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app.py

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+import streamlit as st
+
+# Set Streamlit page configuration
+st.set_page_config(page_title="Solar Forecast", layout="wide")
+
+st.sidebar.title("🔍 Navigation")
+page = st.sidebar.radio("Go to", ["Home", "LSTM Model", "XGBoost Model", "Random Forest", "Linear Regression", "GitHub", "About"])
+
+# Redirect to the selected page
+if page == "Home":
+    from pages import home
+    home.show()
+elif page == "LSTM Model":
+    from pages import lstm
+    lstm.show()
+elif page == "XGBoost Model":
+    from pages import xgboost
+    xgboost.show()
+elif page == "Random Forest":
+    from pages import random_forest
+    random_forest.show()
+elif page == "Linear Regression":
+    from pages import linear_regression
+    linear_regression.show()
+elif page == "GitHub":
+    from pages import github
+    github.show()
+elif page == "About":
+    from pages import about
+    about.show()

pages/about.py

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+import streamlit as st
+
+st.title("ℹ About This Project")
+
+st.write("""
+This application is designed for **solar power prediction** using multiple machine learning models. 
+The models included in this app are:
+- 🔮 **LSTM (Long Short-Term Memory)**
+- 🚀 **XGBoost**
+- 🌳 **Random Forest**
+- 📉 **Linear Regression**
+
+### 🚀 Features:
+✅ Upload and process CSV data  
+✅ Interactive navigation menu  
+✅ Adjustable model parameters  
+✅ Progress bar and logs during training  
+
+### 👨‍💻 Developer:
+- **Your Name**
+- [GitHub Profile](https://github.com/Sivatech24)
+- [LinkedIn Profile](#) (Add your actual LinkedIn link)
+
+Thank you for using this app! 🎉
+""")

pages/github.py

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+import streamlit as st
+
+st.title("🔗 GitHub Repository")
+
+st.write("You can find the complete source code for this project on GitHub.")
+
+# Replace with your actual GitHub link
+github_url = "https://github.com/Sivatech24/Streamlit"
+
+st.markdown(f"[📂 View Repository]({github_url})")
+
+st.write("If you like this project, consider giving it a ⭐ on GitHub!")

pages/home.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+# from statsmodels.tsa.arima.model import ARIMA
+# from statsmodels.tsa.stattools import adfuller
+# from statsmodels.tsa.statespace.sarimax import SARIMAX
+# from sklearn.model_selection import train_test_split
+import matplotlib.image as mpimg
+import seaborn as sns
+import warnings
+import datetime as dt
+from sklearn.metrics import confusion_matrix
+import matplotlib.dates as mdates
+from pandas.tseries.offsets import DateOffset
+import streamlit as st
+# from pmdarima.arima import auto_arima
+# from statsmodels.tsa.stattools import adfuller
+# import streamlit as st
+# import pandas as pd
+# import matplotlib.pyplot as plt
+# from statsmodels.tsa.stattools import adfuller
+# from pmdarima import auto_arima
+# from pandas.tseries.offsets import DateOffset
+# from prophet import Prophet
+warnings.filterwarnings('ignore')
+
+
+# header
+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import xgboost as xgb
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LinearRegression
+
+# --- Sidebar Navigation ---
+# st.sidebar.title("📌 Navigation")
+# st.sidebar.page_link("app.py", label="🏠 Home", icon="🏠")
