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# -----------------------------------------------------------
# 🌾 Project Samarth — Logic Engine (Final Polished Version)
# -----------------------------------------------------------
import pandas as pd
import numpy as np
DATA_PATH = "hybrid_dataset/merged_agri_rainfall.csv"
try:
df = pd.read_csv(DATA_PATH)
df.columns = df.columns.str.lower().str.strip()
df["crop_year"] = pd.to_numeric(df.get("crop_year", pd.Series()), errors="coerce")
df["state_name"] = df["state_name"].fillna("").astype(str)
df["crop"] = df["crop"].fillna("").astype(str)
print(f"✅ Dataset loaded successfully → {DATA_PATH} ({len(df)} rows)")
except Exception as e:
print(f"⚠️ Error loading dataset: {e}")
df = pd.DataFrame()
def run_query(parsed_query: dict):
"""Executes logic for a given parsed query using integrated dataset."""
if df.empty:
return {"error": "Dataset not found or empty."}
if not parsed_query or not isinstance(parsed_query, dict):
return {"error": "Invalid query format."}
states = [s.lower().strip() for s in parsed_query.get("states", [])]
crop = parsed_query.get("crop", "").lower().strip()
years = parsed_query.get("years", 5)
metrics = parsed_query.get("metrics", [])
result = {"states": states, "crop": crop, "years": years}
filtered = df.copy()
# ✅ Safely filter by state
if "state_name" in filtered.columns and states:
filtered = filtered[filtered["state_name"].str.lower().isin(states)]
# ✅ Safely filter by crop
if "crop" in filtered.columns and crop:
filtered = filtered[filtered["crop"].str.lower() == crop]
# ✅ Filter by year range
if "crop_year" in filtered.columns and not filtered["crop_year"].isna().all():
latest_year = int(filtered["crop_year"].max())
start_year = latest_year - years + 1
filtered = filtered[
(filtered["crop_year"] >= start_year)
& (filtered["crop_year"] <= latest_year)
]
if filtered.empty:
return {"message": "No matching records found for your query."}
# 🌧️ Compute Average Rainfall
if "rainfall" in metrics:
rain_cols = [c for c in ["annual", "jjas", "jf", "mam", "ond"] if c in filtered.columns]
if rain_cols:
filtered["avg_rainfall"] = filtered[rain_cols].apply(
pd.to_numeric, errors="coerce"
).mean(axis=1)
rainfall_summary = (
filtered.groupby("state_name")["avg_rainfall"]
.mean()
.round(2)
.to_dict()
)
result["rainfall_summary"] = rainfall_summary
# 🌾 Compute Total Crop Production
if "production" in metrics and "production_" in filtered.columns:
prod_summary = (
filtered.groupby("state_name")["production_"]
.sum()
.round(2)
.to_dict()
)
result["production_summary"] = prod_summary
# 📊 If no data found
if "rainfall_summary" not in result and "production_summary" not in result:
result["message"] = "No metrics found in dataset for the given query."
# ✅ Format clean output
result["states"] = sorted([s.title() for s in result.get("states", [])])
return result
# 🧪 Quick test
if __name__ == "__main__":
test_query = {
"states": ["andaman and nicobar islands", "andhra pradesh"],
"crop": "rice",
"years": 5,
"metrics": ["rainfall", "production"],
}
print("\n🧠 Running test query...\n")
print(run_query(test_query))
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