initial commit

app.py

@@ -1,60 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[4]:
-
-
-import pickle
-import pandas as pd
-import gradio as gr
-import warnings
-warnings.filterwarnings('ignore')
-
-# Load the trained model
-model = pickle.load(open('GradientBoosting.pkl', 'rb'))
-
-def word_happiness(Standard_Error, Economy_GDP_per_Capita, Family, Freedom, Trust_Government_Corruption, Generosity, Dystopia_Residual):
-    # Prepare the input data as a DataFrame
-    data = pd.DataFrame({
-        'Standard_Error': [Standard_Error],
-        'Economy_GDP_per_Capita': [Economy_GDP_per_Capita],
-        'Family': [Family],
-        'Freedom': [Freedom],
-        'Trust_Government_Corruption': [Trust_Government_Corruption],
-        'Generosity': [Generosity],
-        'Dystopia_Residual': [Dystopia_Residual]
-    })
-
-    # Perform the prediction
-    prediction = model.predict(data)
-    return prediction[0]
-
-# Create the input components
-input_components = [
-    gr.inputs.Number(label="Standard Error"),
-    gr.inputs.Number(label="Economy GDP per Capita"),
-    gr.inputs.Number(label="Family"),
-    gr.inputs.Number(label="Freedom"),
-    gr.inputs.Number(label="Trust Government Corruption"),
-    gr.inputs.Number(label="Generosity"),
-    gr.inputs.Number(label="Dystopia Residual")
-]
-
-# Create the interface
-interface = gr.Interface(
-    fn=word_happiness,
-    inputs=input_components,
-    outputs="number",
-    title="Word Happiness Report Project",
-    description="Word Happiness Report Project."
-)
-
-# Launch the interface
-interface.launch()
-
-
-# In[ ]:
-
-
-
-