+# st.sidebar.page_link("pages/lstm.py", label="🔮 LSTM", icon="📈")
+# st.sidebar.page_link("pages/xgboost.py", label="🚀 XGBoost", icon="📊")
+# st.sidebar.page_link("pages/random_forest.py", label="🌳 Random Forest", icon="🌲")
+# st.sidebar.page_link("pages/linear_regression.py", label="📉 Linear Regression", icon="📏")
+# st.sidebar.page_link("pages/github.py", label="🔗 GitHub", icon="🔗")
+# st.sidebar.page_link("pages/about.py", label="ℹ About", icon="ℹ️")
+
+"""# Load Generation Data (Plant 1)"""
+
+from sklearn.model_selection import train_test_split
+import warnings
+warnings.filterwarnings('ignore')
+
+st.title("Solar Plant Data Analysis and Forecasting")
+
+# File Upload
+uploaded_gen = st.file_uploader("Upload Generation Data CSV", type=["csv"], key="gen")
+uploaded_weather = st.file_uploader("Upload Weather Sensor Data CSV", type=["csv"], key="weather")
+
+def load_data(file):
+    if file is not None:
+        return pd.read_csv(file)
+    return None
+
+# Load Data
+gen_data = load_data(uploaded_gen)
+weather_data = load_data(uploaded_weather)
+
+default_gen_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Generation_Data.csv')
+default_weather_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Weather_Sensor_Data.csv')
+
+if gen_data is None:
+    gen_data = default_gen_data
+    gen_1 = default_gen_data
+if weather_data is None:
+    weather_data = default_weather_data
+    sens_1 = default_weather_data
+
+# Data Preview
+st.subheader("Generation Data Preview Head")
+st.dataframe(gen_data.head())
+
+st.subheader("Weather Data Preview Head")
+st.dataframe(weather_data.head())
+
+st.subheader("Generation Data Preview Tail")
+st.dataframe(gen_data.tail())
+
+st.subheader("Weather Data Preview Tail")
+st.dataframe(weather_data.tail())
+
+st.subheader("Generation Data Describe")
+st.dataframe(gen_data.describe())
+
+st.subheader("Weather Data Describe")
+st.dataframe(weather_data.describe())
+
+# Filter out non-numeric columns
+numeric_data = gen_1.select_dtypes(include=['float64', 'int64'])
+
+# Calculate the correlation matrix on the numeric data
+corelation = numeric_data.corr()
+
+# Plot the heatmap
+fig, ax = plt.subplots(figsize=(14, 12))
+sns.heatmap(corelation, annot=True, ax=ax)
+st.subheader("Generation Data HeatMap")
+st.pyplot(fig)
+
+# Filter out non-numeric columns
+numeric_data = sens_1.select_dtypes(include=['float64', 'int64'])
+
+# Calculate the correlation matrix on the numeric data
+corelation = numeric_data.corr()
+
+# Plot the heatmap
+fig, ax = plt.subplots(figsize=(14, 12))
+sns.heatmap(corelation, annot=True, ax=ax)
+st.subheader("Weather Data HeatMap")
+st.pyplot(fig)
+
+
+
+
+st.subheader("Datetime Conversion and Resampling")
+
+# Convert DATE_TIME column to datetime format
+gen_data['DATE_TIME'] = pd.to_datetime(gen_data['DATE_TIME'], format='%d-%m-%Y %H:%M')
+weather_data['DATE_TIME'] = pd.to_datetime(weather_data['DATE_TIME'], format='%Y-%m-%d %H:%M:%S')
+gen_1['DATE_TIME'] = pd.to_datetime(gen_1['DATE_TIME'], format='%d-%m-%Y %H:%M')
+sens_1['DATE_TIME'] = pd.to_datetime(sens_1['DATE_TIME'], format='%Y-%m-%d %H:%M:%S')
+
+# Resample generation data daily
+gen_data_daily = gen_data.set_index('DATE_TIME').resample('D').sum().reset_index()
+
+# Display processed data
+st.subheader("Daily Aggregated Generation Data")
+st.dataframe(gen_data_daily.head())
+
+
+
+
+st.subheader("Daily Yield and Power Analysis")
