Update app.py

app.py

@@ -1,137 +1,135 @@
-import os
-import joblib
-import pandas as pd
-import streamlit as st
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import r2_score
-from typing import List, Dict, Any
-
-# Constants for directories and file names
-DIR = "C:\\Users\\Afshin\\Desktop\\10_Projects\\Project_3_Developer_Salary_Prediction\\"
-# Constants for directories and file names
-MODEL_DIR = DIR + 'models'
-DATA_DIR = DIR + 'datasets'
-DATA_FILE = 'cleaned_survey_results_public_v2.csv'
-MODEL_NAMES = [
-    #'CatBoost Regressor',
-    'XGBoost Regressor',
-    'LGBM Regressor',
-]
-
-def load_models(model_names: List[str]) -> Dict[str, Any]:
-    """Load machine learning models from disk."""
-    models = {}
-    for name in model_names:
-        path = os.path.join(MODEL_DIR, f"{name.replace(' ', '')}.joblib")
-        try:
-            models[name] = joblib.load(path)
-        except Exception as e:
-            st.error(f"Error loading model {name}: {str(e)}")
-    return models
-
-# Load models
-models = load_models(MODEL_NAMES)
-
-# Load dataset
-data_path = os.path.join(DATA_DIR, DATA_FILE)
-df = pd.read_csv(data_path)
-
-# Prepare features and target
-X = df.drop(columns=['Salary'])
-y = df['Salary']
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=123)
-
-# Pre-defined input choices
-input_choices = {
-    'MainBranch': df.MainBranch.unique().tolist(),
-    'Country': X.Country.unique().tolist(),
-    'EducationLevel': X.EducationLevel.unique().tolist(),
-    'RemoteWork': df.RemoteWork.unique().tolist(),
-}
-
-# Pre-computed statistics for default values
-default_comp = float(df.CompTotal.mean())  # Default CompTotal
-max_comp = float(df.CompTotal.max() * 1.5)
-default_years = 3.0  # Default years of experience
-max_years = float(df.YearsOfExperience.max() * 1.5)
-
-# Precompute predictions for training set
-y_train_predictions = {name: model.predict(X_train) for name, model in models.items()}
-
-def load_and_predict(sample: pd.DataFrame) -> pd.DataFrame:
-    """Predict salary using loaded models and evaluate statistics."""
-    results = []
-
-    for name, model in models.items():
-        try:
-            salary_pred = model.predict(sample)[0]
-            results.append({
-                'Model': name,
-                'Predicted Salary': salary_pred,
-                'R2 Score (%)': r2_score(y_train, y_train_predictions[name]) * 100,
-            })
-        except Exception as e:
-            st.error(f"Error during prediction with model {name}: {str(e)}")
-
-    return pd.DataFrame(results).sort_values(by='R2 Score (%)', ascending=False).reset_index(drop=True)
-
-# Streamlit UI setup
-st.set_page_config(page_title="Developer Salary Prediction App", page_icon="🤑", layout="wide")
-st.title("🤑 **Developer Salary Prediction**")
-
-# Sidebar inputs
-st.sidebar.header("Input Information")
-mainbranch = st.sidebar.selectbox("**MainBranch**", options=input_choices['MainBranch'])
-country = st.sidebar.selectbox("**Country**", options=input_choices['Country'])
-educationlevel = st.sidebar.selectbox("**Education Level**", options=input_choices['EducationLevel'])
-remotework = st.sidebar.selectbox("**Remote Work**", options=input_choices['RemoteWork'])
-comptotal = st.sidebar.number_input("**CompTotal**", min_value=0.0, max_value=max_comp, value=default_comp)
-yearsofexperience = st.sidebar.number_input("**Years of Experience**", min_value=0.0, max_value=max_years, value=default_years)
-
-# Handling predictions
-if st.sidebar.button(label=':rainbow[Predict Salary]'):
-    input_data = pd.DataFrame(
-        [[mainbranch, country, educationlevel, remotework, comptotal, yearsofexperience]], 
-        columns=['MainBranch', 'Country', 'EducationLevel', 'RemoteWork', 'CompTotal', 'YearsOfExperience'])
-    
-    results_df = load_and_predict(input_data)
-    
-    if not results_df.empty:
-        st.write("### Prediction Results:")
-        st.dataframe(results_df)
-
-# Disclaimer Section
-st.markdown("---")
-st.text('''
-    >> Developer Salary Prediction App <<
-    This Streamlit application predicts developer salary using multiple machine learning models including LGBM, XGBoost, and Random Forest regressors. 
