Update app.py

app.py

@@ -1,30 +1,33 @@
 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)
+import matplotlib.pyplot as plt
+
+# Load the trained model
+try:
+    loaded_model = load_model('solar_irradiance_model.h5')
+except ValueError as e:
+    st.error(f"Error loading model: {e}")
 
-# Load the data and pre-trained model
-data = pd.read_csv('Solar_Irradiance.csv')
+# Load the dataset
+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
+# One-hot encoder setup
 encoder = OneHotEncoder(sparse=False, categories='auto')
-scaler = StandardScaler()
+categorical_features = data[['Month', 'Hour']]
+encoder.fit(categorical_features)
 
-# 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)']])
+# Standard scaler setup
+scaler = StandardScaler()
+numerical_features = data[['Latitude', 'Longitude', 'Panel_Capacity(W)', 'Panel_Efficiency', 'Wind_Speed(km/h)', 'Cloud_Cover(%)', 'temperature (°f)']]
+scaler.fit(numerical_features)
 
-# Helper functions
+# Function to predict irradiance
 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)
@@ -32,60 +35,36 @@ def predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_e
     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
+# Streamlit app 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)
+# User inputs
+month = st.sidebar.selectbox("Month", data['Month'].unique())
+hour = st.sidebar.slider("Hour", 0, 23, 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)
+temperature = st.sidebar.number_input("Temperature (°F)", value=55.0)
 
-# Predict and Display
+# 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")
+    predicted_irradiance = predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature)
+    st.write(f"Predicted Solar Irradiance: {predicted_irradiance:.2f} W/m²")
 
 # Visualization
-st.header(f"Actual vs. Predicted Irradiance for {month}")
-actual_irradiance = get_actual_irradiance(month)
+st.subheader("Actual vs. Predicted Irradiance")
+actual_irradiance = data[data['Month'] == month]['Irradiance(W/m^2)'].values
 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)
+plt.figure(figsize=(10, 5))
+plt.plot(range(24), actual_irradiance, label='Actual Irradiance')
+plt.plot(range(24), predicted_irradiances, label='Predicted Irradiance')
+plt.xlabel('Hour')
+plt.ylabel('Irradiance (W/m²)')
+plt.title(f'Actual vs. Predicted Irradiance for {month}')
+plt.legend()
+st.pyplot(plt)