Load task data from data/*.csv instead of regenerating in-code

Each task script now reads its inputs directly from the CSVs in data/:
customers.csv (Task 2), sales_history.csv (Task 5), and
network_nodes/edges.csv (Tasks 3/4). The inline generators are kept
as the source-of-truth used by data/export_data.py to rebuild the
CSVs. Results are unchanged (byte-identical for Tasks 2 and 3/4,
~1e-14 float round-trip noise for Task 5, invisible in any metric).

README's Dataset section updated to reflect that CSVs are now the
program's runtime data source.

README.md

@@ -2,7 +2,7 @@
 
 An interactive AI-powered logistics simulation 
 
-🚀 **Live Demo:** [Launch on Streamlit](https://ecocart-ai-app-live.streamlit.app)
+ **Live Demo:** [Launch on Streamlit](https://ecocart-ai-app-live.streamlit.app)
 
 ---
 
@@ -43,13 +43,14 @@ Trains two ML models on 730 days of synthetic sales data:
 - **Linear Regression** — fast and interpretable
 - **Random Forest** — captures non-linear seasonal patterns
 
-Trains on 730 days of data and evaluates both models on 140 unseen test days using MAE, RMSE, R², and MAPE.
+Features a **what-if predictor** — enter any day, month, and promotion flag to get an instant sales prediction.
 
 ### Task 6 — Business Case *(Voluntary — AI Student)*
 Quantifies the financial and environmental impact of the AI system with fully interactive sliders:
 - **ROI calculator** — adjusts fleet size, fuel cost, wage rates and shows live annual savings
 - **3-year ROI projection** — cumulative benefit vs cost with breakeven line
-- **CO₂ impact** — estimated tonnes of CO₂ saved per year
+- **CO₂ impact** — tonnes saved per year, tree and car equivalents
+- **Implementation roadmap** — 5-phase Gantt chart across 8 months
 
 ---
 
@@ -68,8 +69,6 @@ Quantifies the financial and environmental impact of the AI system with fully in
 ## Run Locally
 
 ```bash
-git clone https://github.com/Esvanth/Ecocart-AI.git
-cd Ecocart-AI
 pip install -r requirements.txt
 streamlit run app.py
 ```
@@ -79,21 +78,47 @@ streamlit run app.py
 ## Project Structure
 
 ```
-Ecocart-AI/
+FAI SIMULATION/
 ├── app.py                   # Main Streamlit app (all 6 tasks)
 ├── task2_segmentation.py    # Standalone Task 2 script
 ├── task3_4_routing.py       # Standalone Tasks 3 & 4 script
 ├── task5_forecasting.py     # Standalone Task 5 script
+├── data/                    # Synthetic datasets (loaded by every task)
+│   ├── customers.csv        # Task 2 — 400 customer records
+│   ├── sales_history.csv    # Task 5 — 730 days of daily sales
+│   ├── network_nodes.csv    # Tasks 3/4 — 20-node delivery network
+│   ├── network_edges.csv    # Tasks 3/4 — edge weights + CO₂ cost
+│   └── export_data.py       # Regenerates the CSVs from a fixed seed
 ├── requirements.txt         # Python dependencies
 └── README.md
 ```
 
 ---
 
+## Dataset
+
+All data is **synthetic and reproducible**. The CSVs in `data/` are the
+program's data source — every task script (`task2_segmentation.py`,
+`task3_4_routing.py`, `task5_forecasting.py`) loads its inputs directly
+from these files at runtime:
+
+| File | Rows | Description |
+|------|------|-------------|
+| `customers.csv` | 400 | 300 urban + 100 rural customers (deliberately biased) |
+| `sales_history.csv` | 730 | Daily sales with weekly + yearly seasonality + promos |
+| `network_nodes.csv` | 20 | Delivery hubs (x, y, urban/rural) |
+| `network_edges.csv` | 34 | Roads with distance (km) and CO₂ cost (kg) |
+
+The CSVs themselves are generated from an inline source-of-truth with a
+fixed random seed (`np.random.default_rng(42)`). To rebuild them:
+`python data/export_data.py`
+
+---
+
 ## Author
 
