Create app.py

app.py

@@ -0,0 +1,139 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import gradio as gr
+from darts import TimeSeries
+from darts.models import TFTModel, NBEATSModel
+from darts.dataprocessing.transformers import Scaler
+from sklearn.preprocessing import LabelEncoder
+import numpy as np
+import io
+
+# ----------------------------
+# Load Dataset
+# ----------------------------
+df = pd.read_csv("dataset.csv")   # <-- replace with your dataset file
+df['datetime'] = pd.to_datetime(df['datetime'])
+df = df.sort_values("datetime")
+
+# Encode weather icons (categorical -> numeric)
+encoder = LabelEncoder()
+df['icon_encoded'] = encoder.fit_transform(df['icon'])
+
+# ----------------------------
+# Preprocessing for Forecasting
+# ----------------------------
+series = TimeSeries.from_dataframe(df, "datetime", "pv_output_kWh")
+scaler = Scaler()
+series_scaled = scaler.fit_transform(series)
+
+# Pre-trained model (you can switch between TFT and N-BEATS)
+model = TFTModel.load_from_checkpoint("tft_pretrained", work_dir="./") \
+        if TFTModel.exists("tft_pretrained", work_dir="./") else None
+
+if model is None:
+    # fallback: train small model (for demo)
+    model = NBEATSModel(input_chunk_length=30, output_chunk_length=7, n_epochs=10)
+    model.fit(series_scaled)
+
+# ----------------------------
+# EDA FUNCTIONS
+# ----------------------------
+def eda_summary():
+    buf = io.StringIO()
+    df.describe().to_string(buf)
+    return buf.getvalue()
+
+def eda_histogram(column):
+    plt.figure(figsize=(6,4))
+    sns.histplot(df[column], kde=True, bins=20)
+    plt.title(f"Distribution of {column}")
+    plt.tight_layout()
+    return plt.gcf()
+
+def eda_correlation():
+    plt.figure(figsize=(8,6))
+    sns.heatmap(df.corr(), annot=True, cmap="coolwarm", fmt=".2f")
+    plt.title("Correlation Heatmap")
+    plt.tight_layout()
+    return plt.gcf()
+
+def eda_timeseries():
+    plt.figure(figsize=(10,4))
+    plt.plot(df["datetime"], df["pv_output_kWh"], label="PV Output (kWh)")
+    plt.title("Time-Series Trend of PV Output")
+    plt.xlabel("Date")
+    plt.ylabel("PV Output (kWh)")
+    plt.legend()
+    plt.tight_layout()
+    return plt.gcf()
+
+# ----------------------------
+# FORECAST FUNCTION
+# ----------------------------
+def forecast_pv(horizon, weather_condition):
+    horizon_map = {"24 Hours": 24, "3 Days": 72, "7 Days": 168, "14 Days": 336}
+    steps = horizon_map[horizon]
+
+    forecast = model.predict(steps)
+    forecast = scaler.inverse_transform(forecast)
+
+    # Adjust for weather condition
+    adjustment = {
+        "Clear": 1.0,
+        "Partly Cloudy": 0.85,
+        "Cloudy": 0.65,
+        "Fog": 0.55,
+        "Smoke/Dust": 0.6,
+        "Winter": 0.7,
+        "Rain": 0.5
+    }
+    adj_factor = adjustment.get(weather_condition, 1.0)
+    forecast_adj = forecast * adj_factor
+
+    # Plot forecast
+    plt.figure(figsize=(10,4))
+    series[-7*24:].plot(label="History")  # last 7 days history
+    forecast.plot(label="Forecast (Base)")
+    forecast_adj.plot(label=f"Forecast (Adjusted for {weather_condition})")
+    plt.legend()
+    plt.title(f"PV Forecast for {horizon}")
+    plt.tight_layout()
+
+    # Peak info
+    peak_time = forecast_adj.time_index[np.argmax(forecast_adj.values())]
+    peak_val = np.max(forecast_adj.values())
+    peak_info = f"🔺 Peak PV Output: {round(peak_val,2)} kWh at {peak_time}"
+
+    return plt.gcf(), peak_info
+
+# ----------------------------
+# GRADIO INTERFACE
+# ----------------------------
+eda_tab = gr.TabbedInterface(
+    [
+        gr.Interface(fn=eda_summary, inputs=[], outputs="text", title="Summary Stats"),
+        gr.Interface(fn=eda_histogram, inputs=gr.Dropdown(df.columns, label="Select Column"), outputs="plot", title="Histogram"),
+        gr.Interface(fn=eda_correlation, inputs=[], outputs="plot", title="Correlation Heatmap"),
+        gr.Interface(fn=eda_timeseries, inputs=[], outputs="plot", title="Time Series Trend")
+    ],
+    tab_names=["Summary", "Histogram", "Correlation", "Time Series"]
+)
+
+forecast_tab = gr.Interface(
+    fn=forecast_pv,
+    inputs=[
+        gr.Radio(["24 Hours", "3 Days", "7 Days", "14 Days"], label="Select Forecast Horizon"),
+        gr.Dropdown(["Clear","Partly Cloudy","Cloudy","Fog","Smoke/Dust","Winter","Rain"], label="Weather Condition")
+    ],
+    outputs=[
+        gr.Plot(label="Forecast Plot"),
+        gr.Textbox(label="Peak Info")
+    ],
+    title="PV Forecasting"
+)
+
+app = gr.TabbedInterface([eda_tab, forecast_tab], tab_names=["EDA Dashboard", "Forecasting"])
+
+if __name__ == "__main__":
+    app.launch()