Update app.py

app.py

@@ -48,106 +48,40 @@ st.sidebar.markdown("**Year of Migration**: Enter the year of migration for the
 st.sidebar.markdown("**Country of Birth**: Choose the individual's birth country (e.g., United-States, Other).")
 st.sidebar.markdown("**Importance of Record**: Enter the weight of the instance (numeric value, e.g., 0.9).")
 
-# Create the input fields in the order of your DataFrame
-input_data = {
-    'age': 0,  # Default values, you can change these as needed
-    'gender': unique_values['gender'][0],
-    'education': unique_values['education'][0],
-    'worker_class': unique_values['worker_class'][0],
-    'marital_status': unique_values['marital_status'][0],
-    'race': unique_values['race'][0],
-    'is_hispanic': unique_values['is_hispanic'][0],
-    'employment_commitment': unique_values['employment_commitment'][0],
-    'employment_stat': unique_values['employment_stat'][0],
-    'wage_per_hour': 0,  # Default value
-    'working_week_per_year': 0,  # Default value
-    'industry_code': 0,  # Default value
-    'industry_code_main': unique_values['industry_code_main'][0],
-    'occupation_code': 0,  # Default value
-    'occupation_code_main': unique_values['occupation_code_main'][0],
-    'total_employed': 0,  # Default value
-    'household_stat': unique_values['household_stat'][0],
-    'household_summary': unique_values['household_summary'][0],
-    'vet_benefit': 0,  # Default value
-    'tax_status': unique_values['tax_status'][0],
-    'gains': 0,  # Default value
-    'losses': 0,  # Default value
-    'stocks_status': 0,  # Default value
-    'citizenship': unique_values['citizenship'][0],
-    'mig_year': 0,
-    'country_of_birth_own': 'United-States',
-    'importance_of_record': 0.0  # Default value
-}
-
-# Create the input fields
+# Create input fields for user input
 col1, col2, col3 = st.columns(3)
 
 with col1:
-    input_data['age'] = st.number_input("Age", min_value=0, key='age')
-    input_data['gender'] = st.selectbox("Gender", unique_values['gender'], key='gender')
-    input_data['education'] = st.selectbox("Education", unique_values['education'], key='education')
-    input_data['worker_class'] = st.selectbox("Class of Worker", unique_values['worker_class'], key='worker_class')
-    input_data['marital_status'] = st.selectbox("Marital Status", unique_values['marital_status'], key='marital_status')
-    input_data['race'] = st.selectbox("Race", unique_values['race'], key='race')
-    input_data['is_hispanic'] = st.selectbox("Hispanic Origin", unique_values['is_hispanic'], key='is_hispanic')
-    input_data['employment_commitment'] = st.selectbox("Full/Part-Time Employment", unique_values['employment_commitment'], key='employment_commitment')
-    input_data['employment_stat'] = st.selectbox("Has Own Business Or Is Self Employed", unique_values['employment_stat'], key='employment_stat')
-    input_data['wage_per_hour'] = st.number_input("Wage Per Hour", min_value=0, key='wage_per_hour')
+    age = st.number_input("Age", min_value=0)
+    gender = st.selectbox("Gender", ["Male", "Female"])
+    education = st.selectbox("Education", unique_values['education'])
+    worker_class = st.selectbox("Class of Worker", unique_values['worker_class'])
+    marital_status = st.selectbox("Marital Status", unique_values['marital_status'])
+    race = st.selectbox("Race", unique_values['race'])
+    is_hispanic = st.selectbox("Hispanic Origin", unique_values['is_hispanic'])
+    employment_commitment = st.selectbox("Full/Part-Time Employment", unique_values['employment_commitment'])
+    wage_per_hour = st.number_input("Wage Per Hour", min_value=0)
 
 with col2:
-    input_data['working_week_per_year'] = st.number_input("Weeks Worked Per Year", min_value=0, key='working_week_per_year')
-    input_data['industry_code'] = st.selectbox("Category Code of Industry", unique_values['industry_code'], key='industry_code')
-    input_data['industry_code_main'] = st.selectbox("Major Industry Code", unique_values['industry_code_main'], key='industry_code_main')
-    input_data['occupation_code'] = st.selectbox("Category Code of Occupation", unique_values['occupation_code'], key='occupation_code')
-    input_data['occupation_code_main'] = st.selectbox("Major Occupation Code", unique_values['occupation_code_main'], key='occupation_code_main')
-    input_data['total_employed'] = st.number_input("Number of Persons Worked for Employer", min_value=0, key='total_employed')
-    input_data['household_stat'] = st.selectbox("Detailed Household and Family Status", unique_values['household_stat'], key='household_stat')
-    input_data['household_summary'] = st.selectbox("Detailed Household Summary", unique_values['household_summary'], key='household_summary')
-    input_data['vet_benefit'] = st.selectbox("Veteran Benefits", unique_values['vet_benefit'], key='vet_benefit')
+    working_week_per_year = st.number_input("Weeks Worked Per Year", min_value=0)
+    industry_code = st.selectbox("Category Code of Industry", unique_values['industry_code'])
+    industry_code_main = st.selectbox("Major Industry Code", unique_values['industry_code_main'])
+    occupation_code = st.selectbox("Category Code of Occupation", unique_values['occupation_code'])
+    occupation_code_main = st.selectbox("Major Occupation Code", unique_values['occupation_code_main'])
+    total_employed = st.number_input("Number of Persons Worked for Employer", min_value=0)
+    household_stat = st.selectbox("Detailed Household and Family Status", unique_values['household_stat'])
+    household_summary = st.selectbox("Detailed Household Summary", unique_values['household_summary'])
+    vet_benefit = st.selectbox("Veteran Benefits", unique_values['vet_benefit'])
 
