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('temperature_model.h5')

# Prediction logic
def predict_temperature(model, date):
    # Convert the input date to a numerical feature (e.g., day of the year)
    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
    input_data = np.array([[day_of_year]], dtype=np.float32)
    
    # 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 temperature color mapping
    fig, ax = plt.subplots(figsize=(8, 6))
    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
    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)
    
    # Add color bar to indicate temperature range
    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
    sm.set_array([])  # No data needed for colorbar
    cbar = fig.colorbar(sm, ax=ax)
    cbar.set_label("Temperature (°C)", fontsize=12)
    
    # Return the forecasted temperature and plot
    return f"The forecasted temperature for {date} is {forecast[0]:.2f}°C.", fig

# Gradio interface function
def gradio_interface(date):
    return predict_temperature(model, date)

# Set up Gradio input (textbox for date entry)
date_input = gr.Textbox(label="Enter Date (YYYY-MM-DD)", placeholder="2024-12-31")

# Custom CSS for a warm blue color theme


# Set up the Gradio interface
iface = gr.Interface(
    fn=gradio_interface, 
    inputs=date_input, 
    outputs=["text", "plot"],  # Output both text and plot
    title="Temperature Forecasting 🌡️",
    description="Enter a date to predict the temperature and see it plotted with a color gradient🚨 ...Have a good day🤖🥵🥶",
    live=False,
    theme="huggingface",  # Keep the compact theme
    allow_flagging="never"
)

# Launch the Gradio interface
iface.launch()