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README.md

@@ -1,13 +1,17 @@
 ---
-title: Assignment4
-emoji: 📈
-colorFrom: yellow
-colorTo: yellow
+title: My Streamlit Project
+emoji: 🔥
+colorFrom: blue
+colorTo: indigo
 sdk: streamlit
-sdk_version: 1.38.0
+python_version: '3.10'
+tags:
+- streamlit
+- data-visualization
 app_file: app.py
-pinned: false
-license: mit
+sdk_version: 1.38.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 📜 **License**
+
+Licensed under the MIT License. See the LICENSE file for more details. If you don't like the license, well... good luck changing it! 😄

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import StandardScaler,MinMaxScaler, OneHotEncoder
+import shap
+from streamlit_shap import st_shap
+
+# Page configuration
+st.set_page_config(
+    page_title="Kiva loan amount predictor",
+    page_icon="💰")
+
+st.title('Predict Kiva loan amounts')
+
+
+# Load model and preprocessing objects
+@st.cache_resource
+def load_model_objects():
+    model_rf = joblib.load('best_reg.joblib')
+    scaler = joblib.load('scaler.joblib')
+    ohe = joblib.load('ohe.joblib')
+    return model_rf, scaler, ohe
+
+model_rf, scaler, ohe = load_model_objects()
+
+# Create SHAP explainer
+explainer = shap.TreeExplainer(model_rf)
+
+# App description
+with st.expander("What's this app?"):
+    st.markdown("""
+    This app helps you determine how much you will be succesfully funded with on Kiva
+    """)
+
+st.subheader('Describe what you want to loan to')
+
+# User inputs
+col1, col2 = st.columns(2)
+
+with col1:
+    Sector = st.selectbox('sector', options=ohe.categories_[0])
+    Country = st.selectbox('country', options=ohe.categories_[1])
+    Gender = st.selectbox('borrower_genders', options=ohe.categories_[2])
+
+with col2:
+    term_in_months = st.number_input('Lenght of loan in months', min_value=0, value=1)
+    lender_count = st.number_input('Number of Lenders', min_value=1,value=1)
+
+# Prediction button
+if st.button('Predict Price 🚀'):
+    # Prepare categorical features
+    cat_features = pd.DataFrame({'sector': [Sector], 'country': [Country],'borrower_genders': [Gender]})
+    cat_encoded = pd.DataFrame(ohe.transform(cat_features).todense(), 
+                               columns=ohe.get_feature_names_out(['sector', 'country', 'borrower_genders']))
+    
+    # Prepare numerical features
+    num_features = pd.DataFrame({
+        'term_in_months': [term_in_months],
+        'lender_count': [lender_count],
+       })
+    num_scaled = pd.DataFrame(scaler.transform(num_features), columns=num_features.columns)
+    
+    # Combine features
+    features = pd.concat([num_scaled, cat_encoded], axis=1)
+    
+    # Make prediction
+    predicted_price = model_rf.predict(features)[0]
+    
+    # Display prediction
+    st.metric(label="Predicted loan amount", value=f'{round(predicted_price)} USD')
+    
+    
+    # SHAP explanation
+    st.subheader('Price Factors Explained 🤖')
+    shap_values = explainer.shap_values(features)
+    st_shap(shap.force_plot(explainer.expected_value, shap_values, features), height=400, width=600)
+    
+    st.markdown("""
+    This plot shows how each feature contributes to the predicted price:
+    - Blue bars push the price lower
+    - Red bars push the price higher
+    - The length of each bar indicates the strength of the feature's impact
+    """)
+
+# Footer
+st.markdown("---")
+st.markdown("Developed with ❤️ using Streamlit")

best_reg.joblib

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+oid sha256:062fd7833eba30d26f48650cf05ba1afcc94dbfca5ccc64f85cc42fd373e1a82
+size 8027665

ohe.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d7ffa3ce8ec22fe74f0fe2efe9151cfbeec443c48f6ae490a8fbe6cbcac8d15
+size 3160

requirements.txt

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+requests==2.32.2
+pandas==2.2.2
+numpy==1.26.4
+matplotlib==3.8.4
+streamlit==1.32.0
+shap==0.46.0
+joblib==1.4.2
+streamlit_shap==1.0.2
+scikit-learn==1.5.2

scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f086078aa92ac1945e9551628176c435472ca10512d25e073349b5398d87817c
+size 1079