Create app.py

app.py

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+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
+
+# Initialize Groq API
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# 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)
+
+    # Load data from sheets
+    try:
+        historical_data = data.parse("Historical Data")
+        renewable_forecast = data.parse("Renewable Energy Forecast")
+        ev_profiles = data.parse("EV Charging Profiles")
+
+        # Display datasets
+        st.header("Uploaded Data")
+        st.subheader("Historical Data")
+        st.write(historical_data)
+
+        st.subheader("Renewable Energy Forecast")
+        st.write(renewable_forecast)
+
+        st.subheader("EV Charging Profiles")
+        st.write(ev_profiles)
+
+        # 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
+        st.subheader("Groq AI Insights")
+        prompt = st.text_input("Ask Groq for insights (e.g., grid stability)")
+        if prompt:
+            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 parsing file: {e}")
+
+else:
+    st.warning("Please upload a file to proceed.")