Update mood and expense category

main.py

@@ -233,20 +233,56 @@ def generate_summary(text):
 
 def estimate_mood(text):
     text_lower = text.lower()
+    # Expanded mood map with Hindi/Hinglish and phrases
     mood_map = {
-        "happy": ["happy", "excited", "joy", "grateful", "glad", "pleased", "content", "satisfied", "cheerful", "elated", "joyful", "optimistic", "hopeful", "proud", "relieved", "enthusiastic", "calm", "peaceful", "blessed", "loved", "appreciative", "thankful", "delighted", "ecstatic", "thrilled", "overjoyed", "jubilant", "exhilarated", "radiant", "buoyant", "upbeat", "positive", "motivated", "inspired", "confident", "empowered", "fulfilled", "satisfied", "contented", "serene", "tranquil", "harmonious", "balanced", "centered", "grounded"],
-        "sad": ["sad", "upset", "crying", "lonely", "depressed", "down", "disappointed", "heartbroken", "unhappy", "dismayed", "discouraged", "disheartened", "miserable", "sorrowful", "melancholy", "gloomy", "despondent", "forlorn", "dejected", "woeful", "doleful", "glum", "downcast", "crestfallen", "dispirited", "desolate", "blue", "heavy-hearted", "low-spirited", "disconsolate"],
-        "angry": ["angry", "annoyed", "frustrated", "irritated", "mad", "furious", "enraged", "livid", "outraged", "infuriated", "exasperated", "indignant", "resentful", "incensed", "fuming", "seething", "irate", "cross", "peeved", "vexed", "agitated", "upset", "displeased", "disgruntled", "miffed", "huffy", "snappy", "testy", "touchy", "ticked off", "riled up", "worked up"],
-        "nervous": ["nervous", "anxious", "scared", "worried", "fearful", "uneasy", "apprehensive", "tense", "jittery", "restless", "on edge", "panicky", "fidgety", "edgy", "stressed", "agitated", "distressed", "troubled", "concerned", "uneasy", "jumpy", "hyper", "frenzied", "overwhelmed", "frazzled", "flustered", "perturbed", "disconcerted", "disquieted", "unsettled"],
-        "unwell": ["sick", "unwell", "not feeling well", "fever", "cold", "headache", "flu", "ill", "nauseous", "dizzy", "tired", "exhausted", "fatigued", "weak", "pain", "ache", "vomit", "cough", "sneeze", "chills", "shivers", "congestion", "runny nose", "coughing", "sore throat", "stomachache", "upset stomach", "diarrhea", "vomiting", "nausea", "drowsy", "sleepy", "lethargic", "groggy", "uncomfortable", "feeling off", "under the weather"],
-        "neutral": [],
-        "anxious": ["anxious", "restless", "uneasy", "jittery", "on edge", "tense", "fidgety", "nervous", "worried", "apprehensive", "scared", "panicky", "hyper", "edgy", "stressed", "agitated", "distressed", "troubled", "concerned", "unsettled", "disconcerted", "disquieted", "perturbed", "flustered", "overwhelmed", "frazzled", "discombobulated", "disoriented", "confused"]
+        "happy": [
+            "happy", "excited", "good", "joy", "grateful", "glad", "pleased", "content", "satisfied", "cheerful", "elated",
+            "maza aa gaya", "acha lag raha hai", "khush", "khushi", "badiya", "mast", "enjoy", "enjoyed", "mazedaar"
+        ],
+        "sad": [
+            "sad", "upset", "crying", "lonely", "depressed", "down", "disappointed", "heartbroken", "unhappy",
+            "bura lag raha hai", "dukhi", "udaas", "rona", "rona aa gaya", "dil toot gaya", "nirash"
+        ],
+        "angry": [
+            "angry", "annoyed", "frustrated", "irritated", "mad", "furious", "gussa", "gusse mein", "chidh", "naraz",
+            "bhadak gaya", "chidh gaya", "irritate", "irritated"
+        ],
+        "nervous": [
+            "nervous", "anxious", "scared", "worried", "fearful", "uneasy", "tensed", "tension", "ghabrahat", "chinta",
+            "parishan", "dara hua", "ghabra gaya", "stress", "stressed"
+        ],
+        "unwell": [
+            "sick", "unwell", "not feeling well", "fever", "cold", "headache", "flu", "ill", "nauseous", "dizzy",
+            "thak gaya", "thaka hua", "bimaar", "bimar", "bukhar", "sardard", "beemar", "kamjor", "thakan"
+        ],
+        "neutral": [
+            "ok", "fine", "theek", "normal", "usual", "routine", "nothing special", "kuch khaas nahi", "no stress"
+        ]
     }
 