+
+# Group by DATE_TIME and sum values
+df_gen = gen_1.groupby('DATE_TIME').sum().reset_index()
+df_gen['time'] = df_gen['DATE_TIME'].dt.time
+
+# Create subplots
+fig, ax = plt.subplots(ncols=2, nrows=1, dpi=100, figsize=(20, 5))
+
+# Daily yield plot
+df_gen.plot(x='DATE_TIME', y='DAILY_YIELD', color='navy', ax=ax[0])
+ax[0].set_title('Daily Yield')
+ax[0].set_ylabel('kW', color='navy', fontsize=17)
+
+# AC & DC power plot
+df_gen.set_index('time').drop('DATE_TIME', axis=1)[['AC_POWER', 'DC_POWER']].plot(style='o', ax=ax[1])
+ax[1].set_title('AC Power & DC Power During Day Hours')
+
+# Display the plots in Streamlit
+st.pyplot(fig)
+
+st.subheader("Generation Data Analysis")
+
+# Create subplots
+fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(15, 10))
+
+# Plot Daily and Total Yield
+gen_data.plot(x='DATE_TIME', y=['DAILY_YIELD', 'TOTAL_YIELD'], ax=ax[0], title="Daily and Total Yield (Generation Data)")
+
+# Plot AC Power & DC Power
+gen_data.plot(x='DATE_TIME', y=['AC_POWER', 'DC_POWER'], ax=ax[1], title="AC Power & DC Power (Generation Data)")
+
+# Display the plots in Streamlit
+st.pyplot(fig)
+
+st.subheader("Daily and Total Yield Analysis")
+
+# Create a copy and extract the date
+daily_gen = df_gen.copy()
+daily_gen['date'] = daily_gen['DATE_TIME'].dt.date
+
+# Group by 'date' and sum only the numerical columns
+daily_gen = daily_gen.groupby('date').sum(numeric_only=True)
+
+# Create subplots
+fig, ax = plt.subplots(ncols=2, dpi=100, figsize=(20, 5))
+
+# Plot Daily Yield
+daily_gen['DAILY_YIELD'].plot(ax=ax[0], color='navy')
+ax[0].set_title('Daily Yield')
+ax[0].set_ylabel('kW', color='navy', fontsize=17)
+
+# Plot Total Yield as a bar chart
+daily_gen['TOTAL_YIELD'].plot(kind='bar', ax=ax[1], color='navy')
+ax[1].set_title('Total Yield')
+
+# Adjust x-axis labels
+fig.autofmt_xdate(rotation=45)
+
+# Display the plots in Streamlit
+st.pyplot(fig)
+
+st.subheader("Weather Sensor Data Analysis")
+
+# Group by 'DATE_TIME' and sum
+df_sens = sens_1.groupby('DATE_TIME').sum().reset_index()
+df_sens['time'] = df_sens['DATE_TIME'].dt.time
+
+# Create subplots
+fig, ax = plt.subplots(ncols=2, nrows=1, dpi=100, figsize=(20, 5))
+
+# Irradiation plot
+df_sens.plot(x='time', y='IRRADIATION', ax=ax[0], style='o')
+ax[0].set_title('Irradiation during day hours')
+ax[0].set_ylabel('W/m²', color='navy', fontsize=17)
+
+# Ambient and Module Temperature plot
+df_sens.set_index('DATE_TIME').drop('time', axis=1)[['AMBIENT_TEMPERATURE', 'MODULE_TEMPERATURE']].plot(ax=ax[1])
+ax[1].set_title('Ambient and Module Temperature')
+ax[1].set_ylabel('°C', color='navy', fontsize=17)
+
+# Display the plots in Streamlit
+st.pyplot(fig)
+
+st.subheader("DC to AC Power Conversion Efficiency")
+
+# Create a copy of the data
+loss = gen_1.copy()
+
+# Create a new 'day' column containing only the date part from 'DATE_TIME'
+loss['day'] = loss['DATE_TIME'].dt.date
+
+# Drop the 'DATE_TIME' column to prevent summing over datetime values
+loss = loss.drop(columns=['DATE_TIME'])
+
+# Group by 'day' and sum only numeric columns
+loss = loss.groupby('day').sum()
+
+# Calculate the percentage of DC power converted to AC power
+loss['losses'] = (loss['AC_POWER'] / loss['DC_POWER']) * 100
+
+# Plot the losses
+fig, ax = plt.subplots(figsize=(17, 5))
+loss['losses'].plot(style='o--', ax=ax, label='Real Power')
+
+# Plot styling
