Update app.py

app.py

@@ -10,17 +10,17 @@ from openai import OpenAI
 from pydantic import BaseModel
 from pymongo import MongoClient
 
-# Load .env variables
+# Load environment
 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")
+    raise RuntimeError("MONGO_URI missing in environment variables")
 
 if not OPENAI_API_KEY:
-    raise RuntimeError("❌ OPENAI_API_KEY missing in environment variables")
+    raise RuntimeError("OPENAI_API_KEY missing in environment variables")
 
 # MongoDB Setup
 mongo_client = MongoClient(MONGO_URI)
@@ -31,19 +31,19 @@ if default_db is None:
 budget_collection = default_db["budget"]
 transaction_collection = default_db["transactions"]
 
-# OpenAI Client
+# OpenAI client
 openai = OpenAI(api_key=OPENAI_API_KEY)
 
-# FastAPI App
+# FastAPI app
 app = FastAPI(title="Financial Health Score Service")
 
 
-# Request Model
 class ScoreRequest(BaseModel):
     userId: str
 
 
-# Helpers
+# ----------------------------- HELPERS ---------------------------------- #
+
 def safe_number(value):
     try:
         return float(value)
@@ -78,22 +78,25 @@ def normalize_budgets(budgets):
             "notifications": budget.get("notifications") or [],
             "headCategories": head_categories,
         })
-
     return normalized
 
 
 def normalize_transactions(transactions):
     trimmed = []
     for txn in transactions:
-        date_str = None
-        if isinstance(txn.get("date"), datetime):
-            date_str = txn["date"].date().isoformat()
+        date_value = txn.get("date")
+
+        if isinstance(date_value, datetime):
+            date_str = date_value.date().isoformat()
+        else:
+            date_str = None
 
         trimmed.append({
             "type": txn.get("type"),
             "amount": safe_number(txn.get("amount")),
             "date": date_str,
         })
+
     return trimmed
 
 
@@ -114,15 +117,58 @@ Respond ONLY with:
 
 
 def extract_json_payload(text):
-    """
-    Removes markdown fences and extracts pure JSON.
-    """
+    """Extract JSON from plain text or fenced code blocks."""
     trimmed = (text or "").strip()
     match = re.search(r"```(?:json)?\s*([\s\S]*?)```", trimmed)
+
     payload = match.group(1).strip() if match else trimmed
     return json.loads(payload)
 
 
+def extract_openai_text(response):
+    """
+    Safely extracts text from ANY OpenAI Chat Completion output.
+    Handles:
+      - string output
+      - list of blocks
+      - dict with 'content'
+      - dict with 'text'
+    """
+    try:
+        message = response.choices[0].message
+    except Exception:
+        raise HTTPException(status_code=502, detail="OpenAI returned no valid choices")
+
+    # Case: dict with content
+    if isinstance(message, dict) and "content" in message:
+        content = message["content"]
+    else:
+        content = message
+
+    # Simple string
+    if isinstance(content, str):
+        return content.strip()
+
+    # List of blocks
+    if isinstance(content, list):
+        output_chunks = []
+        for block in content:
+            if isinstance(block, dict) and "text" in block:
+                output_chunks.append(block["text"])
+            elif isinstance(block, str):
+                output_chunks.append(block)
+        return "\n".join(output_chunks).strip()
+
+    # Dict with text
+    if isinstance(content, dict) and "text" in content:
+        return content["text"].strip()
+
+    # Fallback: stringify
+    return str(content).strip()
+
+
+# ----------------------------- ROUTES ---------------------------------- #
+
 @app.get("/")
 def root():
     return {"status": "Financial Health Score service is running"}
@@ -130,7 +176,7 @@ def root():
 
 @app.post("/financial-score")
 def financial_score(payload: ScoreRequest):
-    # Validate userId
+    # Validate ObjectId
     try:
         user_id = ObjectId(payload.userId)
     except Exception:
@@ -139,7 +185,7 @@ def financial_score(payload: ScoreRequest):
     # Fetch budgets
     budgets = list(budget_collection.find({"createdBy": user_id}))
 
-    # Fetch transactions (last 30 days)
+    # Fetch last 30 days transactions
     thirty_days_ago = datetime.utcnow() - timedelta(days=30)
     transactions = list(
         transaction_collection.find({
@@ -148,7 +194,7 @@ def financial_score(payload: ScoreRequest):
         }).sort("date", -1).limit(100)
     )
 
-    # No data fallback
+    # If no data
     if not budgets and not transactions:
         return {
             "userId": payload.userId,
@@ -156,10 +202,10 @@ def financial_score(payload: ScoreRequest):
             "explanation": "No budgets or transactions found."
         }
 
-    # Build LLM Prompt
+    # Build LLM prompt
     prompt = score_prompt(budgets, transactions)
 
-    # Call OpenAI (new SDK)
+    # Call OpenAI (new SDK format)
     try:
         response = openai.chat.completions.create(
             model="gpt-4o-mini",
@@ -169,16 +215,10 @@ def financial_score(payload: ScoreRequest):
     except Exception as exc:
         raise HTTPException(status_code=502, detail=f"OpenAI request failed: {exc}")
 
-    # Extract content text
-    try:
-        model_output = response.choices[0].message["content"]
-    except Exception:
-        raise HTTPException(
-            status_code=502,
-            detail="Invalid response format from OpenAI"
-        )
+    # Extract text safely
+    model_output = extract_openai_text(response)
 
-    # Parse JSON response
+    # Parse JSON
     try:
         parsed = extract_json_payload(model_output)
     except Exception:
@@ -187,18 +227,21 @@ def financial_score(payload: ScoreRequest):
             detail={"error": "Unable to parse OpenAI response", "rawResponse": model_output}
         )
 
-    # Validate fields
+    # Validate format
     if "score" not in parsed or "explanation" not in parsed:
         raise HTTPException(
             status_code=502,
-            detail={"error": "Invalid OpenAI response", "rawResponse": model_output}
+            detail={"error": "OpenAI response missing required fields", "rawResponse": model_output}
         )
 
-    # Clamp score 0–100
-    score_val = max(0, min(100, int(parsed["score"])))
+    # Clamp score to 0–100
+    try:
+        score_val = max(0, min(100, int(float(parsed["score"]))))
+    except Exception:
+        score_val = 0
 
     return {
         "userId": payload.userId,
         "score": score_val,
         "explanation": parsed["explanation"]
-    }
+    }