initial commit

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pickle
+import pandas as pd
+import gradio as gr
+import warnings
+from sklearn.preprocessing import StandardScaler
+
+warnings.filterwarnings('ignore')
+
+# Load the trained model
+model = pickle.load(open('GradientBoosting.pkl', 'rb'))
+
+# Load the scaler model
+scaler = pickle.load(open('scaler.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]
+    })
+
+    # Scale the input data
+    scaled_data = scaler.transform(data)
+
+    # Perform the prediction
+    prediction = model.predict(scaled_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="World Happiness Report Project",
+    description="World Happiness Report Project."
+)
+
+# Launch the interface
+interface.launch()
+
+
+# In[ ]:
+
+
+
+

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de6bffb5f3c78864468e0a5a732841c00df6553e02925f603532d63e0605d066
+size 828