Sample data creation pipeline added

app/ads1/merge.py

@@ -0,0 +1,47 @@
+import pandas as pd
+
+def merge_dfs(real_dfs: dict, sample_dfs: dict) -> dict:
+    """
+    Merge real Google Ads data with synthetic sample data.
+    Keeps schema identical and adds source tracking.
+    """
+
+    merged = {}
+
+    for key in real_dfs.keys():
+        real_df = real_dfs.get(key)
+        sample_df = sample_dfs.get(key)
+
+        # If both exist
+        if real_df is not None and sample_df is not None:
+            real_df = real_df.copy()
+            sample_df = sample_df.copy()
+
+            real_df["source"] = "real"
+            sample_df["source"] = "sample"
+
+            # Align columns safely
+            for col in real_df.columns:
+                if col not in sample_df.columns:
+                    sample_df[col] = None
+
+            for col in sample_df.columns:
+                if col not in real_df.columns:
+                    real_df[col] = None
+
+            # Ensure same column order
+            sample_df = sample_df[real_df.columns]
+
+            merged[key] = pd.concat([real_df, sample_df], ignore_index=True)
+
+        elif real_df is not None:
+            real_df = real_df.copy()
+            real_df["source"] = "real"
+            merged[key] = real_df
+
+        elif sample_df is not None:
+            sample_df = sample_df.copy()
+            sample_df["source"] = "sample"
+            merged[key] = sample_df
+
+    return merged

