new lights

.gitattributes

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+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,12 +1,12 @@
----
-title: Solar Savings
-emoji: ⚡
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Solar Savings
+emoji: ⚡
+colorFrom: yellow
+colorTo: red
+sdk: streamlit
+sdk_version: 1.44.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,332 +1,331 @@
-import streamlit as st
-import polars as pl
-import xarray as xr
-import seaborn as sns
-import matplotlib.pyplot as plt
-import numpy as np
-from typing import Dict, List, Tuple
-
-# Constants
-GRID_PRICE = 28.44  # Ksh/kWh
-TIME_RESOLUTION = 24  # hours in a day
-
-# Appliance data structure
-APPLIANCES = {
-    "Lights": {"power_kw": 0.006 * 5, "usage_hours": 5, "grid_only": False},
-    "TV": {"power_kw": 0.08, "usage_hours": 5, "grid_only": False},
-    "Fridge": {"power_kw": 0.8, "usage_hours": 24, "grid_only": False},
-    "Computer": {"power_kw": 0.15, "usage_hours": 9, "grid_only": False},
-    # "Water Pump": {"power_kw": 0.5, "usage_hours": 2, "grid_only": False},
-    "Instant Shower": {"power_kw": 5.0, "usage_hours": 0.5, "grid_only": True},
-    "Electric Kettle": {"power_kw": 1.5, "usage_hours": 0.25, "grid_only": True},
-    "Microwave": {"power_kw": 2.0, "usage_hours": 0.2, "grid_only": True},
-    # "Oven": {"power_kw": 1.0, "usage_hours": 0.3, "grid_only": True},
-}
-
-
-def initialize_session_state():
-    """Initialize all session state variables"""
-    if "solar_price" not in st.session_state:
-        st.session_state.solar_price = 10.0
-    if "solar_ratio" not in st.session_state:
-        st.session_state.solar_ratio = 0.5
-    if "appliance_data" not in st.session_state:
-        st.session_state.appliance_data = None
-
-
-def create_appliance_dataframe() -> pl.DataFrame:
-    """Create a Polars DataFrame from the appliance data"""
-    data = []
-    for name, specs in APPLIANCES.items():
-        data.append(
-            {
-                "appliance": name,
-                "power_kw": specs["power_kw"],
-                "usage_hours": specs["usage_hours"],
-                "grid_only": specs["grid_only"],
-                "daily_kwh": specs["power_kw"] * specs["usage_hours"],
-            }
-        )
-    return pl.DataFrame(data)
-
-
-def create_time_series_dataset(df: pl.DataFrame) -> xr.Dataset:
-    """Create an xarray Dataset with time series data"""
-    hours = np.arange(TIME_RESOLUTION)
-    appliances = df["appliance"].to_list()
-
-    # Create empty arrays
-    power_usage = xr.DataArray(
-        np.zeros((len(appliances), TIME_RESOLUTION)),
-        dims=("appliance", "hour"),
-        coords={"appliance": appliances, "hour": hours},
-    )
-
-    # Fill with actual usage patterns (simplified - could be enhanced)
-    for i, row in enumerate(df.iter_rows(named=True)):
-        if row["usage_hours"] > 0:
-            usage_start = 8  # assuming morning start
-            usage_end = min(usage_start + int(row["usage_hours"]), TIME_RESOLUTION)
-            power_usage[i, usage_start:usage_end] = row["power_kw"]
-
-    return xr.Dataset(
-        {
-            "power_usage": power_usage,
-            "daily_kwh": xr.DataArray(df["daily_kwh"].to_numpy(), dims="appliance"),
-            "grid_only": xr.DataArray(df["grid_only"].to_numpy(), dims="appliance"),
-        }
-    )
-
-
-def calculate_consumption(ds: xr.Dataset, solar_ratio: float) -> Dict:
-    """Calculate consumption breakdown and costs"""
-    # Separate grid-only and mixed appliances
-    mixed_mask = ~ds["grid_only"]
-    grid_only_mask = ds["grid_only"]
-
-    # Calculate consumptions
-    grid_only_consumption = ds["daily_kwh"].where(grid_only_mask, 0).sum().item()
-    total_mixed_consumption = ds["daily_kwh"].where(mixed_mask, 0).sum().item()
-
-    solar_consumption = total_mixed_consumption * solar_ratio
-    grid_mixed_consumption = total_mixed_consumption * (1 - solar_ratio)
-    total_grid_consumption = grid_mixed_consumption + grid_only_consumption
-
-    # Calculate costs
-    grid_cost = total_grid_consumption * GRID_PRICE
-    solar_cost = solar_consumption * st.session_state.solar_price
-    total_cost = grid_cost + solar_cost
-
-    return {
-        "solar_consumption": solar_consumption,
-        "grid_consumption": total_grid_consumption,
-        "grid_only_consumption": grid_only_consumption,
-        "total_consumption": total_mixed_consumption + grid_only_consumption,
-        "grid_cost": grid_cost,
-        "solar_cost": solar_cost,
-        "total_cost": total_cost,
-        "hourly_power": ds["power_usage"],
-    }
-
-
-def find_optimal_solar_price(solar_ratio: float, years: int = 5) -> float:
-    """Calculate optimal solar price considering payback period"""
-    # Simplified model - in reality would need installation costs, etc.
