app.py

import gradio as gr
from transformers import pipeline
import datetime

# Load the Hugging Face model for weather prediction
model = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")

def predict_weather(description):
    # Use the Hugging Face model to predict the weather sentiment
    prediction = model(description)[0]

    # Map the sentiment prediction to weather categories
    if prediction['label'] == 'positive':
        weather = 'Sunny'
    elif prediction['label'] == 'negative':
        weather = 'Rainy'
    else:
        weather = 'Neutral'

    # Calculate tomorrow's date
    tomorrow = datetime.date.today() + datetime.timedelta(days=1)

    # Return the predicted weather and tomorrow's date
    return weather, tomorrow

# Define the input field for the Gradio interface
description_input = gr.inputs.Textbox(label="Weather Description")

# Define the output fields for the Gradio interface
weather_output = gr.outputs.Textbox(label="Predicted Weather")
date_output = gr.outputs.Textbox(label="Tomorrow's Date")

# Create the Gradio interface
interface = gr.Interface(fn=predict_weather,
                         inputs=description_input,
                         outputs=[weather_output, date_output],
                         title="Tomorrow's Weather Prediction",
                         description="Predict tomorrow's weather based on description.")

# Launch the Gradio interface
interface.launch()