+ax.set_title('% of DC Power Converted to AC Power', size=17)
+ax.set_ylabel('DC Power Converted (%)', fontsize=14, color='red')
+ax.axhline(loss['losses'].mean(), linestyle='--', color='gray', label='Mean')
+ax.legend()
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("DC Power During the Day for All Sources")
+
+# Create a copy of the data
+sources = gen_1.copy()
+sources['time'] = sources['DATE_TIME'].dt.time
+
+# Create the plot
+fig, ax = plt.subplots(figsize=(20, 10))
+sources.set_index('time').groupby('SOURCE_KEY')['DC_POWER'].plot(style='o', ax=ax, legend=True)
+
+# Plot styling
+ax.set_title('DC Power During the Day for All Sources', size=17)
+ax.set_ylabel('DC POWER (kW)', color='navy', fontsize=17)
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("DC Power Distribution Across Sources")
+
+# Create a copy of the data
+dc_gen = gen_1.copy()
+dc_gen['time'] = dc_gen['DATE_TIME'].dt.time
+
+# Group by 'time' and 'SOURCE_KEY', then calculate the mean
+dc_gen = dc_gen.groupby(['time', 'SOURCE_KEY'])['DC_POWER'].mean().unstack()
+
+# Define the color palette
+cmap = sns.color_palette("Spectral", n_colors=12)
+
+# Create subplots
+fig, ax = plt.subplots(ncols=2, nrows=1, dpi=100, figsize=(20, 6))
+
+# Plot the first 11 sources
+dc_gen.iloc[:, 0:11].plot(ax=ax[0], color=cmap)
+ax[0].set_title('First 11 Sources')
+ax[0].set_ylabel('DC POWER (kW)', fontsize=17, color='navy')
+
+# Plot the last 11 sources
+dc_gen.iloc[:, 11:22].plot(ax=ax[1], color=cmap)
+ax[1].set_title('Last 11 Sources')
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("Weather Data Analysis")
+
+# Create subplots
+fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(15, 10))
+
+# Plot Irradiation
+weather_data.plot(x='DATE_TIME', y='IRRADIATION', ax=ax[0], title="Irradiation (Weather Data)")
+
+# Plot Ambient & Module Temperature
+weather_data.plot(x='DATE_TIME', y=['AMBIENT_TEMPERATURE', 'MODULE_TEMPERATURE'], ax=ax[1], title="Ambient & Module Temperature (Weather Data)")
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("DC Power Converted with 2% Loss Assumption")
+
+# Calculate DC power after conversion loss
+gen_data['DC_POWER_CONVERTED'] = gen_data['DC_POWER'] * 0.98  # Assume 2% loss in conversion
+
+# Create the plot
+fig, ax = plt.subplots(figsize=(15, 5))
+gen_data.plot(x='DATE_TIME', y='DC_POWER_CONVERTED', ax=ax, title="DC Power Converted")
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("DC Power Generated During Day Hours")
+
+# Filter data for day hours (6 AM to 6 PM)
+day_data_gen = gen_data[(gen_data['DATE_TIME'].dt.hour >= 6) & (gen_data['DATE_TIME'].dt.hour <= 18)]
+
+# Create the plot
+fig, ax = plt.subplots(figsize=(15, 5))
+day_data_gen.plot(x='DATE_TIME', y='DC_POWER', ax=ax, title="DC Power Generated During Day Hours")
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("Prepare Data for Time-Based Analysis")
+
+# Copy generation data and extract time and date
+temp1_gen = gen_1.copy()
+temp1_gen['time'] = temp1_gen['DATE_TIME'].dt.time
+temp1_gen['day'] = temp1_gen['DATE_TIME'].dt.date
+
+# Copy sensor data and extract time and date
+temp1_sens = sens_1.copy()
+temp1_sens['time'] = temp1_sens['DATE_TIME'].dt.time
+temp1_sens['day'] = temp1_sens['DATE_TIME'].dt.date
+
+# Compute mean DC power grouped by time and day
+cols = temp1_gen.groupby(['time', 'day'])['DC_POWER'].mean().unstack()
+
+st.write("Data prepared successfully!")
