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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() | |