 with col3:
-    input_data['tax_status'] = st.selectbox("Tax Filer Status", unique_values['tax_status'], key='tax_status')
-    input_data['gains'] = st.number_input("Gains", min_value=0, key='gains')
-    input_data['losses'] = st.number_input("Losses", min_value=0, key='losses')
-    input_data['stocks_status'] = st.number_input("Dividends from Stocks", min_value=0, key='stocks_status')
-    input_data['citizenship'] = st.selectbox("Citizenship", unique_values['citizenship'], key='citizenship')
-    input_data['mig_year'] = st.selectbox("Migration Year", unique_values['mig_year'], key='migration_year')
-    input_data['country_of_birth_own'] = st.selectbox("Country of Birth", unique_values['country_of_birth_own'], key='country_of_birth_own')
-    input_data['importance_of_record'] = st.number_input("Importance of Record", min_value=0, key='importance_of_record')
-
-# Button to make predictions
-if st.button("Predict"):
-    # Transform the input data to a DataFrame for prediction
-    input_df = pd.DataFrame([input_data])
-
-    # Make predictions
-    prediction = dt_model.predict(input_df)
-    prediction_proba = dt_model.predict_proba(input_df)
-
-    # Display prediction result
-    st.subheader("Prediction")
-    if prediction[0] == 1:
-        st.success("This individual is predicted to have an income of over $50K.")
-    else:
-        st.error("This individual is predicted to have an income of under $50K")
-
-    # Show prediction probability
-    st.subheader("Prediction Probability")
-    st.write(f"The probability of the individual having an income over $50K is: {prediction_proba[0][1]:.2f}")
-
-
-
-import streamlit as st
-import requests
-
-# Streamlit UI for user input
-st.title("Income Prediction App")
-st.sidebar.header("Enter User Information")
-
-# Create input fields for user input
-age = st.number_input("Age", min_value=0)
-gender = st.selectbox("Gender", ["Male", "Female"])
-# Include other input fields...
+    tax_status = st.selectbox("Tax Filer Status", unique_values['tax_status'])
+    gains = st.number_input("Gains", min_value=0)
+    losses = st.number_input("Losses", min_value=0)
+    stocks_status = st.number_input("Dividends from Stocks", min_value=0)
+    citizenship = st.selectbox("Citizenship", unique_values['citizenship'])
+    mig_year = st.selectbox("Migration Year", unique_values['mig_year'])
+    country_of_birth_own = st.selectbox("Country of Birth", unique_values['country_of_birth_own'])
+    importance_of_record = st.number_input("Importance of Record", min_value=0.0)
 
 # Button to trigger prediction
 if st.button("Predict"):
@@ -155,18 +89,50 @@ if st.button("Predict"):
     user_input = {
         "age": age,
         "gender": gender,
-        # Include other input fields...
+        "education": education,
+        "worker_class": worker_class,
+        "marital_status": marital_status,
+        "race": race,
+        "is_hispanic": is_hispanic,
+        "employment_commitment": employment_commitment,
+        "wage_per_hour": wage_per_hour,
+        "working_week_per_year": working_week_per_year,
+        "industry_code": industry_code,
+        "industry_code_main": industry_code_main,
+        "occupation_code": occupation_code,
+        "occupation_code_main": occupation_code_main,
+        "total_employed": total_employed,
+        "household_stat": household_stat,
+        "household_summary": household_summary,
+        "vet_benefit": vet_benefit,
+        "tax_status": tax_status,
+        "gains": gains,
+        "losses": losses,
+        "stocks_status": stocks_status,
+        "citizenship": citizenship,
+        "mig_year": mig_year,
+        "country_of_birth_own": country_of_birth_own,
+        "importance_of_record": importance_of_record
     }
-
     # Send a POST request to the FastAPI server
-    response = requests.post("http://fastapi-server:8000/predict/", json=user_input)
+    response = requests.post("rasmodev-income-prediction-fastapi.hf.space/docs#/default/predict_income_predict__post", json=user_input)
     
     # Check if the request was successful
     if response.status_code == 200:
         prediction_data = response.json()
+        
         # Display prediction result to the user
         st.subheader("Prediction Result")
-        st.write(f"Income: {prediction_data['income_prediction']}")
-        st.write(f"Prediction Probability: {prediction_data['prediction_probability']:.2f}")
+    
+        # Determine income prediction and format message
+        if prediction_data['income_prediction'] == "Income over $50K":
+            st.success("This individual is predicted to have an income of over $50K.")
+        else:
+            st.error("This individual is predicted to have an income of under $50K")
+        
+        # Display prediction probability
+        st.subheader("Prediction Probability")
+        probability = prediction_data['prediction_probability']
+        st.write(f"The probability of the individual having an income over $50K is: {probability:.2f}")
     else:
         st.error("Error: Unable to get prediction")