-    Users can input developer information through a user-friendly interface, which includes fields such as country, education level, and years of experience.
-        
-    > Features:
-        **Input Components**: 
-        - **MainBranch**: Select your main area of expertise in development, such as software engineering, data science, or web development. This selection may influence salary expectations based on the branch's demand and trends.
-        
-        - **Country**: Choose your country from the dropdown list. Regions often exhibit varying salary scales due to economic factors, the cost of living, and market demand for tech workers.
-        
-        - **Education Level**: Indicate the highest level of education you have completed. Higher educational qualifications often correlate with higher earning potential in the tech industry.
-        
-        - **Remote Work**: Specify whether you primarily work remotely, on-site, or in a hybrid setup. Remote work setups can affect salary offers, especially if hiring companies are based in different geographic areas.
-        
-        - **CompTotal**: Enter your expected total compensation, which includes salary, bonuses, and other benefits. This field is crucial for setting a base for salary predictions and facilitates comparisons.
-        
-        - **Years of Experience**: Provide the number of years you've been in a coding-related job. Generally, more years of experience are associated with higher salaries due to skill accumulation and professional development.
-        
-        **Data Processing**: 
-        - The app employs a pre-processed dataset, cleaned and prepared for model training. 
-        - It utilizes features including country, education level, and years of experience for predictions.
-        - Models are loaded from disk, obtaining predictions based on user-provided input.
-
-        **Prediction**: The app performs predictions with loaded models and calculates performance metrics like R2 score.
-        **Results Display**: The predicted salary and model performance metrics are presented in a user-friendly format.
-        
-    > Usage: 
-       Fill out the developer information and click "Predict Salary" to derive insights on anticipated salary and model performance.
-       
-    > Disclaimer: 
-       This application serves educational purposes. Predictions are not guaranteed to be accurate.
+import os
+import joblib
+import pandas as pd
+import streamlit as st
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score
+from typing import List, Dict, Any
+
+# Constants for directories and file names
+MODEL_DIR = 'models'
+DATA_DIR = 'datasets'
+DATA_FILE = 'cleaned_survey_results_public.csv'
+MODEL_NAMES = [
+    'CatBoost Regressor',
+    'XGBoost Regressor',
+    'LGBM Regressor',
+]
+
+def load_models(model_names: List[str]) -> Dict[str, Any]:
+    """Load machine learning models from disk."""
+    models = {}
+    for name in model_names:
+        path = os.path.join(MODEL_DIR, f"{name.replace(' ', '')}.joblib")
+        try:
+            models[name] = joblib.load(path)
+        except Exception as e:
+            st.error(f"Error loading model {name}: {str(e)}")
+    return models
+
+# Load models
+models = load_models(MODEL_NAMES)
+
+# Load dataset
+data_path = os.path.join(DATA_DIR, DATA_FILE)
+df = pd.read_csv(data_path)
+
+# Prepare features and target
+X = df.drop(columns=['Salary'])
+y = df['Salary']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=123)
+
+# Pre-defined input choices
+input_choices = {
+    'MainBranch': df.MainBranch.unique().tolist(),
+    'Country': X.Country.unique().tolist(),
+    'EducationLevel': X.EducationLevel.unique().tolist(),
+    'RemoteWork': df.RemoteWork.unique().tolist(),
+}
+
+# Pre-computed statistics for default values
+default_comp = float(df.CompTotal.mean())  # Default CompTotal
+max_comp = float(df.CompTotal.max() * 1.5)
+default_years = 3.0  # Default years of experience
+max_years = float(df.YearsOfExperience.max() * 1.5)
+
+# Precompute predictions for training set
+y_train_predictions = {name: model.predict(X_train) for name, model in models.items()}
+
+def load_and_predict(sample: pd.DataFrame) -> pd.DataFrame:
+    """Predict salary using loaded models and evaluate statistics."""