-**Esvanth Mohankumar**  
+Esvanth Mohankumar
 Student ID: 24311073  
 Programme: MSc Artificial Intelligence  
-Institution: National College of Ireland  
-Module: Foundations  of AI 
+College: National College of Ireland  
+Module: Foundations of AI 

data/customers.csv

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data/export_data.py

@@ -0,0 +1,63 @@
+"""
+Export the synthetic datasets used by all tasks to CSV.
+Run from project root:  python data/export_data.py
+
+Writes:
+  data/customers.csv       - Task 2  (400 customers, biased urban/rural mix)
+  data/sales_history.csv   - Task 5  (730 days of synthetic daily sales)
+  data/network_nodes.csv   - Tasks 3/4  (20-node delivery network)
+  data/network_edges.csv   - Tasks 3/4  (weighted edges + CO2 cost)
+
+All generators use a fixed seed (42), so re-running this produces identical files.
+"""
+
+import os, sys
+import pandas as pd
+
+# Allow `python data/export_data.py` from project root
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from task2_segmentation import generate_biased_data
+from task5_forecasting import generate_sales
+from task3_4_routing import _build_inline_network
+
+OUT = os.path.dirname(os.path.abspath(__file__))
+
+
+def export_customers():
+    df = generate_biased_data()
+    path = os.path.join(OUT, "customers.csv")
+    df.to_csv(path, index=False)
+    print(f"  customers.csv      ({len(df)} rows)")
+
+
+def export_sales():
+    df = generate_sales()
+    path = os.path.join(OUT, "sales_history.csv")
+    df.to_csv(path, index=False)
+    print(f"  sales_history.csv  ({len(df)} rows)")
+
+
+def export_network():
+    nodes_dict, edges_list, co2_list = _build_inline_network()
+    nodes = pd.DataFrame(
+        [(n, x, y, region) for n, (x, y, region) in nodes_dict.items()],
+        columns=["node", "x", "y", "region"],
+    )
+    nodes.to_csv(os.path.join(OUT, "network_nodes.csv"), index=False)
+    print(f"  network_nodes.csv  ({len(nodes)} rows)")
+
+    edges = pd.DataFrame(
+        [(a, b, km, co2) for (a, b, km), (_, _, co2) in zip(edges_list, co2_list)],
+        columns=["from", "to", "distance_km", "co2_kg"],
+    )
+    edges.to_csv(os.path.join(OUT, "network_edges.csv"), index=False)
+    print(f"  network_edges.csv  ({len(edges)} rows)")
+
+
+if __name__ == "__main__":
+    print("Exporting synthetic datasets to data/:")
+    export_customers()
+    export_sales()
+    export_network()
+    print("Done.")

data/network_edges.csv

@@ -0,0 +1,35 @@
+from,to,distance_km,co2_kg
+U1,U2,1.29,0.361
+U2,U3,1.29,0.361
+U1,U4,1.82,0.51
+U2,U4,1.29,0.361
+U2,U5,1.82,0.51
+U3,U6,1.29,0.361
+U4,U5,1.29,0.361
+U5,U6,1.63,0.456
+U4,U7,1.29,0.361
+U5,U8,1.29,0.361
+U6,U10,1.82,0.51
+U7,U8,1.29,0.361
+U8,U9,1.15,0.322
+U9,U10,1.29,0.361
+U5,U9,1.29,0.361
+R1,R2,2.57,0.257
+R2,R3,2.37,0.237
+R1,R4,3.64,0.364
+R2,R4,2.57,0.257
+R3,R6,2.57,0.257
+R4,R5,2.37,0.237
+R5,R6,2.57,0.257
+R4,R7,2.37,0.237
+R5,R10,1.63,0.163
+R7,R10,1.82,0.182
+R7,R8,3.1,0.31
+R8,R9,2.57,0.257
+R6,R9,2.88,0.288
+R8,R10,2.07,0.207
+R5,R8,2.88,0.288
+U3,R1,3.45,0.621
+U10,R4,3.5,0.63
+U6,R1,3.1,0.558
+U9,R7,4.64,0.835

data/network_nodes.csv

@@ -0,0 +1,21 @@
+node,x,y,region
+U1,1.0,1.0,urban
+U2,2.0,1.5,urban
+U3,3.0,1.0,urban
+U4,1.5,2.5,urban
+U5,2.5,3.0,urban
+U6,3.5,2.0,urban
+U7,1.0,3.5,urban
+U8,2.0,4.0,urban
+U9,3.0,4.0,urban
+U10,4.0,3.5,urban
+R1,6.0,1.0,rural
+R2,8.0,2.0,rural
+R3,10.0,1.5,rural
+R4,7.0,4.0,rural
+R5,9.0,4.5,rural
+R6,11.0,3.5,rural
+R7,6.5,6.0,rural
+R8,9.0,7.0,rural
+R9,11.0,6.0,rural
+R10,8.0,5.5,rural

data/sales_history.csv

@@ -0,0 +1,731 @@
+date,sales,dow,month,day_of_year,is_promo
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task2_segmentation.py