+
+st.subheader("Time-Based DC Power and Daily Yield Analysis")
+
+# Create subplots for DC_POWER and DAILY_YIELD
+fig, ax = plt.subplots(nrows=17, ncols=2, sharex=True, figsize=(20, 30))
+
+# Plot DC Power
+dc_power_plot = temp1_gen.groupby(['time', 'day'])['DC_POWER'].mean().unstack().plot(
+    sharex=True, subplots=True, layout=(17, 2), figsize=(20, 30), ax=ax
+)
+
+# Plot Daily Yield
+daily_yield_plot = temp1_gen.groupby(['time', 'day'])['DAILY_YIELD'].mean().unstack().plot(
+    sharex=True, subplots=True, layout=(17, 2), figsize=(20, 20), style='-.', ax=ax
+)
+
+# Add titles and legends
+i = 0
+for a in range(len(ax)):
+    for b in range(len(ax[a])):
+        ax[a, b].set_title(cols.columns[i], size=15)
+        ax[a, b].legend(['DC_POWER', 'DAILY_YIELD'])
+        i += 1
+
+plt.tight_layout()
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("Temperature Analysis Over Time")
+
+# Create subplots for MODULE_TEMPERATURE and AMBIENT_TEMPERATURE
+fig, ax = plt.subplots(nrows=17, ncols=2, sharex=True, figsize=(20, 30))
+
+# Plot Module Temperature
+module_temp_plot = temp1_sens.groupby(['time', 'day'])['MODULE_TEMPERATURE'].mean().unstack().plot(
+    subplots=True, layout=(17, 2), figsize=(20, 30), ax=ax
+)
+
+# Plot Ambient Temperature
+ambient_temp_plot = temp1_sens.groupby(['time', 'day'])['AMBIENT_TEMPERATURE'].mean().unstack().plot(
+    subplots=True, layout=(17, 2), figsize=(20, 40), style='-.', ax=ax
+)
+
+# Add titles, legends, and threshold line
+i = 0
+for a in range(len(ax)):
+    for b in range(len(ax[a])):
+        ax[a, b].axhline(50, color='r', linestyle='--', label='Threshold (50°C)')
+        ax[a, b].set_title(cols.columns[i], size=15)
+        ax[a, b].legend(['Module Temperature', 'Ambient Temperature', 'Threshold'])
+        i += 1
+
+plt.tight_layout()
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("Worst Performing Source Analysis")
+
+# Filter the worst-performing source
+worst_source = gen_1[gen_1['SOURCE_KEY'] == 'bvBOhCH3iADSZry'].copy()
+worst_source['time'] = worst_source['DATE_TIME'].dt.time
+worst_source['day'] = worst_source['DATE_TIME'].dt.date
+
+# Create subplots
+fig, ax = plt.subplots(nrows=17, ncols=2, sharex=True, figsize=(20, 30))
+
+# Plot DC Power for worst source
+dc_power_plot = worst_source.groupby(['time', 'day'])['DC_POWER'].mean().unstack().plot(
+    subplots=True, layout=(17, 2), figsize=(20, 30), ax=ax
+)
+
+# Plot Daily Yield for worst source
+daily_yield_plot = worst_source.groupby(['time', 'day'])['DAILY_YIELD'].mean().unstack().plot(
+    subplots=True, layout=(17, 2), figsize=(20, 30), ax=ax, style='-.'
