Update app.py

app.py

@@ -3,7 +3,7 @@ import pandas as pd
 import pickle
 import sklearn  # Ensure scikit-learn is imported
 
-# Load Model with Error Handling
+# Load the pre-trained model
 try:
     with open('logreg_model.pkl', 'rb') as model_file:
         model = pickle.load(model_file)
@@ -14,31 +14,33 @@ except Exception as e:
 
 st.title('Iris Variety Prediction')
 
-# Form
+# User Input Form
 with st.form(key='form_parameters'):
-    sepal_length = st.slider('Sepal Length', 4.0, 8.0, 4.0)
-    sepal_width = st.slider('Sepal Width', 2.0, 4.5, 2.0)
-    petal_length = st.slider('Petal Length', 1.0, 7.0, 1.0)
-    petal_width = st.slider('Petal Width', 0.1, 2.5, 0.1)
+    sepal_length = st.slider('Sepal Length (cm)', 4.0, 8.0, 5.0)
+    sepal_width = st.slider('Sepal Width (cm)', 2.0, 4.5, 3.0)
+    petal_length = st.slider('Petal Length (cm)', 1.0, 7.0, 1.5)
+    petal_width = st.slider('Petal Width (cm)', 0.1, 2.5, 0.2)
     st.markdown('---')
 
     submitted = st.form_submit_button('Predict')
 
 # Data Inference
-data_inf = {
-    'sepal.length': sepal_length,
-    'sepal.width': sepal_width,
-    'petal.length': petal_length,
-    'petal.width': petal_width
-}
+if submitted:
+    # Create DataFrame for prediction
+    data_inf = {
+        'Id': 0,
+        'SepalLengthCm': sepal_length,
+        'SepalWidthCm': sepal_width,
+        'PetalLengthCm': petal_length,
+        'PetalWidthCm': petal_width
+    }
 
-data_inf = pd.DataFrame([data_inf])
+    data_inf = pd.DataFrame([data_inf])
 
-if submitted:
+    # Predict using the model
     try:
-        # Predict using Logistic Regression
         y_pred_inf = model.predict(data_inf)
-        st.write('## Iris Variety = ' + str(y_pred_inf[0]))
+        st.write('## Iris Variety: **' + str(y_pred_inf[0]) + '**')
     except Exception as e:
         st.error("Prediction failed. Please ensure the input format is correct and compatible with the model.")
         st.error(f"Error Details: {e}")