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app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import pickle
+
+
+
+with open('./ML_APP/model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+
+def proLocation(location):
+    if location=='Rural':
+        return 0
+    elif location=='Urban':
+        return 1
+    else:
+        return 2
+
+def predict_loan_amount(gender, age, income, income_stability, property_age,  property_price, property_location):
+    input_data = {
+        
+        "Gender": [1 if gender == 'M' else 0],
+        "Age": [age],
+        "Income (USD)": [income],
+        "Income Stability": [1 if income_stability == 'Low' else 0],
+        "Property Age": [property_age],
+        "Property Price": [property_price],
+        "Property Location": [proLocation(property_location)],
+       
+    }
+    input_df = pd.DataFrame(input_data)
+   
+    prediction = model.predict(input_df.to_numpy())
+    return prediction[0]
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_loan_amount,
+    inputs=[
+        gr.Radio(['F', 'M'], label='Gender'),
+        gr.Slider(18, 70, step=1, label='Age'),
+        gr.Number(label='Income (USD)'),
+        gr.Radio(['Low', 'High'], label='Income Stability'),
+        gr.Number(label='Property Age'),
+        gr.Number(label='Property Price'),
+        gr.Radio(['Rural', 'Urban', 'Semi-Urban'], label='Property Location'),
+    ],
+    outputs="number",
+    live=True
+)
+
+iface.launch()