Create app.py

app.py

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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()