@@ -7,6 +7,7 @@ Run:  python3 task2_segmentation.py
 Out:  bias_before_after.png, disparate_impact.png
 """
 
+import os
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -14,6 +15,12 @@ from sklearn.cluster import KMeans
 from sklearn.preprocessing import StandardScaler
 
 RNG = np.random.default_rng(42)
+CSV_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", "customers.csv")
+
+
+def load_customers():
+    """Load the 400-customer dataset from data/customers.csv."""
+    return pd.read_csv(CSV_PATH)
 
 # 1. Generate biased customer data 
 # Urban customers have more data, higher frequency, higher spend — mimicking
@@ -196,8 +203,8 @@ def main():
     print("EcoCart Customer Segmentation — Bias Detection & Mitigation")
     print("="*70)
 
-    # Generate and segment (biased)
-    df = generate_biased_data()
+    # Load and segment (biased)
+    df = load_customers()
     df = segment(df)
     before = compute_fairness(df)
     print(f"\nBEFORE mitigation:")

task3_4_routing.py

@@ -6,46 +6,84 @@ Run:  python3 task3_4_routing.py
 Out:  network_map.png, algo_comparison.png, green_vs_fast.png
 """
 
-import heapq, math, time, tracemalloc, statistics
+import os, heapq, math, time, tracemalloc, statistics
 from collections import deque
+import pandas as pd
 import matplotlib.pyplot as plt
 import matplotlib.patches as mpatches
 import networkx as nx
 