app/ads1/sample_data.py

@@ -0,0 +1,218 @@
+import numpy as np
+import pandas as pd
+
+def _rand(low, high, size):
+    return np.random.randint(low, high, size)
+
+def _money(low, high, size):
+    return np.round(np.random.uniform(low, high, size), 2)
+
+def _pick(arr, size):
+    return np.random.choice(arr, size)
+
+def generate_sample_campaigns():
+    campaigns = [
+        {
+            "id": 1001,
+            "name": "Nursery Admissions 2026",
+            "status": "ENABLED",
+            "impressions": 120000,
+            "clicks": 8500,
+            "cost": 12000.0,
+            "ctr": 7.08,
+            "conversions": 420,
+        },
+        {
+            "id": 1002,
+            "name": "Playgroup Enrollment Campaign",
+            "status": "ENABLED",
+            "impressions": 90000,
+            "clicks": 6200,
+            "cost": 8000.0,
+            "ctr": 6.88,
+            "conversions": 310,
+        },
+        {
+            "id": 1003,
+            "name": "Summer Camp 2026",
+            "status": "ENABLED",
+            "impressions": 150000,
+            "clicks": 4000,
+            "cost": 7000.0,
+            "ctr": 2.66,
+            "conversions": 180,
+        },
+        {
+            "id": 1004,
+            "name": "School Tour Booking Campaign",
+            "status": "ENABLED",
+            "impressions": 60000,
+            "clicks": 2200,
+            "cost": 3500.0,
+            "ctr": 3.66,
+            "conversions": 140,
+        },
+    ]
+
+    return pd.DataFrame(campaigns)
+
+# 2. SEARCH TERMS
+def generate_sample_search_terms():
+    positive_intent = [
+        "nursery admission near me",
+        "best preschool in Yelahanka",
+        "play school admission 2026",
+        "kg admission Bangalore",
+        "preschool fees near me",
+        "daycare near Yelahanka",
+    ]
+
+    negative_intent = [
+        "free babysitting jobs",
+        "toy store near me",
+        "kids games online",
+        "montessori certification course",
+        "child psychology course",
+        "teaching jobs preschool",
+    ]
+
+    campaigns = [
+        "Nursery Admissions 2026",
+        "Playgroup Enrollment Campaign",
+        "Summer Camp 2026",
+        "School Tour Booking Campaign",
+    ]
+
+    rows = []
+
+    for campaign in campaigns:
+        for term in positive_intent + negative_intent:
+
+            clicks = np.random.randint(5, 250)
+            impressions = clicks * np.random.randint(5, 60)
+            cost = round(clicks * np.random.uniform(0.5, 4.0), 2)
+
+            # realistic conversion logic
+            if term in positive_intent:
+                conversions = np.random.randint(1, 25)
+            else:
+                conversions = np.random.choice([0, 0, 0, 1])
+
+            rows.append({
+                "search_term": term,
+                "campaign_name": campaign,
+                "clicks": clicks,
+                "impressions": impressions,
+                "cost": cost,
+                "conversions": conversions,
+            })
+
+    return pd.DataFrame(rows)
+
+# 3. KEYWORDS
+def generate_sample_keywords():
+    keywords = [
+        "preschool admission",
+        "nursery school near me",
+        "play school Bangalore",
+        "kg admission",
+        "daycare Yelahanka",
+        "best preschool",
+    ]
+
+    campaigns = [
+        "Nursery Admissions 2026",
+        "Playgroup Enrollment Campaign",
+        "Summer Camp 2026",
+        "School Tour Booking Campaign",
+    ]
+
+    rows = []
+
+    for campaign in campaigns:
+        for kw in keywords:
+
+            clicks = np.random.randint(50, 500)
+            impressions = clicks * np.random.randint(10, 80)
+            cost = round(clicks * np.random.uniform(0.8, 5.0), 2)
+
+            # realistic conversion behavior
+            if "admission" in kw or "preschool" in kw:
+                conversions = np.random.randint(5, 40)
+            else:
+                conversions = np.random.choice([0, 0, 1, 2, 3])
+
+            ctr = round((clicks / impressions) * 100, 2)
+
+            rows.append({
+                "campaign_name": campaign,
+                "keyword": kw,
+                "clicks": clicks,
+                "impressions": impressions,
+                "cost": cost,
+                "conversions": conversions,
+                "ctr": ctr,
+            })
+
+    return pd.DataFrame(rows)
+
+# ==================================================
+# 4. HOURLY (OPTIONAL BUT MATCHES REAL STRUCTURE)
+# ==================================================
+
+def generate_sample_hourly():
+    hours = list(range(24))
+    rows = []
+
+    for campaign in [
+        "Nursery Admissions 2026",
+        "Playgroup Enrollment Campaign",
+        "Summer Camp 2026",
+        "School Tour Booking Campaign",
+    ]:
+        for h in hours:
+            clicks = np.random.randint(0, 80)
+            impressions = clicks * np.random.randint(5, 30)
+            cost = round(clicks * np.random.uniform(0.2, 3.0), 2)
+
+            rows.append({
+                "date": "2026-06-01",
+                "hour": h,
+                "clicks": clicks,
+                "impressions": impressions,
+                "cost": cost,
+            })
+
+    return pd.DataFrame(rows)
+
+
+# ==================================================
+# 5. GEO (OPTIONAL BUT MATCHES REAL STRUCTURE)
+# ==================================================
+
+def generate_sample_geo():
+    countries = ["IN", "AE", "US"]
+    rows = []
+
+    for c in countries:
+        rows.append({
+            "country_id": c,
+            "clicks": np.random.randint(500, 5000),
+            "impressions": np.random.randint(10000, 100000),
+            "cost": round(np.random.uniform(500, 5000), 2),
+        })
+
+    return pd.DataFrame(rows)
+
+
+# 4. MASTER GENERATOR
+def generate_sample_dfs():
+    return {
+        "campaigns": generate_sample_campaigns(),
+        "search_terms": generate_sample_search_terms(),
+        "keywords": generate_sample_keywords(),
+        "hourly": generate_sample_hourly(),
+        "geo": generate_sample_geo(),
+        "devices": pd.DataFrame(),            # optional placeholder
+        "recommendations": pd.DataFrame(),    # optional placeholder
+    }

app/controller/session_loader.py

@@ -7,6 +7,9 @@ import pickle
 
 import pandas as pd
 from app.ads1.fetch_ads_data import fetch_all_data, to_dataframes
+from app.ads1.merge import merge_dfs
+from app.ads1.sample_data import generate_sample_dfs
+
 # from dotenv import load_dotenv
 # load_dotenv() 
 
@@ -33,8 +36,11 @@ def load_google_ads_data(force_refresh=False):
                 return pickle.load(f)
 
     print("🌐 Disk cache expired or missing. Fetching live Google Ads data...")
-    raw = fetch_all_data(customer_id)
-    dfs = to_dataframes(raw)
+    real_raw = fetch_all_data(customer_id)
+    real_dfs = to_dataframes(real_raw)
+
+    sample_dfs = generate_sample_dfs()
+    dfs = merge_dfs(real_dfs, sample_dfs)
 
     # Save to disk cache safely
     with open(CACHE_FILE, "wb") as f: