Update app.py

app.py

@@ -1,83 +1,39 @@
-from flask import Flask, request, render_template
-import pandas as pd
-import numpy as np
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import train_test_split
-from joblib import dump, load
-import os
-
-# Existing app initialization
-app = Flask(__name__)
-
-
-# Function to load and preprocess the data
-def load_and_train_model():
-    # Load and process data
-    data = pd.read_csv("clean_train.csv")
-    X = data.drop('Credit_Score', axis=1)
-    y = data['Credit_Score']
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-    # Train the model
-    rf = RandomForestClassifier(random_state=42)
-    rf.fit(X_train, y_train)
-
-    # Save the model to a file
-    dump(rf, 'credit_classifier.joblib', compress=('gzip', 9))
-    return rf
-
-# Load the trained model (or train it if necessary)
-def load_model():
-    if os.path.exists('credit_classifier.joblib'):
-        return load('credit_classifier.joblib')
-    else:
-        return load_and_train_model()
-
-loaded_model = load_model()
-
-# Define target names
-target_names = {0: "Good", 1: "Poor", 2: "Standard"}
-
-# Flask routes
-@app.route("/")
-def home():
-    return render_template("index.html")
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    try:
-        # Extract input values from the form
-        outstanding_debt = float(request.form['outstanding_debt'])
-        credit_mix = int(request.form['credit_mix'])
-        credit_history_age = float(request.form['credit_history_age'])
-        monthly_balance = float(request.form['monthly_balance'])
-        payment_behaviour = float(request.form['payment_behaviour'])
-        annual_income = float(request.form['annual_income'])
-        delayed_payments = int(request.form['delayed_payments'])
-
-        # Prepare input data for prediction
-        input_data = [[
-            outstanding_debt, credit_mix, credit_history_age,
-            monthly_balance, payment_behaviour, annual_income,
-            delayed_payments
-        ]]
-
-        # Predict using the loaded model
-        prediction = loaded_model.predict(input_data)[0]  # Use loaded_model here
-
-        # Map the prediction to the category name
-        target_names = {0: "Good", 1: "Poor", 2: "Standard"}
-        prediction_text = target_names.get(prediction, "Unknown")
-
-        # Render result.html with the prediction
-        return render_template('result.html', prediction=prediction_text)
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
-
-#if __name__ == "__main__":
-    #app.run(debug=True)
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=8080)
-
+import gradio as gr
+import pandas as pd
+import numpy as np
+from joblib import load
+
+# Load the trained model
+model = load("credit_classifier.joblib")
+
+# Prediction function
+def predict_credit_score(outstanding_debt, credit_mix, credit_history_age, monthly_balance,
+                         payment_behaviour, annual_income, delayed_payments):
+    input_data = [[
+        outstanding_debt, credit_mix, credit_history_age,
+        monthly_balance, payment_behaviour, annual_income,
+        delayed_payments
+    ]]
+    prediction = model.predict(input_data)[0]
+
+    target_names = {0: "Good", 1: "Poor", 2: "Standard"}
+    return target_names.get(prediction, "Unknown")
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict_credit_score,
+    inputs=[
+        gr.Number(label="Outstanding Debt"),
+        gr.Number(label="Credit Mix"),
+        gr.Number(label="Credit History Age"),
+        gr.Number(label="Monthly Balance"),
+        gr.Number(label="Payment Behaviour"),
+        gr.Number(label="Annual Income"),
+        gr.Number(label="Delayed Payments"),
+    ],
+    outputs="text",
+    title="Credit Scoring Classifier",
+    description="Enter your credit-related information to classify your credit score as Good, Standard, or Poor.",
+)
+
+demo.launch()