-    # Assumes you want payback within 'years' years
-    daily_savings = GRID_PRICE - (GRID_PRICE * 0.8)  # 20% savings target
-    return max(0, GRID_PRICE - (daily_savings / years))
-
-
-def plot_consumption_breakdown(data: Dict):
-    """Create consumption breakdown visualization"""
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
-
-    # Pie chart
-    labels = ["Solar", "Grid (mixed)", "Grid (high load)"]
-    sizes = [
-        data["solar_consumption"],
-        data["grid_consumption"] - data["grid_only_consumption"],
-        data["grid_only_consumption"],
-    ]
-    ax1.pie(sizes, labels=labels, autopct="%1.1f%%", startangle=90)
-    ax1.set_title("Energy Source Breakdown")
-
-    # Cost comparison bar plot
-    cost_df = pl.DataFrame(
-        {
-            "source": ["Solar", "Grid", "Total"],
-            "cost": [data["solar_cost"], data["grid_cost"], data["total_cost"]],
-        }
-    )
-    sns.barplot(data=cost_df.to_pandas(), x="source", y="cost", ax=ax2)
-    ax2.set_title("Daily Cost Comparison")
-    ax2.set_ylabel("Cost (Ksh)")
-
-    st.pyplot(fig)
-
-
-def plot_hourly_usage(hourly_data: xr.DataArray):
-    """Plot hourly power usage patterns"""
-    fig, ax = plt.subplots(figsize=(10, 5))
-
-    # Convert to DataFrame for Seaborn
-    df = hourly_data.to_dataframe(name="power_kw").reset_index()
-
-    # Plot grid-only vs solar-capable appliances separately
-    grid_only_mask = df["appliance"].isin(
-        [name for name, specs in APPLIANCES.items() if specs["grid_only"]]
-    )
-
-    sns.lineplot(
-        data=df[~grid_only_mask],
-        x="hour",
-        y="power_kw",
-        hue="appliance",
-        ax=ax,
-        palette="crest",
-        legend=True,
-    )
-
-    sns.lineplot(
-        data=df[grid_only_mask],
-        x="hour",
-        y="power_kw",
-        hue="appliance",
-        ax=ax,
-        palette="flare",
-        linestyle="--",
-        legend=True,
-    )
-
-    ax.set_title("Hourly Power Consumption Patterns")
-    ax.set_xlabel("Hour of Day")
-    ax.set_ylabel("Power (kW)")
-    ax.legend(title="Appliance", bbox_to_anchor=(1.05, 1), loc="upper left")
-
-    st.pyplot(fig)
-
-
-def main():
-    st.set_page_config(
-        page_title="Solar vs Grid Consumption Analyzer", page_icon="☀️", layout="wide"
-    )
-
-    # Initialize session state and data structures
-    initialize_session_state()
-    appliance_df = create_appliance_dataframe()
-    ds = create_time_series_dataset(appliance_df)
-
-    # Main title and description
-    st.title("☀️ Solar vs National Grid Consumption Analyzer")
-    st.markdown("""
-    Analyze the financial impact of different solar/grid consumption ratios 
-    and optimize your energy costs.
