Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from datetime import datetime
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+
+# Load the saved model
+model = tf.keras.models.load_model('/kaggle/working/temperature_model.h5')
+
+# Prediction logic
+def predict_temperature(model, date):
+    # Convert the input date to a numerical feature (e.g., day of the year, timestamp)
+    try:
+        date_obj = datetime.strptime(date, '%Y-%m-%d')
+        day_of_year = date_obj.timetuple().tm_yday  # Get the day of the year (1-366)
+    except ValueError:
+        return "Invalid date format! Please enter the date in 'YYYY-MM-DD' format."
+
+    # Create input data for the model (assuming the model accepts this as a feature)
+    input_data = np.array([[day_of_year]], dtype=np.float32)  # Reshape to (1, 1) if necessary
+    
+    # Predict temperature
+    forecast = model.predict(input_data).flatten()
+    
+    # Create a color map for warm and cold temperatures
+    cmap = cm.get_cmap("coolwarm")
+    norm = plt.Normalize(vmin=-30, vmax=40)  # Set min and max temperature values
+    
+    # Plotting with enhanced aesthetics
+    fig, ax = plt.subplots(figsize=(8, 6))
+    
+    # Map temperature value to color
+    color = cmap(norm(forecast[0]))
+    
+    ax.plot([day_of_year], [forecast[0]], marker='o', color=color, linestyle='-', markersize=10, label=f"Predicted Temp: {forecast[0]:.2f}°C")
+    
+    # Customize the plot for better visualization
+    ax.set_title(f"Temperature Forecast for {date}", fontsize=18, fontweight='bold', color='darkslategray')
+    ax.set_xlabel("Day of the Year", fontsize=14, color='slategray')
+    ax.set_ylabel("Temperature (°C)", fontsize=14, color='slategray')
+    ax.set_xticks([day_of_year])
+    ax.set_xticklabels([date], rotation=45, ha='right', fontsize=12, color='darkslategray')
+    ax.grid(True, which='both', linestyle='--', linewidth=0.5, color='lightgray')
+    ax.legend(loc="upper left", fontsize=12)
+    ax.margins(y=0.1)
+    
+    # Adding color bar to indicate temperature range
+    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm.set_array([])  # No data is required for colorbar
+    cbar = fig.colorbar(sm, ax=ax)
+    cbar.set_label("Temperature (°C)", fontsize=12)
+    
+    # Adding interactive features with annotations
+    ax.annotate(f"{forecast[0]:.2f}°C", (day_of_year, forecast[0]), textcoords="offset points", xytext=(0,10), ha='center', fontsize=14, color='darkgreen')
+
+    # Return the plot and the forecasted temperature
+    return f"The forecasted temperature for {date} is {forecast[0]:.2f}°C.", fig
+
+# Example Gradio interface
+def gradio_interface(date):
+    return predict_temperature(model, date)
+
+# Set up Gradio input (use Textbox for date entry)
+date_input = gr.Textbox(label="Enter Date (YYYY-MM-DD)", placeholder="2024-12-31")  # Allow user to input a date manually
+
+# Custom CSS for enhanced interface
+css = """
+#interface {
+    background: linear-gradient(to right, #00b4db, #0083b0);  /* Cool blue to warm orange gradient */
+    border-radius: 20px;
+    padding: 20px;
+    box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.2);
+}
+#interface .output {
+    font-size: 18px;
+    color: #333;
+    font-family: Arial, sans-serif;
+}
+#interface .input-textbox {
+    font-size: 16px;
+    color: #333;
+    border: 2px solid #0083b0;
+    border-radius: 10px;
+    padding: 10px;
+    width: 300px;
+}
+#interface .input-textbox:focus {
+    border-color: #00b4db;
+    outline: none;
+}
+#interface .output-text {
+    font-size: 16px;
+    font-weight: bold;
+    color: #333;
+}
+#interface .output-plot {
+    border-radius: 10px;
+    box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.2);
+    margin-top: 20px;
+}
+"""
+
+# Set up Gradio interface with more customization
+iface = gr.Interface(
+    fn=gradio_interface, 
+    inputs=date_input, 
+    outputs=["text", "plot"],  # Output both text and plot
+    title="Interactive Temperature Forecasting",
+    description="This application predicts the temperature for a given date based on historical data. Enter a date to get the prediction and see it plotted interactively.",
+    live=True,  # Live updates if needed
+    theme="compact",  # Compact theme for better aesthetics
+    allow_flagging="never",  # Optional: Hide flagging options to improve UI
+    css=css  # Apply custom CSS
+)
+
+# Launch the Gradio interface
+iface.launch()