency amounts with currency code (e.g., "INR 10,000").
+# - Keep explanation short (1–2 sentences).
+# - Score must reflect spending, budget control, and overall balance.
+# - Respond ONLY in the JSON schema required below.
+
+# Budgets:
+# {json.dumps(budgets, indent=2)}
+
+# Transactions:
+# {json.dumps(transactions, indent=2)}
+# """
+
+
+# # ===========================
+# # ROUTES
+# # ===========================
+# @app.get("/")
+# def root():
+#     return {"status": "Financial Health Score service running"}
+
+
+# @app.post("/financial-score")
+# def financial_score(payload: ScoreRequest):
+#     # Validate ObjectId
+#     try:
+#         user_id = ObjectId(payload.userId)
+#     except:
+#         raise HTTPException(status_code=400, detail="Invalid userId")
+
+#     # Fetch budgets
+#     budgets_raw = list(budget_collection.find({"createdBy": user_id}))
+#     budgets = normalize_budgets(budgets_raw)
+
+#     # Fetch last 30 days transactions
+#     thirty_days_ago = datetime.utcnow() - timedelta(days=30)
+#     txns_raw = list(
+#         transaction_collection.find(
+#             {"user": user_id, "date": {"$gte": thirty_days_ago}}
+#         ).sort("date", -1).limit(150)
+#     )
+#     transactions = normalize_transactions(txns_raw)
+
+#     # If no data
+#     if not budgets and not transactions:
+#         return {
+#             "userId": payload.userId,
+#             "score": 0,
+#             "explanation": "No budgets or recent transactions found."
+#         }
+
+#     prompt = build_prompt(budgets, transactions)
+
+#     # ===========================
+#     # GPT-4o-mini WITH JSON SCHEMA
+#     # ===========================
+#     try:
+#         response = openai.chat.completions.create(
+#             model="gpt-4o-mini",
+#             temperature=0.6,
+#             response_format={
+#                 "type": "json_schema",
+#                 "json_schema": {
+#                     "name": "financial_score",
+#                     "schema": {
+#                         "type": "object",
+#                         "properties": {
+#                             "score": {"type": "number"},
+#                             "explanation": {"type": "string"}
+#                         },
+#                         "required": ["score", "explanation"],
+#                         "additionalProperties": False
+#                     }
+#                 }
+#             },
+#             messages=[
+#                 {"role": "system", "content": "You are a financial scoring engine."},
+#                 {"role": "user", "content": prompt}
+#             ]
+#         )
+#     except Exception as exc:
+#         raise HTTPException(status_code=502, detail=f"OpenAI request failed: {exc}")
+
+#     # ===========================
+#     # UNIVERSAL JSON PARSING (supports ALL SDK formats)
+#     # ===========================
+#     try:
+#         content = response.choices[0].message.content
+
+#         # Case A — list of dicts (new SDK)
+#         if isinstance(content, list):
+#             raw_json = content[0].get("text", content[0])
+#         else:
+#             # Case B — plain string (older SDK)
+#             raw_json = content
+
+#         if not isinstance(raw_json, str):
+#             raw_json = str(raw_json)
+
+#         parsed = json.loads(raw_json)
+
+#     except Exception as e:
+#         raise HTTPException(
+#             status_code=502,
+#             detail=f"Could not parse JSON output: {e}"
+#         )
+
+#     # Score normalization
+#     score_val = parsed.get("score", 0)
+#     try:
+#         score_val = int(float(score_val))
+#     except:
+#         score_val = 0
+
+#     score_val = max(0, min(100, score_val))
+
+#     return {
+#         "userId": payload.userId,
+#         "score": score_val,
+#         "explanation": parsed.get("explanation")
+#     }