+)
+
+# Add titles and legends
+i = 0
+for a in range(len(ax)):
+    for b in range(len(ax[a])):
+        ax[a, b].set_title(cols.columns[i], size=15)
+        ax[a, b].legend(['DC_POWER', 'DAILY_YIELD'])
+        i += 1
+
+plt.tight_layout()
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("Inverter Performance Analysis")
+
+# Calculate average DC power for each inverter
+inverter_performance = gen_data.groupby('SOURCE_KEY')['DC_POWER'].mean().sort_values()
+
+# Identify the underperforming inverter
+underperforming_inverter = inverter_performance.idxmin()
+
+# Display the result
+st.write(f"**Underperforming Inverter:** {underperforming_inverter}")
+
+# Plot inverter performance
+fig, ax = plt.subplots(figsize=(12, 6))
+inverter_performance.plot(kind='bar', ax=ax, color='navy')
+ax.set_title("Average DC Power by Inverter")
+ax.set_ylabel("DC Power (kW)")
+ax.set_xlabel("Inverter (SOURCE_KEY)")
+plt.xticks(rotation=90)
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+st.subheader("Module and Ambient Temperature (Weather Data)")
+
+# Plot the temperature data
+fig, ax = plt.subplots(figsize=(15, 5))
+weather_data.plot(x='DATE_TIME', y=['AMBIENT_TEMPERATURE', 'MODULE_TEMPERATURE'], ax=ax, title="Module and Ambient Temperature (Weather Data)")
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+st.subheader("Inverter bvBOhCH3iADSZry Performance")
+
+# Filter data for the specific inverter
+inverter_data = gen_data[gen_data['SOURCE_KEY'] == 'bvBOhCH3iADSZry']
+
+# Plot AC and DC power
+fig, ax = plt.subplots(figsize=(15, 5))
+inverter_data.plot(x='DATE_TIME', y=['AC_POWER', 'DC_POWER'], ax=ax, title="Inverter bvBOhCH3iADSZry")
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+
+# done

pages/linear_regression.py

@@ -0,0 +1,65 @@
+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+
+st.title("Linear Regression for Solar Energy Prediction")
+
+# File Upload for Generation Data
+uploaded_gen = st.file_uploader("Upload Generation Data CSV", type=["csv"], key="lr_gen")
+uploaded_weather = st.file_uploader("Upload Weather Sensor Data CSV", type=["csv"], key="lr_weather")
+
+def load_data(file):
+    if file is not None:
+        return pd.read_csv(file)
+    return None
+
+# Load Data Separately
+gen_data = load_data(uploaded_gen)
+weather_data = load_data(uploaded_weather)
+
+# Default Data (if no file is uploaded)
+default_gen_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Generation_Data.csv')
+default_weather_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Weather_Sensor_Data.csv')
+
+if gen_data is None:
+    gen_data = default_gen_data
+if weather_data is None:
+    weather_data = default_weather_data
+
+# Choose which dataset to use
+dataset_choice = st.radio("Select dataset:", ("Generation Data", "Weather Data"))
+
+if dataset_choice == "Generation Data":
+    df = gen_data
+    target_col = "DAILY_YIELD"
+elif dataset_choice == "Weather Data":
+    df = weather_data
+    target_col = "MODULE_TEMPERATURE"
+
+# Feature Selection
+features = [col for col in df.columns if col not in ["DATE_TIME", target_col, "SOURCE_KEY"]]
+st.write("Selected Features:", features)
+
+X = df[features]
+y = df[target_col]
+
+# Train-Test Split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train Model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Predictions
+y_pred = model.predict(X_test)
+
+# Performance Metrics
+mae = mean_absolute_error(y_test, y_pred)
+mse = mean_squared_error(y_test, y_pred)
+
+# Display Results
+st.write(f"**Mean Absolute Error:** {mae:.4f}")
+st.write(f"**Mean Squared Error:** {mse:.4f}")
