add new get visualize endpoint

main.py

@@ -686,6 +686,106 @@ async def not_found_handler(request: Request, exc):
 async def internal_error_handler(request: Request, exc):
     return ORJSONResponse(status_code=500, content={"error": "Internal server error: " + str(exc)})
 
+@app.get("/visualyse/{user_id}", response_class=ORJSONResponse)
+async def visualyse_dashboard(user_id: str):
+    try:
+        conn = psycopg2.connect(DATABASE_URL)
+        cur = conn.cursor()
+        # Fetch all entries for the user
+        cur.execute("SELECT * FROM user_entries WHERE user_id = %s", (user_id,))
+        rows = cur.fetchall()
+        columns = [desc[0] for desc in cur.description]
+        entries = [dict(zip(columns, row)) for row in rows]
+        cur.close()
+        conn.close()
+    except Exception as e:
+        return ORJSONResponse(status_code=500, content={"error": str(e)})
+
+    # Section 1: Expense Overview
+    expenses = [e for e in entries if e["type"] == "expense"]
+    total_expense = sum(a["value"] for e in expenses for a in (e["amounts"] or []))
+    expense_count = len(expenses)
+    expense_by_category = {}
+    for e in expenses:
+        cat = e.get("expense_type", "miscellaneous")
+        amt = sum(a["value"] for a in (e["amounts"] or []))
+        expense_by_category[cat] = expense_by_category.get(cat, 0) + amt
+
+    # Monthly/Weekly Trends
+    monthly_trends = {}
+    for e in expenses:
+        key = f"{e['month']}-{e['year']}"
+        amt = sum(a["value"] for a in (e["amounts"] or []))
+        monthly_trends[key] = monthly_trends.get(key, 0) + amt
+
+    # Section 2: Top Stores & Categories
+    store_stats = {}
+    for e in expenses:
+        for s in (e["stores"] or []):
+            store = s.get("store", "unknown")
+            amt = sum(a["value"] for a in (e["amounts"] or []))
+            if store not in store_stats:
+                store_stats[store] = {"count": 0, "total": 0}
+            store_stats[store]["count"] += 1
+            store_stats[store]["total"] += amt
+    top_categories = sorted(expense_by_category.items(), key=lambda x: x[1], reverse=True)
+
+    # Section 3: Recent Expenses
+    recent_expenses = sorted(expenses, key=lambda e: e.get("created_at", ""), reverse=True)[:10]
+
+    # Section 4: Mood Trends
+    mood_dist = {}
+    for e in entries:
+        mood = e.get("mood", "neutral")
+        mood_dist[mood] = mood_dist.get(mood, 0) + 1
+
+    # Section 5: Tags & Keywords
+    tag_freq = {}
+    for e in entries:
+        for tag in (e["tags"] or []):
+            tag_freq[tag] = tag_freq.get(tag, 0) + 1
+    top_tags = sorted(tag_freq.items(), key=lambda x: x[1], reverse=True)[:10]
+
+    # Section 6: Time Analysis
+    day_stats = {}
+    hour_stats = {}
+    for e in expenses:
+        day = e.get("day_of_week", "unknown")
+        hour = e.get("hour_of_day", 0)
+        amt = sum(a["value"] for a in (e["amounts"] or []))
+        day_stats[day] = day_stats.get(day, 0) + amt
+        hour_stats[hour] = hour_stats.get(hour, 0) + amt
+
+    # Section 7: Meta Info
+    entry_count = len(entries)
+    type_dist = {}
+    for e in entries:
+        t = e.get("type", "other")
+        type_dist[t] = type_dist.get(t, 0) + 1
+
+    dashboard = {
+        "expense_overview": {
+            "total_expense": total_expense,
+            "expense_count": expense_count,
+            "expense_by_category": expense_by_category,
+            "monthly_trends": monthly_trends
+        },
+        "top_stores": store_stats,
+        "top_categories": top_categories,
+        "recent_expenses": recent_expenses,
+        "mood_distribution": mood_dist,
+        "top_tags": top_tags,
+        "time_analysis": {
+            "by_day": day_stats,
+            "by_hour": hour_stats
+        },
+        "meta_info": {
+            "entry_count": entry_count,
+            "type_distribution": type_dist
+        }
+    }
+    return ORJSONResponse(content=dashboard)
+
 @app.post("/analyze", response_class=ORJSONResponse)
 async def analyze(input: TextInput):
     start_time = time.time()  # ⏱️ start