+    results = []
+
+    for name, model in models.items():
+        try:
+            salary_pred = model.predict(sample)[0]
+            results.append({
+                'Model': name,
+                'Predicted Salary': salary_pred,
+                'R2 Score (%)': r2_score(y_train, y_train_predictions[name]) * 100,
+            })
+        except Exception as e:
+            st.error(f"Error during prediction with model {name}: {str(e)}")
+
+    return pd.DataFrame(results).sort_values(by='R2 Score (%)', ascending=False).reset_index(drop=True)
+
+# Streamlit UI setup
+st.set_page_config(page_title="Developer Salary Prediction App", page_icon="🤑", layout="wide")
+st.title("🤑 **Developer Salary Prediction**")
+
+# Sidebar inputs
+st.sidebar.header("Input Information")
+mainbranch = st.sidebar.selectbox("**MainBranch**", options=input_choices['MainBranch'])
+country = st.sidebar.selectbox("**Country**", options=input_choices['Country'])
+educationlevel = st.sidebar.selectbox("**Education Level**", options=input_choices['EducationLevel'])
+remotework = st.sidebar.selectbox("**Remote Work**", options=input_choices['RemoteWork'])
+comptotal = st.sidebar.number_input("**CompTotal**", min_value=0.0, max_value=max_comp, value=default_comp)
+yearsofexperience = st.sidebar.number_input("**Years of Experience**", min_value=0.0, max_value=max_years, value=default_years)
+
+# Handling predictions
+if st.sidebar.button(label=':rainbow[Predict Salary]'):
+    input_data = pd.DataFrame(
+        [[mainbranch, country, educationlevel, remotework, comptotal, yearsofexperience]], 
+        columns=['MainBranch', 'Country', 'EducationLevel', 'RemoteWork', 'CompTotal', 'YearsOfExperience'])
+    
+    results_df = load_and_predict(input_data)
+    
+    if not results_df.empty:
+        st.write("### Prediction Results:")
+        st.dataframe(results_df)
+
+# Disclaimer Section
+st.markdown("---")
+st.text('''
+    >> Developer Salary Prediction App <<
+    This Streamlit application predicts developer salary using multiple machine learning models including LGBM, XGBoost, and Random Forest regressors. 
+    Users can input developer information through a user-friendly interface, which includes fields such as country, education level, and years of experience.
+        
+    > Features:
+        **Input Components**: 
+        - **MainBranch**: Select your main area of expertise in development, such as software engineering, data science, or web development. This selection may influence salary expectations based on the branch's demand and trends.
+        
+        - **Country**: Choose your country from the dropdown list. Regions often exhibit varying salary scales due to economic factors, the cost of living, and market demand for tech workers.
+        
+        - **Education Level**: Indicate the highest level of education you have completed. Higher educational qualifications often correlate with higher earning potential in the tech industry.
+        
+        - **Remote Work**: Specify whether you primarily work remotely, on-site, or in a hybrid setup. Remote work setups can affect salary offers, especially if hiring companies are based in different geographic areas.
+        
+        - **CompTotal**: Enter your expected total compensation, which includes salary, bonuses, and other benefits. This field is crucial for setting a base for salary predictions and facilitates comparisons.
+        
+        - **Years of Experience**: Provide the number of years you've been in a coding-related job. Generally, more years of experience are associated with higher salaries due to skill accumulation and professional development.
+        
+        **Data Processing**: 
+        - The app employs a pre-processed dataset, cleaned and prepared for model training. 
+        - It utilizes features including country, education level, and years of experience for predictions.
+        - Models are loaded from disk, obtaining predictions based on user-provided input.
+
+        **Prediction**: The app performs predictions with loaded models and calculates performance metrics like R2 score.
+        **Results Display**: The predicted salary and model performance metrics are presented in a user-friendly format.
+        
+    > Usage: 
+       Fill out the developer information and click "Predict Salary" to derive insights on anticipated salary and model performance.
+       
+    > Disclaimer: 
+       This application serves educational purposes. Predictions are not guaranteed to be accurate.
 ''')