Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+
+# Load the dataset
+df = pd.read_csv("/content/TEMP_ANNUAL_SEASONAL_MEAN.csv")
+
+# Convert 'YEAR' column to datetime
+df.columns = df.columns.str.upper()  # Ensure column names are uppercase
+df['YEAR'] = pd.to_datetime(df['YEAR'], format='%Y')
+df.set_index('YEAR', inplace=True)
+
+# Selecting the relevant temperature column
+temp_col = 'ANNUAL'
+df[temp_col] = pd.to_numeric(df[temp_col], errors='coerce')  # Convert to numeric, handle errors
+data = df[[temp_col]].dropna()
+
+# Normalize the data
+scaler = MinMaxScaler()
+data_scaled = scaler.fit_transform(data)
+
+# Prepare sequences for LSTM
+sequence_length = 10  # Using past 10 years to predict next year
+def create_sequences(data, seq_length):
+    X, y = [], []
+    for i in range(len(data) - seq_length):
+        X.append(data[i:i + seq_length])
+        y.append(data[i + seq_length])
+    return np.array(X), np.array(y)
+
+X, y = create_sequences(data_scaled, sequence_length)
+
+# Split data into training & testing sets
+split = int(len(X) * 0.8)
+X_train, X_test = X[:split], X[split:]
+y_train, y_test = y[:split], y[split:]
+
+# Define LSTM model
+model = Sequential([
+    LSTM(100, return_sequences=True, input_shape=(sequence_length, 1)),
+    Dropout(0.2),
+    LSTM(100, return_sequences=False),
+    Dropout(0.2),
+    Dense(50, activation='relu'),
+    Dense(1)
+])
+
+model.compile(optimizer='adam', loss='mse')
+
+# Train the model
+model.fit(X_train, y_train, epochs=50, batch_size=8, validation_data=(X_test, y_test))
+
+# Make predictions
+predictions = model.predict(X_test)
+predictions = scaler.inverse_transform(predictions)  # Convert back to original scale
+
+# Streamlit UI
+st.title("India Climate Change Forecast")
+
+st.write("### Actual vs Predicted Annual Mean Temperature")
+
+fig, ax = plt.subplots(figsize=(12, 6))
+ax.plot(df.index[-len(y_test):], scaler.inverse_transform(y_test.reshape(-1, 1)), label="Actual")
+ax.plot(df.index[-len(y_test):], predictions, label="Predicted", linestyle='dashed')
+ax.set_xlabel("Year")
+ax.set_ylabel("Temperature (°C)")
+ax.set_title("Actual vs Predicted Annual Mean Temperature in India")
+ax.legend()
+st.pyplot(fig)