Update app.py

app.py

@@ -1,15 +1,14 @@
 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
+import gradio as gr
 
 # Constants for directories and file names
 MODEL_DIR = 'models'
 DATA_DIR = 'datasets'
-DATA_FILE = 'cleaned_survey_results_public.csv'
+DATA_FILE = 'cleaned_survey_results_public_v2.csv'
 MODEL_NAMES = [
     'CatBoost Regressor',
     'XGBoost Regressor',
@@ -24,7 +23,7 @@ def load_models(model_names: List[str]) -> Dict[str, Any]:
         try:
             models[name] = joblib.load(path)
         except Exception as e:
-            st.error(f"Error loading model {name}: {str(e)}")
+            print(f"Error loading model {name}: {str(e)}")  # Use print for logging in Gradio
     return models
 
 # Load models
@@ -37,7 +36,6 @@ 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 = {
@@ -53,83 +51,44 @@ 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:
+def load_and_predict(mainbranch, country, educationlevel, remotework, comptotal, yearsofexperience):
     """Predict salary using loaded models and evaluate statistics."""
+    input_data = pd.DataFrame(
+        [[mainbranch, country, educationlevel, remotework, comptotal, yearsofexperience]], 
+        columns=['MainBranch', 'Country', 'EducationLevel', 'RemoteWork', 'CompTotal', 'YearsOfExperience']
+    )
+    
     results = []
 
     for name, model in models.items():
         try:
-            salary_pred = model.predict(sample)[0]
+            salary_pred = model.predict(input_data)[0]
             results.append({
                 'Model': name,
                 'Predicted Salary': salary_pred,
-                'R2 Score (%)': r2_score(y_train, y_train_predictions[name]) * 100,
+                'R2 Score (%)': r2_score(y, model.predict(X)) * 100, # Re-calculate R2 score on the entire dataset
             })
         except Exception as e:
-            st.error(f"Error during prediction with model {name}: {str(e)}")
-
+            print(f"Error during prediction with model {name}: {str(e)}")  # Logging
+     
     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)
+# Gradio interface
+inputs = [
+    gr.Dropdown(choices=input_choices['MainBranch'], label="Main Branch"),
+    gr.Dropdown(choices=input_choices['Country'], label="Country"),
+    gr.Dropdown(choices=input_choices['EducationLevel'], label="Education Level"),
+    gr.Dropdown(choices=input_choices['RemoteWork'], label="Remote Work"),
+    gr.Number(minimum=0.0, maximum=max_comp, value=default_comp, step=0.5, label="CompTotal"),
+    gr.Number(minimum=0.0, maximum=50, value=default_years, step=0.5, label="Years of Experience"),
+]
 
-# 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.
+output = gr.Dataframe(label="Prediction Results")
 
-        **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.
-''')
+gr.Interface(
+    fn=load_and_predict,
+    inputs=inputs,
+    outputs=output,
+    title="Developer Salary Prediction App",
+    description="This application predicts developer salaries using multiple machine learning models. Provide your details to get salary predictions.",
+).launch()