+    detected_moods = []
     for mood, keywords in mood_map.items():
         for kw in keywords:
             if kw in text_lower:
-                return mood
+                detected_moods.append(mood)
+                break  # Only need one match per mood
+
+    # Use sentiment as a fallback if no mood keyword matched
+    if not detected_moods:
+        sentiment = get_sentiment_score(text)
+        if sentiment > 0.2:
+            return "happy"
+        elif sentiment < -0.2:
+            return "sad"
+        else:
+            return "neutral"
+
+    # Priority: angry > sad > unwell > nervous > happy > neutral
+    priority = ["angry", "sad", "unwell", "nervous", "happy", "neutral"]
+    for mood in priority:
+        if mood in detected_moods:
+            return mood
+
     return "neutral"
 
 def generate_tags(label, text):
@@ -524,25 +560,63 @@ def predict_expense_category(text, detected_stores):
 
     # Category keyword mapping
     category_keywords = {
-        "food": ["food", "lunch", "dinner", "breakfast", "snacks", "swiggy", "zomato", "dominos", "pizza", "kfc", "mcdonald"],
-        "transport": ["uber", "ola", "taxi", "cab", "bus", "train", "metro", "flight", "auto"],
-        "shopping": ["amazon", "flipkart", "myntra", "shopping", "clothes", "apparel", "shoes", "jeans", "tshirt", "store", "fashion"],
-        "housing": ["rent", "apartment", "house", "flat", "maintenance", "landlord"],
-        "utilities": ["electricity", "power", "water", "gas", "bill", "recharge", "broadband", "wifi", "airtel", "jio"],
-        "entertainment": ["movie", "netflix", "hotstar", "bookmyshow", "spotify", "gaming", "youtube premium"],
-        "health": ["medicine", "hospital", "doctor", "clinic", "pharmacy", "tablet", "surgery", "checkup"],
-        "travel": ["trip", "travel", "tour", "vacation", "hotel", "airbnb", "booking.com", "goibibo", "makemytrip", "yatra"],
-        "education": ["course", "webinar", "class", "training", "workshop", "udemy", "coursera", "byjus", "unacademy", "skill"],
-        "digital_services": ["domain", "hosting", "license", "email", "software", "zoom", "notion", "figma", "aws", "google cloud", "saas"],
-        "gifts_donations": ["gift", "donation", "present", "charity", "ngo", "temple", "mandir", "birthday gift", "festival gift"],
-        "finance": ["insurance", "sip", "mutual fund", "stock", "demat", "zerodha", "investment", "trading", "upstox", "crypto"],
-        "family_kids": ["kid", "baby", "school", "daycare", "tuition", "books", "uniform", "toys", "creche"],
-        "stationery": [
-            "pen", "pencil", "notebook", "diary", "eraser", "sharpener", "paper", "stationery", 
-            "register", "files", "file", "markers", "highlighter", "sticky notes", "geometry box",
-            "stapler", "ink", "printer paper", "stationary shop", "stationary"
-        ]
-    }
+    "food": [
+        "food", "lunch", "dinner", "breakfast", "snacks", "swiggy", "zomato", "dominos", "pizza", "kfc", "mcdonald",
+        "restaurant", "hotel", "cafe", "canteen", "meal", "buffet", "thali", "tiffin", "order", "takeaway", "parcel",
+        "eat", "eating", "brunch", "supper", "kitchen", "cook", "cooking", "chef", "dish", "dishes", "menu", "serve",
+        "served", "serving", "food court", "food delivery", "delivery", "online order", "food app", "food bill",
+        "beverage", "juice", "shake", "smoothie", "coffee", "tea", "chai", "cold drink", "soft drink", "soda", "water bottle",
+        "ice cream", "dessert", "sweet", "sweets", "chocolate", "candy", "bakery", "bread", "cake", "pastry", "cookie",
+        "biscuit", "chips", "fries", "burger", "sandwich", "roll", "wrap", "noodles", "pasta", "rice", "biryani", "curry",
+        "gravy", "dal", "sabzi", "roti", "naan", "paratha", "chapati", "idli", "dosa", "vada", "sambar", "chutney", "samosa",
+        "pakora", "chaat", "pani puri", "golgappa", "sev", "poha", "upma", "maggi", "maggie", "momos", "spring roll",
