Upload streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,184 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+import streamlit as st
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import pickle
+from tensorflow.keras.models import load_model
+
+# Streamlit page configuration
+st.set_page_config(
+    page_title="Power Consumption Predictor",
+    layout="centered",
+    initial_sidebar_state="collapsed"
+)
+
+# Custom CSS for eye-catching design
+st.markdown("""
+    <style>
+    @import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
+    .main {background-color: #ffffff;}
+    .stTitle {color: #003087; font-family: 'Roboto', sans-serif; text-align: center; margin-bottom: 10px; font-size: 32px; font-weight: 700;}
+    .stSubheader {color: #003087; font-family: 'Roboto', sans-serif; font-size: 22px; font-weight: 700; margin-top: 10px; margin-bottom: 10px;}
+    .stMarkdown {font-family: 'Roboto', sans-serif; color: #212529; font-size: 16px;}
+    .stDataFrame {
+        background-color: #ffffff;
+        border-radius: 12px;
+        padding: 15px;
+        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+    }
+    .stButton>button {
+        background-color: #007bff;
+        color: white;
+        border-radius: 10px;
+        padding: 12px 30px;
+        font-size: 18px;
+        font-family: 'Roboto', sans-serif;
+        font-weight: 700;
+        display: block;
+        margin: 15px auto;
+        border: none;
+        transition: all 0.3s ease;
+    }
+    .stButton>button:hover {
+        background: linear-gradient(45deg, #0056b3, #007bff);
+        transform: scale(1.05);
+        box-shadow: 0 4px 8px rgba(0,0,0,0.2);
+    }
+    .stNumberInput label {
+        color: #007bff;
+        font-family: 'Roboto', sans-serif;
+        font-weight: 700;
+        font-size: 16px;
+    }
+    .stNumberInput input {
+        background-color: #ffffff;
+        color: #212529;
+        border: 2px solid #007bff;
+        border-radius: 8px;
+        padding: 10px;
+        font-family: 'Roboto', sans-serif;
+        font-size: 14px;
+        caret-color: #212529;
+    }
+    .stNumberInput input:focus {
+        outline: none;
+        border-color: #003087;
+        box-shadow: 0 0 8px rgba(0,123,255,0.3);
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Load model and scalers
+try:
+    model = load_model('my_model.keras')
+    scaler_X = pickle.load(open('scaler_X.pkl', 'rb'))
+    scaler_y = pickle.load(open('scaler_y.pkl', 'rb'))
+except Exception as e:
+    st.error(f"Failed to load model or scalers: {str(e)}. Ensure 'my_model.keras', 'scaler_X.pkl', and 'scaler_y.pkl' are in E:\\grid\\. "
+             "This error may occur if the TensorFlow version used to save the model differs from your installed version. "
+             "Try installing TensorFlow 2.17.0 or the version used to save the model (e.g., `pip install tensorflow==2.17.0`).")
+    st.stop()
+
+# Main app layout
+st.title("Power Consumption Predictor")
+st.markdown("""
+    Enter values for one timestep to predict power consumption for Zone1, Zone2, and Zone3.  
+    Results will be displayed as a vibrant bar plot and a clear table.
+""")
+
+# Input section
+st.subheader("Enter Timestep Data")
+st.markdown("""
+    **Instructions**:
+    - Enter values for the 8 features below (default values are provided).
+    - **Hour**: 0 to 23 (e.g., 14 for 2 PM).
+    - **DayOfWeek**: 0 to 6 (0 = Monday, 6 = Sunday).
+    - **Month**: 1 to 12 (e.g., 7 for July).
+    - **Other features**: Use reasonable values (e.g., Temperature in °C, Humidity as a fraction).
+    - Click "Predict" to see results.
+""")
+
+# Vertical form for input
+with st.container():
+    feature_names = ['Hour', 'DayOfWeek', 'Month', 'Temperature', 'Humidity', 'WindSpeed', 'GeneralDiffuseFlows', 'DiffuseFlows']
+    default_values = [0, 6, 1, 6.559, 73.8, 0.083, 0.051, 0.119]  # From dataset
+    user_input = []
+    for i, (name, default) in enumerate(zip(feature_names, default_values)):
+        if name in ['Hour', 'DayOfWeek', 'Month']:
+            value = st.number_input(
+                f"{name}",
+                min_value=0,
+                max_value=23 if name == 'Hour' else 6 if name == 'DayOfWeek' else 12,
+                value=int(default),
+                step=1,
+                key=f"input_{i}"
+            )
+            user_input.append(value)
+        else:
+            value = st.number_input(
+                f"{name}",
+                value=float(default),
+                step=0.01,
+                format="%.6f",
+                key=f"input_{i}"
+            )
+            user_input.append(value)
+
+# Predict button
+if st.button("Predict", key="predict_button"):
+    try:
+        # Replicate input for 24 timesteps
+        custom_raw_data = np.array([user_input] * 24).reshape(1, 24, 8)
+        
+        # Selective scaling
+        features_to_scale = ['Temperature', 'Humidity', 'WindSpeed', 'GeneralDiffuseFlows', 'DiffuseFlows']
+        scale_indices = [3, 4, 5, 6, 7]
+        custom_scaled = custom_raw_data.copy()
+        custom_2d_to_scale = custom_raw_data[:, :, scale_indices].reshape(-1, len(scale_indices))
+        custom_scaled_2d = scaler_X.transform(custom_2d_to_scale)
+        custom_scaled[:, :, scale_indices] = custom_scaled_2d.reshape(1, 24, len(scale_indices))
+
+        # Predict
+        y_pred_scaled = model.predict(custom_scaled)
+        if isinstance(y_pred_scaled, list):
+            y_pred_combined = np.concatenate(y_pred_scaled, axis=1)
+        else:
+            y_pred_combined = y_pred_scaled
+        y_pred_original = scaler_y.inverse_transform(y_pred_combined)
+
+        # Store predictions
+        labels = ['PowerConsumption_Zone1', 'PowerConsumption_Zone2', 'PowerConsumption_Zone3']
+        st.session_state.pred_df = pd.DataFrame(y_pred_original, columns=labels, index=['User Input'])
+        st.session_state.predictions = y_pred_original
+
+    except Exception as e:
+        st.error(f"Error processing input: {str(e)}")
+
+# Display predictions if available
+if 'predictions' in st.session_state and st.session_state.predictions is not None:
+    st.markdown("### Predicted Power Consumption")
+    fig = px.bar(
+        st.session_state.pred_df.reset_index().melt(id_vars='index', value_vars=labels, var_name='Zone', value_name='Power Consumption'),
+        x='index', y='Power Consumption', color='Zone', barmode='group',
+        title='Predicted Power Consumption by Zone',
+        labels={'index': 'Sample', 'Power Consumption': 'Power Consumption (Original Scale)'},
+        color_discrete_sequence=['#007bff', '#28a745', '#dc3545']
+    )
+    fig.update_layout(
+        plot_bgcolor='white',
+        paper_bgcolor='white',
+        font=dict(family='Roboto', size=12, color='#212529'),
+        title_font=dict(size=18, family='Roboto', color='#003087'),
+        xaxis_title="Sample",
+        yaxis_title="Power Consumption (Original Scale)",
+        legend_title="Zones",
+        margin=dict(l=40, r=40, t=60, b=40)
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+    st.markdown("### Prediction Table")
+    st.dataframe(st.session_state.pred_df.style.format("{:.4f}").set_caption("Predicted Power Consumption (Original Scale)"))
+
+# Footer
+st.markdown("---")
+st.markdown("**Made by Sadik Al Jarif**")