+st.line_chart(pd.DataFrame({"Actual": y_test.values, "Predicted": y_pred}))

pages/lstm.py

@@ -0,0 +1,90 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+
+st.title("LSTM Model for Solar Energy Prediction")
+
+# File Upload for Generation Data
+uploaded_gen = st.file_uploader("Upload Generation Data CSV", type=["csv"], key="lstm_gen")
+uploaded_weather = st.file_uploader("Upload Weather Sensor Data CSV", type=["csv"], key="lstm_weather")
+
+def load_data(file):
+    if file is not None:
+        return pd.read_csv(file)
+    return None
+
+# Load Data Separately
+gen_data = load_data(uploaded_gen)
+weather_data = load_data(uploaded_weather)
+
+# Default Data (if no file is uploaded)
+default_gen_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Generation_Data.csv')
+default_weather_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Weather_Sensor_Data.csv')
+
+if gen_data is None:
+    gen_data = default_gen_data
+if weather_data is None:
+    weather_data = default_weather_data
+
+# Choose which dataset to use
+dataset_choice = st.radio("Select dataset:", ("Generation Data", "Weather Data"))
+
+if dataset_choice == "Generation Data":
+    df = gen_data
+    target_col = "DAILY_YIELD"
+elif dataset_choice == "Weather Data":
+    df = weather_data
+    target_col = "MODULE_TEMPERATURE"
+
+# Feature Selection
+features = [col for col in df.columns if col not in ["DATE_TIME", target_col, "SOURCE_KEY"]]
+st.write("Selected Features:", features)
+
+# Normalize Data
+scaler = MinMaxScaler()
+scaled_data = scaler.fit_transform(df[features + [target_col]])
+
+# Create Sequences for LSTM
+def create_sequences(data, seq_length):
+    X, y = [], []
+    for i in range(len(data) - seq_length):
+        X.append(data[i : i + seq_length, :-1])  # All features except target
+        y.append(data[i + seq_length, -1])  # Target value
+    return np.array(X), np.array(y)
+
+seq_length = st.slider("Select Sequence Length", 1, 30, 10)
+X, y = create_sequences(scaled_data, seq_length)
+
+# Train-Test Split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Define LSTM Model
+model = Sequential([
+    LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])),
+    LSTM(50, return_sequences=False),
+    Dense(25),
+    Dense(1)
+])
+
+model.compile(optimizer='adam', loss='mse')
+
+# Train Model
+epochs = st.slider("Select Number of Epochs", 1, 100, 10)
+progress_bar = st.progress(0)
+history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=16, verbose=1, callbacks=[tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, logs: progress_bar.progress((epoch+1)/epochs))])
+
+# Predictions
+y_pred = model.predict(X_test)
+
+# Inverse Transform Predictions
+y_test_actual = scaler.inverse_transform(np.hstack((X_test[:, -1, :], y_test.reshape(-1, 1))))[:, -1]
+y_pred_actual = scaler.inverse_transform(np.hstack((X_test[:, -1, :], y_pred)))[:, -1]
+
+# Display Results
+st.write("LSTM Model Performance:")
+st.line_chart(pd.DataFrame({"Actual": y_test_actual, "Predicted": y_pred_actual}))

pages/random_forest.py

@@ -0,0 +1,68 @@
+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+
+st.title("Random Forest for Solar Energy Prediction")
+
+# File Upload for Generation Data
+uploaded_gen = st.file_uploader("Upload Generation Data CSV", type=["csv"], key="rf_gen")
+uploaded_weather = st.file_uploader("Upload Weather Sensor Data CSV", type=["csv"], key="rf_weather")
+
+def load_data(file):
+    if file is not None:
+        return pd.read_csv(file)
+    return None
+
+# Load Data Separately
+gen_data = load_data(uploaded_gen)
