Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from keras.models import load_model
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+
+# Load the data and pre-trained model
+data = pd.read_csv('solar_irradiance.csv')
+data['Latitude'] = data['Latitude'].str.rstrip('°').astype(float)
+data['Longitude'] = data['Longitude'].str.rstrip('°').astype(float)
+loaded_model = load_model('solar_irradiance_model.h5')
+
+# Preprocessing
+encoder = OneHotEncoder(sparse=False, categories='auto')
+scaler = StandardScaler()
+
+# Encode categorical and scale numerical features
+encoder.fit(data[['Month', 'Hour']])
+scaler.fit(data[['Latitude', 'Longitude', 'Panel_Capacity(W)', 'Panel_Efficiency', 
+                 'Wind_Speed(km/h)', 'Cloud_Cover(%)', 'temperature (°f)']])
+
+# Helper functions
+def predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature):
+    # Encode and scale features
+    encoded_month_hour = encoder.transform([[month, hour]])
+    scaled_features = scaler.transform([[latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature]])
+    processed_features = np.concatenate((encoded_month_hour, scaled_features), axis=1)
+    reshaped_features = np.reshape(processed_features, (1, 1, processed_features.shape[1]))
+    predicted_irradiance = loaded_model.predict(reshaped_features)
+    return max(predicted_irradiance[0][0], 0.0)
+
+def get_actual_irradiance(month):
+    return data[data['Month'] == month]['Irradiance(W/m^2)'].values
+
+# Streamlit UI
+st.title("Solar Irradiance Prediction App")
+st.sidebar.header("Input Parameters")
+
+# Sidebar Inputs
+month = st.sidebar.selectbox("Month", options=data['Month'].unique())
+hour = st.sidebar.slider("Hour of the Day", min_value=0, max_value=23, value=12)
+latitude = st.sidebar.number_input("Latitude", value=28.570633)
+longitude = st.sidebar.number_input("Longitude", value=77.327215)
+panel_capacity = st.sidebar.number_input("Panel Capacity (W)", value=500)
+panel_efficiency = st.sidebar.number_input("Panel Efficiency", value=0.15)
+wind_speed = st.sidebar.number_input("Wind Speed (km/h)", value=6.43988)
+cloud_cover = st.sidebar.number_input("Cloud Cover (%)", value=17.7)
+temperature = st.sidebar.number_input("Temperature (°F)", value=55)
+
+# Predict and Display
+if st.button("Predict"):
+    predicted = predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature)
+    st.write(f"**Predicted Solar Irradiance:** {predicted:.2f} W/m²")
+
+# Visualization
+st.header(f"Actual vs. Predicted Irradiance for {month}")
+actual_irradiance = get_actual_irradiance(month)
+predicted_irradiances = [predict_irradiance(month, h, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature) for h in range(24)]
+
+fig, ax = plt.subplots(figsize=(10, 6))
+ax.plot(range(24), actual_irradiance, label="Actual Irradiance", marker='o')
+ax.plot(range(24), predicted_irradiances, label="Predicted Irradiance", marker='x')
+ax.set_xlabel("Hour")
+ax.set_ylabel("Irradiance (W/m²)")
+ax.set_title(f"Actual vs. Predicted Irradiance for {month}")
+ax.legend()
+st.pyplot(fig)
+
+# Hour vs. Irradiance
+st.header("Hour vs. Irradiance for All Months")
+hour_range = range(24)
+predicted_irradiances_all = []
+
+for h in hour_range:
+    if h in range(6) or h in range(18, 24):
+        predicted_irradiances_all.append(0)
+    else:
+        predicted_irradiances_all.append(
+            predict_irradiance(month, h, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature)
+        )
+
+fig2, ax2 = plt.subplots(figsize=(10, 6))
+ax2.bar(hour_range, predicted_irradiances_all, label="Predicted Irradiance", alpha=0.7)
+ax2.set_xlabel("Hour")
+ax2.set_ylabel("Irradiance (W/m²)")
+ax2.set_title("Predicted Irradiance Across the Day")
+ax2.legend()
+st.pyplot(fig2)