# mcp_server.py
import sys
import json
import pandas as pd
from parser import parse_ingredients
from spoilage_model import predict_spoilage
from carbon_model import estimate_carbon
from meal_planner import generate_meal_plan

# Load Excel dataset for carbon model once
carbon_df = pd.read_excel("SuEatableLife_Food_Fooprint_database.xlsx", sheet_name=0)
carbon_data = carbon_df.to_dict(orient="records")

def handle_tool_call(tool_name, arguments):
    if tool_name == "parse_ingredients":
        user_input = arguments.get("text", "")
        return parse_ingredients(user_input)

    elif tool_name == "predict_spoilage":
        items = arguments.get("items", [])
        return predict_spoilage(items)

    elif tool_name == "estimate_carbon":
        items = arguments.get("items", [])
        return [estimate_carbon(item, carbon_data) for item in items]

    elif tool_name == "generate_meal_plan":
        items = arguments.get("items", [])
        spoilage = arguments.get("spoilage", [])
        carbon = arguments.get("carbon", [])
        parsed = arguments.get("parsed", [])
        api_key = arguments.get("api_key", []) # api key
        return generate_meal_plan(items, spoilage, carbon, parsed, api_key)

    else:
        return {"error": f"Unknown tool: {tool_name}"}

def main():
    while True:
        line = sys.stdin.readline()
        if not line:
            break
        try:
            request = json.loads(line)
            tool_name = request.get("tool")
            arguments = request.get("input", {})

            result = handle_tool_call(tool_name, arguments)
            response = {
                "id": request.get("id"),
                "output": result,
            }
            print(json.dumps(response), flush=True)

        except Exception as e:
            error_response = {
                "error": str(e)
            }
            print(json.dumps(error_response), flush=True)

if __name__ == "__main__":
    main()