Create app.py

app.py

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+# libraries for data preprocessing
+import numpy as np 
+import pandas as pd 
+import pickle
+
+import gradio as gr 
+
+  
+## lets load the model 
+with open('loan-model.bin', 'rb') as f_in:
+    dv, model = pickle.load(f_in) 
+
+
+# Example function to preprocess the input
+def applicant_data(gender, married, dependents, education, self_employed, applicantincome, coapplicantincome, loanamount, loan_amount_term, credit_history, property_area):
+    # Create a dictionary of the input features
+    input_data = {
+        'Gender': gender,
+        'Married': married,
+        'Dependents': dependents,
+        'Education': education,
+        'Self_Employed': self_employed,
+        'ApplicantIncome': applicantincome,
+        'CoapplicantIncome': coapplicantincome,
+        'LoanAmount': loanamount,
+        'Loan_Amount_Term': loan_amount_term,
+        'Credit_History': credit_history,
+        'Property_Area': property_area
+    }
+
+    # Transform the input data using the pre-fitted transformer (e.g., DictVectorizer, OneHotEncoder)
+    X = dv.transform([input_data])
+
+    return X
+
+# Function to make predictions
+def predict_loan_status(gender, married, dependents, education, self_employed, applicantincome, coapplicantincome, loanamount, loan_amount_term, credit_history, property_area):
+    # Preprocess the input data
+    input_data = applicant_data(gender, married, dependents, education, self_employed, applicantincome, coapplicantincome, loanamount, loan_amount_term, credit_history, property_area)
+    
+    # Predict using the model
+    prediction = model.predict_proba(input_data)[:,1]
+    
+    
+    # Post-process the prediction if needed
+    # Assuming the model output is a single prediction value
+    predicted_value = prediction[0]
+
+    print(predicted_value)
+    
+     # Return verdict based on threshold
+    if predicted_value >= 0.3:
+        return f'Verdict: Good standing - Approved ({predicted_value:.2f})'
+    else:
+        return f'Verdict: Bad standing - Rejected ({predicted_value:.2f})'
+
+
+
+# Gradio interface
+# Gradio interface with improved input handling
+inputs = [
+    gr.Dropdown(choices=["Male", "Female"], label="Gender"),
+    gr.Dropdown(choices=["Yes", "No"], label="Married"),
+    gr.Number(label="Dependents", value=0),
+    gr.Dropdown(choices=["Graduate", "Not Graduate"], label="Education"),
+    gr.Dropdown(choices=["Yes", "No"], label="Self Employed"),
+    gr.Number(label="Applicant Income", value=0),
+    gr.Number(label="Coapplicant Income", value=0),
+    gr.Number(label="Loan Amount", value=0),
+    gr.Number(label="Loan Amount Term", value=360),
+    gr.Dropdown(choices=[0, 1], label="Credit History"),
+    gr.Dropdown(choices=["Urban", "Semiurban", "Rural"], label="Property Area")
+]
+
+
+output_text = gr.Textbox(label="Loan Prediction")
+
+
+
+# Create the Gradio interface
+gr.Interface(fn=predict_loan_status, inputs=inputs, outputs=output_text, title="Loan Status Prediction").launch(share=True)