-    """)
-
-    # Sidebar for inputs
-    with st.sidebar:
-        st.header("Configuration")
-        st.session_state.solar_ratio = st.slider(
-            "Solar/Grid Consumption Ratio",
-            min_value=0.0,
-            max_value=1.0,
-            value=0.5,
-            step=0.1,
-            format="%.1f",
-        )
-
-        st.session_state.solar_price = st.number_input(
-            "Custom Solar Price (Ksh/kWh)",
-            min_value=0.0,
-            max_value=float(GRID_PRICE),
-            value=10.0,
-            step=0.1,
-        )
-
-        st.markdown("---")
-        st.metric("Current Grid Price", f"{GRID_PRICE} Ksh/kWh")
-
-    # Calculate consumption and costs
-    data = calculate_consumption(ds, st.session_state.solar_ratio)
-    optimal_price = find_optimal_solar_price(st.session_state.solar_ratio)
-
-    # Main content columns
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        st.metric("Total Daily Consumption", f"{data['total_consumption']:.2f} kWh")
-    with col2:
-        st.metric("Your Solar Price", f"{st.session_state.solar_price} Ksh/kWh")
-    with col3:
-        st.metric("Suggested Optimal Price", f"{optimal_price:.2f} Ksh/kWh")
-
-    # Visualization section
-    st.markdown("---")
-    st.subheader("Consumption Analysis")
-    plot_consumption_breakdown(data)
-
-    st.subheader("Hourly Usage Patterns")
-    plot_hourly_usage(data["hourly_power"])
-
-    # Financial analysis
-    st.subheader("Financial Impact")
-    savings = (GRID_PRICE * data["total_consumption"]) - data["total_cost"]
-    col1, col2 = st.columns(2)
-    with col1:
-        st.metric("Daily Savings vs All-Grid", f"{savings:.2f} Ksh")
-        st.metric("Annual Savings Potential", f"{savings * 365:.2f} Ksh")
-    with col2:
-        st.metric(
-            "Grid Dependency",
-            f"{(data['grid_consumption'] / data['total_consumption']) * 100:.1f}%",
-        )
-        st.metric(
-            "Solar Viability",
-            f"{(data['solar_consumption'] / data['total_consumption']) * 100:.1f}%",
-        )
-
-    # Appliance details table
-    st.subheader("Appliance Details")
-    st.dataframe(
-        appliance_df.select(
-            [
-                pl.col("appliance"),
-                pl.col("power_kw").alias("Power (kW)"),
-                pl.col("usage_hours").alias("Usage (hrs)"),
-                pl.col("daily_kwh").alias("Daily (kWh)"),
-                pl.col("grid_only").alias("Grid Only"),
-            ]
-        ),
-        use_container_width=True,
-    )
-
-    # Grid-only appliances warning
-    grid_only_appliances = [
-        name for name, specs in APPLIANCES.items() if specs["grid_only"]
-    ]
-    st.warning(f"""
-    **Grid-Only Appliances:** These high-load devices cannot be solar-powered:
-    {", ".join(grid_only_appliances)}.
-    They represent fixed grid costs of {data["grid_only_consumption"]:.2f} kWh/day.
-    """)
-
-    # Advanced analysis expander
-    with st.expander("Advanced Analysis"):
-        st.write("""
-        ### Detailed Financial Modeling
-        
-        For a more accurate financial analysis, consider:
-        - Solar installation costs
-        - Maintenance expenses
-        - Battery storage requirements
-        - Grid feed-in tariffs
-        - Equipment degradation over time
-        """)
-
-        # Sensitivity analysis
-        st.write("### Price Sensitivity Analysis")
-        solar_prices = np.linspace(0, GRID_PRICE, 20)
-        costs = []
-        for price in solar_prices:
-            temp_data = calculate_consumption(ds, st.session_state.solar_ratio)
-            temp_data["solar_price"] = price
-            costs.append(temp_data["total_cost"])
-
-        fig, ax = plt.subplots()
-        sns.lineplot(x=solar_prices, y=costs, ax=ax)
-        ax.set_xlabel("Solar Price (Ksh/kWh)")
-        ax.set_ylabel("Total Daily Cost (Ksh)")
-        ax.axvline(x=optimal_price, color="r", linestyle="--", label="Optimal Price")
-        ax.legend()
-        st.pyplot(fig)
-
-    # Footer
-    st.markdown("---")
-    st.caption("""
-    *Note: This analysis provides estimates only. Consult with a solar energy professional 
-    for accurate system sizing and financial projections.*
-    """)
-
-
-if __name__ == "__main__":
-    main()
-
+import streamlit as st
+import polars as pl