+weather_data = load_data(uploaded_weather)
+
+# Default Data (if no file is uploaded)
+default_gen_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Generation_Data.csv')
+default_weather_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Weather_Sensor_Data.csv')
+
+if gen_data is None:
+    gen_data = default_gen_data
+if weather_data is None:
+    weather_data = default_weather_data
+
+# Choose which dataset to use
+dataset_choice = st.radio("Select dataset:", ("Generation Data", "Weather Data"))
+
+if dataset_choice == "Generation Data":
+    df = gen_data
+    target_col = "DAILY_YIELD"
+elif dataset_choice == "Weather Data":
+    df = weather_data
+    target_col = "MODULE_TEMPERATURE"
+
+# Feature Selection
+features = [col for col in df.columns if col not in ["DATE_TIME", target_col, "SOURCE_KEY"]]
+st.write("Selected Features:", features)
+
+X = df[features]
+y = df[target_col]
+
+# Train-Test Split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Model Hyperparameters
+n_estimators = st.slider("Select Number of Trees", 10, 500, 100)
+
+# Train Model
+model = RandomForestRegressor(n_estimators=n_estimators, random_state=42)
+model.fit(X_train, y_train)
+
+# Predictions
+y_pred = model.predict(X_test)
+
+# Performance Metrics
+mae = mean_absolute_error(y_test, y_pred)
+mse = mean_squared_error(y_test, y_pred)
+
+# Display Results
+st.write(f"**Mean Absolute Error:** {mae:.4f}")
+st.write(f"**Mean Squared Error:** {mse:.4f}")
+st.line_chart(pd.DataFrame({"Actual": y_test.values, "Predicted": y_pred}))

pages/xgboost.py

@@ -0,0 +1,61 @@
+import streamlit as st
+import pandas as pd
+import xgboost as xgb
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_absolute_error
+
+st.title("XGBoost Model for Solar Energy Prediction")
+
+# File Upload for Generation Data
+uploaded_gen = st.file_uploader("Upload Generation Data CSV", type=["csv"], key="xgb_gen")
+uploaded_weather = st.file_uploader("Upload Weather Sensor Data CSV", type=["csv"], key="xgb_weather")
+
+def load_data(file):
+    if file is not None:
+        return pd.read_csv(file)
+    return None
+
+# Load Data Separately
+gen_data = load_data(uploaded_gen)
+weather_data = load_data(uploaded_weather)
+
+# Default Data (if no file is uploaded)
+default_gen_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Generation_Data.csv')
+default_weather_data = pd.read_csv('https://github.com/Sivatech24/Streamlit/raw/refs/heads/main/Plant_1_Weather_Sensor_Data.csv')
+
+if gen_data is None:
+    gen_data = default_gen_data
+if weather_data is None:
+    weather_data = default_weather_data
+
+# Choose which dataset to use
+dataset_choice = st.radio("Select dataset:", ("Generation Data", "Weather Data"))
+
+if dataset_choice == "Generation Data":
+    df = gen_data
+    target_col = "DAILY_YIELD"
+elif dataset_choice == "Weather Data":
+    df = weather_data
+    target_col = "MODULE_TEMPERATURE"
+
+# Feature Selection
+features = [col for col in df.columns if col not in ["DATE_TIME", target_col, "SOURCE_KEY"]]
+st.write("Selected Features:", features)
+
+X = df[features]
+y = df[target_col]
+
+# Train-Test Split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train Model
+n_estimators = st.slider("Select Number of Estimators", 10, 500, 100)
+model = xgb.XGBRegressor(n_estimators=n_estimators, random_state=42)
+model.fit(X_train, y_train)
+
+# Predictions
+y_pred = model.predict(X_test)
+
+# Display Results
+st.write(f"Mean Absolute Error: {mean_absolute_error(y_test, y_pred):.4f}")
+st.line_chart(pd.DataFrame({"Actual": y_test.values, "Predicted": y_pred}))