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@@ -10,7 +10,8 @@ RUN apt-get update && apt-get install -y \
     && rm -rf /var/lib/apt/lists/*
 
 COPY requirements.txt ./
-COPY src/ ./src/
+COPY . .
+# COPY src/ ./src/
 
 RUN pip3 install -r requirements.txt
 
@@ -18,4 +19,4 @@ EXPOSE 8501
 
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+from predictor import make_prediction
+
+st.set_page_config(page_title='Webfala', page_icon='💰')
+st.title('Webfala Loan Eligibility Predictor')
+st.markdown("Provide your details to check if you're eligible for a loan")
+
+#collect user inputs
+full_name = st.text_input('Full name', placeholder='Enter your full name')
+gender = st.selectbox('Gender', ['Female', 'Male'])
+married = st.selectbox('Married', ['Yes', 'No'])
+dependents = st.selectbox('Number of Dependents', ['0', '1', '2', '3', '3+'])
+education = st.selectbox('Education', ['Graduate', 'Not Graduate'])
+self_employed = st.selectbox('Self Employed', ['Yes', 'No'])
+applicant_income = st.number_input('Applicant Income(₦)', min_value=0)
+coapplicant_income = st.number_input('Co-applicant Income(₦)', min_value=0)
+loan_amount = st.number_input('Loan Amount (in thousand ₦)', min_value=0)
+loan_term = st.number_input('Loan term (in days: 0 - 365)', min_value=0)
+credit_history = st.selectbox('Credit History', [1, 0])
+property_area = st.selectbox('Property Area',  ['Urban', 'Semiurban', 'Rural'])
+
+#Bundle inputs
+user_input = {
+    'Gender': gender,
+    'Married': married,
+    'Dependents': dependents,
+    'Education': education,
+    'Self_Employed': self_employed,
+    'ApplicantIncome': applicant_income,
+    'CoapplicantIncome': coapplicant_income,
+    'LoanAmount': loan_amount,
+    'Loan_Amount_Term': loan_term,
+    'Credit_History': credit_history,
+    'Property_Area': property_area
+}
+
+if st.button('Check Eligibility'):
+    with st.spinner("Analyzing..."):
+        pred, prob = make_prediction(user_input)
+        if pred == 1:
+            st.success(f"Dear {full_name}, you're eligible for this loan ")
+        else:
+            st.error(f"Dear {full_name}, sorry, you're not eligible for this loan")
+    st.info(f'Confidence: **{prob:.2f}** %')
+
+st.markdown("---")
+st.markdown("Copyright 2025")

model/dt_clf.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1e81f8aa83ffd9e86de9d1183ae8610e02074e074fbd0731a10b7e40729e231
+size 2985

predictor.py

@@ -0,0 +1,39 @@
+import joblib
+import numpy as np
+import pandas as pd
+
+#MODEL_PATH = 'model/dt_clf.joblib'  #another method to load the model
+model = joblib.load('model/dt_clf.joblib')
+
+FEATURE_NAMES = ['Gender', 'Married', 'Dependents', 'Education',
+       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',
+       'Loan_Amount_Term', 'Credit_History', 'Property_Area']
+
+def preprocess_input(user_input):
+    mapping = {
+        'Gender': {'Female': 0, 'Male': 1},
+        'Married': {"Yes": 1, 'No': 0},
+        'Education': {'Graduate': 1, 'Not Graduate': 0},
+        'Self_Employed': {'Yes': 1, 'No': 0},
+        'Property_Area': {'Rural': 0, "Semiurban": 1, 'Urban': 2},
+        'Dependents': {'0':0, '1':1, '2':2, '3':3, '3+':4}
+    }
+
+    processed = []
+    for feature in FEATURE_NAMES:
+        val = user_input[feature]
+        if feature in mapping:
+            val = mapping[feature][val]
+        processed.append(val)
+
+    return np.array(processed).reshape(1, -1)
+
+def make_prediction(user_input):
+    try:
+        processed_input = preprocess_input(user_input)
+        pred = model.predict(processed_input)[0]
+        prob = model.predict_proba(processed_input).max()
+        return pred, prob
+    except Exception as e:
+        return f'Error: {str(e)}', 0.0
+

requirements.txt

@@ -1,3 +0,0 @@
-altair
-pandas
-streamlit