+import xarray as xr
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+from typing import Dict, List, Tuple
+
+# Constants
+GRID_PRICE = 28.44  # Ksh/kWh
+TIME_RESOLUTION = 24  # hours in a day
+
+# Appliance data structure
+APPLIANCES = {
+    "Lights": {"power_kw": 2088, "usage_hours": 16, "grid_only": False},
+    "TV": {"power_kw": 0.08, "usage_hours": 5, "grid_only": False},
+    "Fridge": {"power_kw": 0.8, "usage_hours": 24, "grid_only": False},
+    "Computer": {"power_kw": 0.15, "usage_hours": 9, "grid_only": False},
+    # "Water Pump": {"power_kw": 0.5, "usage_hours": 2, "grid_only": False},
+    "Instant Shower": {"power_kw": 5.0, "usage_hours": 0.5, "grid_only": True},
+    "Electric Kettle": {"power_kw": 1.5, "usage_hours": 0.25, "grid_only": True},
+    "Microwave": {"power_kw": 2.0, "usage_hours": 0.2, "grid_only": True},
+    # "Oven": {"power_kw": 1.0, "usage_hours": 0.3, "grid_only": True},
+}
+
+
+def initialize_session_state():
+    """Initialize all session state variables"""
+    if "solar_price" not in st.session_state:
+        st.session_state.solar_price = 10.0
+    if "solar_ratio" not in st.session_state:
+        st.session_state.solar_ratio = 0.5
+    if "appliance_data" not in st.session_state:
+        st.session_state.appliance_data = None
+
+
+def create_appliance_dataframe() -> pl.DataFrame:
+    """Create a Polars DataFrame from the appliance data"""
+    data = []
+    for name, specs in APPLIANCES.items():
+        data.append(
+            {
+                "appliance": name,
+                "power_kw": specs["power_kw"],
+                "usage_hours": specs["usage_hours"],
+                "grid_only": specs["grid_only"],
+                "daily_kwh": specs["power_kw"] * specs["usage_hours"],
+            }
+        )
+    return pl.DataFrame(data)
+
+
+def create_time_series_dataset(df: pl.DataFrame) -> xr.Dataset:
+    """Create an xarray Dataset with time series data"""
+    hours = np.arange(TIME_RESOLUTION)
+    appliances = df["appliance"].to_list()
+
+    # Create empty arrays
+    power_usage = xr.DataArray(
+        np.zeros((len(appliances), TIME_RESOLUTION)),
+        dims=("appliance", "hour"),
+        coords={"appliance": appliances, "hour": hours},
+    )
+
+    # Fill with actual usage patterns (simplified - could be enhanced)
+    for i, row in enumerate(df.iter_rows(named=True)):
+        if row["usage_hours"] > 0:
+            usage_start = 8  # assuming morning start
+            usage_end = min(usage_start + int(row["usage_hours"]), TIME_RESOLUTION)
+            power_usage[i, usage_start:usage_end] = row["power_kw"]
+
+    return xr.Dataset(
+        {
+            "power_usage": power_usage,
+            "daily_kwh": xr.DataArray(df["daily_kwh"].to_numpy(), dims="appliance"),
+            "grid_only": xr.DataArray(df["grid_only"].to_numpy(), dims="appliance"),
+        }
+    )
+
+
+def calculate_consumption(ds: xr.Dataset, solar_ratio: float) -> Dict:
+    """Calculate consumption breakdown and costs"""
+    # Separate grid-only and mixed appliances
+    mixed_mask = ~ds["grid_only"]
+    grid_only_mask = ds["grid_only"]
+
+    # Calculate consumptions
+    grid_only_consumption = ds["daily_kwh"].where(grid_only_mask, 0).sum().item()
+    total_mixed_consumption = ds["daily_kwh"].where(mixed_mask, 0).sum().item()
+
+    solar_consumption = total_mixed_consumption * solar_ratio
+    grid_mixed_consumption = total_mixed_consumption * (1 - solar_ratio)
+    total_grid_consumption = grid_mixed_consumption + grid_only_consumption
+
+    # Calculate costs
+    grid_cost = total_grid_consumption * GRID_PRICE
+    solar_cost = solar_consumption * st.session_state.solar_price
+    total_cost = grid_cost + solar_cost
+
+    return {
+        "solar_consumption": solar_consumption,
+        "grid_consumption": total_grid_consumption,
+        "grid_only_consumption": grid_only_consumption,
+        "total_consumption": total_mixed_consumption + grid_only_consumption,
+        "grid_cost": grid_cost,
+        "solar_cost": solar_cost,
+        "total_cost": total_cost,
+        "hourly_power": ds["power_usage"],
+    }
+
+
+def find_optimal_solar_price(solar_ratio: float, years: int = 5) -> float:
+    """Calculate optimal solar price considering payback period"""
+    # Simplified model - in reality would need installation costs, etc.