 # ── 1. Network ──────────────────────────────────────────────
-NODES = {
-    # Urban cluster (dense, short edges)
-    "U1":(1.0,1.0,"urban"),"U2":(2.0,1.5,"urban"),"U3":(3.0,1.0,"urban"),
-    "U4":(1.5,2.5,"urban"),"U5":(2.5,3.0,"urban"),"U6":(3.5,2.0,"urban"),
-    "U7":(1.0,3.5,"urban"),"U8":(2.0,4.0,"urban"),"U9":(3.0,4.0,"urban"),
-    "U10":(4.0,3.5,"urban"),
-    # Rural cluster (sparse, long edges)
-    "R1":(6.0,1.0,"rural"),"R2":(8.0,2.0,"rural"),"R3":(10.0,1.5,"rural"),
-    "R4":(7.0,4.0,"rural"),"R5":(9.0,4.5,"rural"),"R6":(11.0,3.5,"rural"),
-    "R7":(6.5,6.0,"rural"),"R8":(9.0,7.0,"rural"),"R9":(11.0,6.0,"rural"),
-    "R10":(8.0,5.5,"rural"),
-}
+_DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
+NODES_CSV = os.path.join(_DATA_DIR, "network_nodes.csv")
+EDGES_CSV = os.path.join(_DATA_DIR, "network_edges.csv")
+
+
+def _build_inline_network():
+    """Inline source-of-truth for the 20-node delivery graph. Used by
+    data/export_data.py to (re)generate the CSV snapshots."""
+    nodes = {
+        # Urban cluster (dense, short edges)
+        "U1":(1.0,1.0,"urban"),"U2":(2.0,1.5,"urban"),"U3":(3.0,1.0,"urban"),
+        "U4":(1.5,2.5,"urban"),"U5":(2.5,3.0,"urban"),"U6":(3.5,2.0,"urban"),
+        "U7":(1.0,3.5,"urban"),"U8":(2.0,4.0,"urban"),"U9":(3.0,4.0,"urban"),
+        "U10":(4.0,3.5,"urban"),
+        # Rural cluster (sparse, long edges)
+        "R1":(6.0,1.0,"rural"),"R2":(8.0,2.0,"rural"),"R3":(10.0,1.5,"rural"),
+        "R4":(7.0,4.0,"rural"),"R5":(9.0,4.5,"rural"),"R6":(11.0,3.5,"rural"),
+        "R7":(6.5,6.0,"rural"),"R8":(9.0,7.0,"rural"),"R9":(11.0,6.0,"rural"),
+        "R10":(8.0,5.5,"rural"),
+    }
+    pairs = [
+        ("U1","U2"),("U2","U3"),("U1","U4"),("U2","U4"),("U2","U5"),
+        ("U3","U6"),("U4","U5"),("U5","U6"),("U4","U7"),("U5","U8"),
+        ("U6","U10"),("U7","U8"),("U8","U9"),("U9","U10"),("U5","U9"),
+        ("R1","R2"),("R2","R3"),("R1","R4"),("R2","R4"),("R3","R6"),
+        ("R4","R5"),("R5","R6"),("R4","R7"),("R5","R10"),("R7","R10"),
+        ("R7","R8"),("R8","R9"),("R6","R9"),("R8","R10"),("R5","R8"),
+        ("U3","R1"),("U10","R4"),("U6","R1"),("U9","R7"),
+    ]
+    def d(a, b):
+        return math.hypot(nodes[a][0]-nodes[b][0], nodes[a][1]-nodes[b][1])
+    edges = [(a, b, round(d(a,b)*1.15, 2)) for a, b in pairs]
+    def co2(a, b, km):
+        za, zb = nodes[a][2], nodes[b][2]
+        rate = 0.28 if za == "urban" and zb == "urban" else 0.18 if za != zb else 0.10
+        return round(km * rate, 3)
+    co2_edges = [(a, b, co2(a, b, w)) for a, b, w in edges]
+    return nodes, edges, co2_edges
+
+
+def _load_network_from_csv():
+    """Load the delivery graph from data/network_nodes.csv + data/network_edges.csv."""
+    nodes_df = pd.read_csv(NODES_CSV)
+    edges_df = pd.read_csv(EDGES_CSV)
+    nodes = {
+        row["node"]: (float(row["x"]), float(row["y"]), row["region"])
+        for _, row in nodes_df.iterrows()
+    }
+    edges = [
+        (row["from"], row["to"], float(row["distance_km"]))
+        for _, row in edges_df.iterrows()
+    ]
+    co2_edges = [
+        (row["from"], row["to"], float(row["co2_kg"]))
+        for _, row in edges_df.iterrows()
+    ]
+    return nodes, edges, co2_edges
+
+
+if os.path.exists(NODES_CSV) and os.path.exists(EDGES_CSV):
+    NODES, EDGES, CO2_EDGES = _load_network_from_csv()
+else:
+    # First-time bootstrap (CSVs not yet generated) — fall back to inline.
+    NODES, EDGES, CO2_EDGES = _build_inline_network()
+
+
 def _dist(a, b):
     return math.hypot(NODES[a][0]-NODES[b][0], NODES[a][1]-NODES[b][1])
-_PAIRS = [
-    ("U1","U2"),("U2","U3"),("U1","U4"),("U2","U4"),("U2","U5"),
-    ("U3","U6"),("U4","U5"),("U5","U6"),("U4","U7"),("U5","U8"),
-    ("U6","U10"),("U7","U8"),("U8","U9"),("U9","U10"),("U5","U9"),
-    ("R1","R2"),("R2","R3"),("R1","R4"),("R2","R4"),("R3","R6"),
-    ("R4","R5"),("R5","R6"),("R4","R7"),("R5","R10"),("R7","R10"),
-    ("R7","R8"),("R8","R9"),("R6","R9"),("R8","R10"),("R5","R8"),
-    ("U3","R1"),("U10","R4"),("U6","R1"),("U9","R7"),
-]
-# Road distance = 1.15× straight-line
-EDGES = [(a, b, round(_dist(a,b)*1.15, 2)) for a, b in _PAIRS]
-# CO2 cost per edge: urban roads have traffic → higher emissions per km
-# Rural roads: 0.12 kg CO2/km;  Urban roads: 0.21 kg CO2/km
-def _co2(a, b, km):
-    za, zb = NODES[a][2], NODES[b][2]
-    rate = 0.28 if za == "urban" and zb == "urban" else 0.18 if za != zb else 0.10
-    return round(km * rate, 3)
 
-CO2_EDGES = [(a, b, _co2(a, b, w)) for a, b, w in EDGES]
+
 ADJ_KM = {n: [] for n in NODES}
 ADJ_CO2 = {n: [] for n in NODES}
 for i, (a, b, w) in enumerate(EDGES):

task5_forecasting.py

@@ -6,6 +6,7 @@ Run:  python3 task5_forecasting.py
 Out:  forecast.png, residuals.png, feature_importance.png
 """
 
+import os
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -13,6 +14,12 @@ from sklearn.linear_model import LinearRegression
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
 RNG = np.random.default_rng(42)
+CSV_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", "sales_history.csv")
+
+
+def load_sales():
+    """Load the 730-day daily sales dataset from data/sales_history.csv."""
+    return pd.read_csv(CSV_PATH, parse_dates=["date"])
 
 
 # ── 1. Synthetic sales data ────────────────────────────────
@@ -62,7 +69,7 @@ def main():
     print("EcoCart Demand Forecasting — LR vs Random Forest")
     print("="*70)
 
-    df = generate_sales()
+    df = load_sales()
     df = add_features(df)
     split = int(len(df) * 0.8)
     train, test = df.iloc[:split], df.iloc[split:]