app.py

import streamlit as st
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import faiss
from groq import Groq
import os
from dotenv import load_dotenv  # Import dotenv

# Load environment variables from .env file
load_dotenv()

# Fetch the API key from the environment variable
api_key = os.getenv("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")

# Debugging line to verify the API key is loaded
st.write("Loaded API Key:", api_key)

if api_key:
    st.success("Groq API key loaded successfully.")
    client = Groq(api_key=api_key)
else:
    st.warning("Groq API key is not set. Features requiring Groq API will be disabled.")
    client = None

# App title
st.title("Optimized EV Charging Scheduling in Microgrids")

# Upload Excel file
st.sidebar.header("Upload Data File")
uploaded_file = st.sidebar.file_uploader("Upload Excel File", type=["xlsx", "xls"])

if uploaded_file:
    data = pd.ExcelFile(uploaded_file)

    try:
        # Load data from sheets
        historical_data = data.parse("Historical Data")
        renewable_forecast = data.parse("Renewable Energy Forecast")
        ev_profiles = data.parse("EV Charging Profiles")

        # Standardize column names
        historical_data.columns = historical_data.columns.str.strip()
        renewable_forecast.columns = renewable_forecast.columns.str.strip()
        ev_profiles.columns = ev_profiles.columns.str.strip()

        # Rename specific columns if necessary
        historical_data.rename(columns={"DATE": "Date", "date": "Date"}, inplace=True)
        renewable_forecast.rename(columns={"DATE": "Date", "date": "Date"}, inplace=True)
        ev_profiles.rename(columns={"DATE": "Date", "date": "Date"}, inplace=True)

        # Parameters
        rated_grid_capacity = 3500  # kW
        disconnect_threshold = 3200  # kW

        # Generate optimized schedule
        def optimize_schedule(historical, renewable, profiles):
            combined_data = pd.merge(
                historical, renewable, on=["Date", "Time"], how="inner"
            )
            combined_data = pd.merge(
                combined_data, profiles, on=["Date"], how="inner"
            )

            # Initialize schedule
            schedule = []
            total_load = 0

            for _, row in combined_data.iterrows():
                ev_load = row["State of Charge (kWh)"]
                solar_load = row["Solar Load (kW)"]
                total_load += ev_load - solar_load

                if total_load > disconnect_threshold:
                    schedule.append("DISCONNECT")
                elif total_load > rated_grid_capacity:
                    schedule.append("V2G ENABLED")
                else:
                    schedule.append("ALLOW")

            combined_data["Charging Action"] = schedule
            return combined_data

        # Generate schedule
        st.subheader("Optimized EV Charging Schedule")
        schedule_df = optimize_schedule(historical_data, renewable_forecast, ev_profiles)
        st.write(schedule_df)

        # Download optimized schedule
        st.download_button(
            "Download Schedule",
            data=schedule_df.to_csv(index=False),
            file_name="optimized_schedule.csv",
            mime="text/csv",
        )

        # Simulate Groq API
        if client:
            st.subheader("Groq AI Insights")
            prompt = st.text_input("Ask Groq for insights (e.g., grid stability)")
            if prompt:
                try:
                    response = client.chat.completions.create(
                        messages=[{"role": "user", "content": prompt}],
                        model="llama-3.3-70b-versatile",
                    )
                    st.write(response.choices[0].message.content)
                except Exception as e:
                    st.error(f"Error communicating with Groq API: {e}")
        else:
            st.warning("Groq API key is missing. Insights feature is disabled.")

    except Exception as e:
        st.error(f"Error parsing file: {e}")

else:
    st.warning("Please upload a file to proceed.")