Upload app.py

app.py

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+from flask import Flask, render_template, request, jsonify
+import gradio as gr
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+import warnings
+
+# Ignore all warnings
+warnings.filterwarnings("ignore")
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load Data
+df_lending_data = pd.read_csv('Resources/lending_data.csv')
+
+# Prepare Features and Labels
+y = df_lending_data['loan_status']
+X = df_lending_data.drop(columns=['loan_status'])
+
+# Split Data
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
+
+# Train Logistic Regression Model
+model = LogisticRegression(max_iter=200, random_state=1)
+model.fit(X_train, y_train)
+
+# Gradio Function for Prediction
+def predict_loan_status(loan_size, interest_rate, borrower_income, debt_to_income, num_of_accounts, derogatory_marks, total_debt):
+    input_data = pd.DataFrame({
+        'loan_size': [loan_size],
+        'interest_rate': [interest_rate],
+        'borrower_income': [borrower_income],
+        'debt_to_income': [debt_to_income],
+        'num_of_accounts': [num_of_accounts],
+        'derogatory_marks': [derogatory_marks],
+        'total_debt': [total_debt]
+    })
+    prediction = model.predict(input_data)
+    return "Healthy Loan (0)" if prediction[0] == 0 else "High-Risk Loan (1)"
+
+# Flask route for home page
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+# Flask route for prediction
+@app.route('/predict', methods=['POST'])
+def predict():
+    # Get form data
+    loan_size = float(request.form['loan_size'])
+    interest_rate = float(request.form['interest_rate'])
+    borrower_income = float(request.form['borrower_income'])
+    debt_to_income = float(request.form['debt_to_income'])
+    num_of_accounts = int(request.form['num_of_accounts'])
+    derogatory_marks = int(request.form['derogatory_marks'])
+    total_debt = float(request.form['total_debt'])
+
+    # Prepare input data
+    input_data = pd.DataFrame({
+        'loan_size': [loan_size],
+        'interest_rate': [interest_rate],
+        'borrower_income': [borrower_income],
+        'debt_to_income': [debt_to_income],
+        'num_of_accounts': [num_of_accounts],
+        'derogatory_marks': [derogatory_marks],
+        'total_debt': [total_debt]
+    })
+
+    # Make prediction
+    prediction = model.predict(input_data)
+    result = "Healthy Loan (0)" if prediction[0] == 0 else "High-Risk Loan (1)"
+
+    return render_template('index.html', prediction_text=f'Prediction: {result}')
+
+# Flask route to serve Gradio interface
+@app.route('/gradio')
+def gradio_interface():
+    # Create the Gradio interface
+    interface = gr.Interface(
+        fn=predict_loan_status,
+        inputs=[
+            gr.Number(label="Loan Size"),
+            gr.Number(label="Interest Rate"),
+            gr.Number(label="Borrower Income"),
+            gr.Number(label="Debt-to-Income Ratio"),
+            gr.Number(label="Number of Accounts"),
+            gr.Number(label="Derogatory Marks"),
+            gr.Number(label="Total Debt"),
+        ],
+        outputs="text",
+        title="Loan Status Prediction",
+        description="Input loan details to predict whether the loan is healthy or high-risk."
+    )
+
+    # Launch Gradio interface on a different port
+    return interface.launch(share=True, inline=True)
+
+# Run Flask app
+if __name__ == "__main__":
+    app.run(debug=True)