+        "manchurian", "paneer", "butter chicken", "tandoori", "kebab", "shawarma", "pizza hut", "subway", "starbucks",
+        # Hindi/Hinglish
+        "khana", "nashta", "bhojan", "rasoi", "thali", "dabba", "tiffin", "chai", "paani", "jal", "kharcha khana",
+        "khane ka bill", "khane ka paisa", "khane ki cheez", "khana order", "khana mangwaya", "khana khaya", "khana khud banaya",
+        "khana kharch", "khana kharida", "khana diya", "khana laya", "khana banaya"
+    ],
+    "transport": [
+        "uber", "ola", "taxi", "cab", "bus", "train", "metro", "flight", "auto", "rickshaw", "car", "gaadi", "yatra", "safar", "travel", "ticket", "plane", "udaan", "station", "airport"
+    ],
+    "shopping": [
+        "amazon", "flipkart", "myntra", "shopping", "clothes", "kapde", "apparel", "shoes", "jeans", "tshirt", "store", "fashion", "dukaan", "mall", "bazaar", "market", "kharida", "order diya", "le liya"
+    ],
+    "housing": [
+        "rent", "apartment", "house", "ghar", "flat", "maintenance", "landlord", "kiraya", "makaan", "room", "hostel", "pg", "society"
+    ],
+    "utilities": [
+        "electricity", "power", "bijli", "water", "pani", "gas", "bill", "recharge", "broadband", "wifi", "airtel", "jio", "phone", "mobile", "internet", "light", "cylinder", "connection"
+    ],
+    "entertainment": [
+        "movie", "netflix", "hotstar", "bookmyshow", "spotify", "gaming", "youtube premium", "cinema", "film", "picture", "game", "khel", "manoranjan", "show", "concert"
+    ],
+    "health": [
+        "medicine", "hospital", "doctor", "clinic", "pharmacy", "tablet", "surgery", "checkup", "dawai", "aspatal", "ilaaj", "health", "bimari", "test", "medical", "pathology", "chemist"
+    ],
+    "travel": [
+        "trip", "travel", "tour", "vacation", "hotel", "airbnb", "booking.com", "goibibo", "makemytrip", "yatra", "safar", "holiday", "journey", "musafir", "booking", "trip kiya"
+    ],
+    "education": [
+        "course", "webinar", "class", "training", "workshop", "udemy", "coursera", "byjus", "unacademy", "skill", "padhai", "school", "college", "tuition", "kitab", "book", "fees", "shiksha"
+    ],
+    "digital_services": [
+        "domain", "hosting", "license", "email", "software", "zoom", "notion", "figma", "aws", "google cloud", "saas", "subscription", "digital", "online", "app", "service", "renewal"
+    ],
+    "gifts_donations": [
+        "gift", "donation", "present", "charity", "ngo", "temple", "mandir", "birthday gift", "festival gift", "uphaar", "daan", "tohfa", "chanda", "puja", "mandir", "gurudwara"
+    ],
+    "finance": [
+        "insurance", "sip", "mutual fund", "stock", "demat", "zerodha", "investment", "trading", "upstox", "crypto", "policy", "premium", "loan", "emi", "fd", "rd", "paisa", "bank", "account"
+    ],
+    "family_kids": [
+        "kid", "baby", "school", "daycare", "tuition", "books", "uniform", "toys", "creche", "baccha", "bachche", "parivar", "family", "beti", "beta", "child", "children"
+    ],
+    "stationery": [
+        "pen", "pencil", "notebook", "diary", "eraser", "sharpener", "paper", "stationery", "register", "files", "file", "markers", "highlighter", "sticky notes", "geometry box",
+        "stapler", "ink", "printer paper", "stationary shop", "stationary", "copy", "kagaz", "likhne ka saman"
+    ]
+}
 
     # 2. Match using keyword scores
     matched = {cat: sum(1 for kw in kws if kw in text_lower) for cat, kws in category_keywords.items()}
@@ -669,7 +743,7 @@ async def analyze(input: TextInput):
     urgency_score = get_urgency_score(text, parsed_dates)
     detected_stores = detect_store_category(text)
     expense_category = ""
-    if best_label == "expense":
+    if best_label == "expense" or best_label ==  "purchase":
         expense_category = predict_expense_category(text, detected_stores)
 
     # Define action triggers