+    # Assumes you want payback within 'years' years
+    daily_savings = GRID_PRICE - (GRID_PRICE * 0.8)  # 20% savings target
+    return max(0, GRID_PRICE - (daily_savings / years))
+
+
+def plot_consumption_breakdown(data: Dict):
+    """Create consumption breakdown visualization"""
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
+
+    # Pie chart
+    labels = ["Solar", "Grid (mixed)", "Grid (high load)"]
+    sizes = [
+        data["solar_consumption"],
+        data["grid_consumption"] - data["grid_only_consumption"],
+        data["grid_only_consumption"],
+    ]
+    ax1.pie(sizes, labels=labels, autopct="%1.1f%%", startangle=90)
+    ax1.set_title("Energy Source Breakdown")
+
+    # Cost comparison bar plot
+    cost_df = pl.DataFrame(
+        {
+            "source": ["Solar", "Grid", "Total"],
+            "cost": [data["solar_cost"], data["grid_cost"], data["total_cost"]],
+        }
+    )
+    sns.barplot(data=cost_df.to_pandas(), x="source", y="cost", ax=ax2)
+    ax2.set_title("Daily Cost Comparison")
+    ax2.set_ylabel("Cost (Ksh)")
+
+    st.pyplot(fig)
+
+
+def plot_hourly_usage(hourly_data: xr.DataArray):
+    """Plot hourly power usage patterns"""
+    fig, ax = plt.subplots(figsize=(10, 5))
+
+    # Convert to DataFrame for Seaborn
+    df = hourly_data.to_dataframe(name="power_kw").reset_index()
+
+    # Plot grid-only vs solar-capable appliances separately
+    grid_only_mask = df["appliance"].isin(
+        [name for name, specs in APPLIANCES.items() if specs["grid_only"]]
+    )
+
+    sns.lineplot(
+        data=df[~grid_only_mask],
+        x="hour",
+        y="power_kw",
+        hue="appliance",
+        ax=ax,
+        palette="crest",
+        legend=True,
+    )
+
+    sns.lineplot(
+        data=df[grid_only_mask],
+        x="hour",
+        y="power_kw",
+        hue="appliance",
+        ax=ax,
+        palette="flare",
+        linestyle="--",
+        legend=True,
+    )
+
+    ax.set_title("Hourly Power Consumption Patterns")
+    ax.set_xlabel("Hour of Day")
+    ax.set_ylabel("Power (kW)")
+    ax.legend(title="Appliance", bbox_to_anchor=(1.05, 1), loc="upper left")
+
+    st.pyplot(fig)
+
+
+def main():
+    st.set_page_config(
+        page_title="Solar vs Grid Consumption Analyzer", page_icon="☀️", layout="wide"
+    )
+
+    # Initialize session state and data structures
+    initialize_session_state()
+    appliance_df = create_appliance_dataframe()
+    ds = create_time_series_dataset(appliance_df)
+
+    # Main title and description
+    st.title("☀️ Solar vs National Grid Consumption Analyzer")
+    st.markdown("""
+    Analyze the financial impact of different solar/grid consumption ratios 
+    and optimize your energy costs.
+    """)
+
+    # Sidebar for inputs
+    with st.sidebar:
+        st.header("Configuration")
+        st.session_state.solar_ratio = st.slider(
+            "Solar/Grid Consumption Ratio",
+            min_value=0.0,
+            max_value=1.0,
+            value=0.5,
+            step=0.1,
+            format="%.1f",
+        )
+
+        st.session_state.solar_price = st.number_input(
+            "Custom Solar Price (Ksh/kWh)",
+            min_value=0.0,
+            max_value=float(GRID_PRICE),
+            value=10.0,
+            step=0.1,
+        )
+
+        st.markdown("---")
+        st.metric("Current Grid Price", f"{GRID_PRICE} Ksh/kWh")
+
+    # Calculate consumption and costs
+    data = calculate_consumption(ds, st.session_state.solar_ratio)
+    optimal_price = find_optimal_solar_price(st.session_state.solar_ratio)
+
+    # Main content columns
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.metric("Total Daily Consumption", f"{data['total_consumption']:.2f} kWh")
+    with col2:
+        st.metric("Your Solar Price", f"{st.session_state.solar_price} Ksh/kWh")
+    with col3:
+        st.metric("Suggested Optimal Price", f"{optimal_price:.2f} Ksh/kWh")
+
+    # Visualization section
+    st.markdown("---")
+    st.subheader("Consumption Analysis")
+    plot_consumption_breakdown(data)
+
+    st.subheader("Hourly Usage Patterns")
+    plot_hourly_usage(data["hourly_power"])
+
+    # Financial analysis
+    st.subheader("Financial Impact")
+    savings = (GRID_PRICE * data["total_consumption"]) - data["total_cost"]
+    col1, col2 = st.columns(2)
+    with col1:
+        st.metric("Daily Savings vs All-Grid", f"{savings:.2f} Ksh")
+        st.metric("Annual Savings Potential", f"{savings * 365:.2f} Ksh")
+    with col2:
+        st.metric(
+            "Grid Dependency",
+            f"{(data['grid_consumption'] / data['total_consumption']) * 100:.1f}%",
+        )
+        st.metric(
+            "Solar Viability",
+            f"{(data['solar_consumption'] / data['total_consumption']) * 100:.1f}%",
+        )
+
+    # Appliance details table
+    st.subheader("Appliance Details")
+    st.dataframe(
+        appliance_df.select(
+            [
+                pl.col("appliance"),
+                pl.col("power_kw").alias("Power (kW)"),
+                pl.col("usage_hours").alias("Usage (hrs)"),
+                pl.col("daily_kwh").alias("Daily (kWh)"),
+                pl.col("grid_only").alias("Grid Only"),
+            ]
+        ),
+        use_container_width=True,
+    )
+
+    # Grid-only appliances warning
+    grid_only_appliances = [
+        name for name, specs in APPLIANCES.items() if specs["grid_only"]
+    ]
+    st.warning(f"""
+    **Grid-Only Appliances:** These high-load devices cannot be solar-powered:
+    {", ".join(grid_only_appliances)}.
+    They represent fixed grid costs of {data["grid_only_consumption"]:.2f} kWh/day.
+    """)
+
+    # Advanced analysis expander
+    with st.expander("Advanced Analysis"):
+        st.write("""
+        ### Detailed Financial Modeling
+        
+        For a more accurate financial analysis, consider:
+        - Solar installation costs
+        - Maintenance expenses
+        - Battery storage requirements
+        - Grid feed-in tariffs
+        - Equipment degradation over time
+        """)
+
+        # Sensitivity analysis
+        st.write("### Price Sensitivity Analysis")
+        solar_prices = np.linspace(0, GRID_PRICE, 20)
+        costs = []
+        for price in solar_prices:
+            temp_data = calculate_consumption(ds, st.session_state.solar_ratio)
+            temp_data["solar_price"] = price
+            costs.append(temp_data["total_cost"])
+
+        fig, ax = plt.subplots()
+        sns.lineplot(x=solar_prices, y=costs, ax=ax)
+        ax.set_xlabel("Solar Price (Ksh/kWh)")
+        ax.set_ylabel("Total Daily Cost (Ksh)")
+        ax.axvline(x=optimal_price, color="r", linestyle="--", label="Optimal Price")
+        ax.legend()
+        st.pyplot(fig)
+
+    # Footer
+    st.markdown("---")
+    st.caption("""
+    *Note: This analysis provides estimates only. Consult with a solar energy professional 
+    for accurate system sizing and financial projections.*
+    """)
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -1,6 +1,6 @@
-streamlit
-polars
-xarray
-seaborn
-matplotlib
+streamlit
+polars
+xarray
+seaborn
+matplotlib
 numpy