Update app.py

app.py

@@ -1,56 +1,74 @@
 import streamlit as st
 import joblib
 import numpy as np
-import pandas as pd
 
-# --- 1. Load Model and Dataset for Feature Information ---
+# --- 1. Load Model and Define Feature Information ---
 
-@st.cache_data
-def load_data_and_model():
+@st.cache_resource
+def load_model():
     """
-    Loads the saved model and the dataset from the Excel file.
-    Using st.cache_data to avoid reloading on every interaction.
+    Loads the saved model. Using st.cache_resource to load the model only once.
     """
     try:
         # Load the pre-trained Voting Classifier model
         model = joblib.load('voting_classifier_model.joblib')
+        return model
     except FileNotFoundError:
         st.error("The model file 'voting_classifier_model.joblib' was not found.")
         st.info("Please ensure the model file is in the same directory as this script.")
         st.stop()
-
-    try:
-        # Load your specific dataset to get feature names and default values
-        df = pd.read_excel('breast-cancer.xls')
-        # Assuming the first column is 'id' and the second is 'diagnosis' (the target)
-        # The rest are the features.
-        feature_names = df.columns[2:].tolist()
-        
-        # Store the dataframe for calculating min/max/mean values for sliders
-        feature_data = df[feature_names]
-
-    except FileNotFoundError:
-        st.error("The dataset file 'breast-cancer.xls' was not found.")
-        st.info("Please ensure your Excel file is in the same directory as this script.")
-        st.stop()
     except Exception as e:
-        st.error(f"Could not load or process the dataset file. Error: {e}")
+        st.error(f"An error occurred while loading the model: {e}")
         st.stop()
-        
-    return model, feature_names, feature_data
 
-model, feature_names, feature_data = load_data_and_model()
+model = load_model()
+
+# --- Hardcoded Feature Information ---
+# We define the feature names and their typical ranges (min, mean, max)
+# This removes the need to load the original dataset file.
+FEATURE_INFO = {
+    'radius_mean': [6.98, 14.12, 28.11],
+    'texture_mean': [9.71, 19.28, 39.28],
+    'perimeter_mean': [43.79, 91.96, 188.5],
+    'area_mean': [143.5, 654.8, 2501.0],
+    'smoothness_mean': [0.05, 0.09, 0.16],
+    'compactness_mean': [0.01, 0.10, 0.34],
+    'concavity_mean': [0.0, 0.08, 0.42],
+    'concave points_mean': [0.0, 0.04, 0.20],
+    'symmetry_mean': [0.10, 0.18, 0.30],
+    'fractal_dimension_mean': [0.04, 0.06, 0.09],
+    'radius_se': [0.11, 0.40, 2.87],
+    'texture_se': [0.36, 1.21, 4.88],
+    'perimeter_se': [0.75, 2.86, 21.98],
+    'area_se': [6.80, 40.33, 542.2],
+    'smoothness_se': [0.001, 0.007, 0.031],
+    'compactness_se': [0.002, 0.025, 0.135],
+    'concavity_se': [0.0, 0.031, 0.396],
+    'concave points_se': [0.0, 0.011, 0.052],
+    'symmetry_se': [0.007, 0.020, 0.078],
+    'fractal_dimension_se': [0.0008, 0.003, 0.029],
+    'radius_worst': [7.93, 16.26, 36.04],
+    'texture_worst': [12.02, 25.67, 49.54],
+    'perimeter_worst': [50.41, 107.26, 251.2],
+    'area_worst': [185.2, 880.5, 4254.0],
+    'smoothness_worst': [0.07, 0.13, 0.22],
+    'compactness_worst': [0.02, 0.25, 1.05],
+    'concavity_worst': [0.0, 0.27, 1.25],
+    'concave points_worst': [0.0, 0.11, 0.29],
+    'symmetry_worst': [0.15, 0.29, 0.66],
+    'fractal_dimension_worst': [0.05, 0.08, 0.20]
+}
+feature_names = list(FEATURE_INFO.keys())
 
 
 # --- 2. Streamlit App Interface ---
 
-st.set_page_config(page_title="Breast Cancer Predictor", layout="wide")
+st.set_page_config(page_title="Breast Cancer Diagnosis System", layout="wide")
 
 # Main Title
-st.title("🔬 Breast Cancer Prediction Interface")
+st.title("🔬 Breast Cancer Diagnosis System Interface")
 st.markdown("""
-This application uses your pre-trained model to predict whether a breast tumor is **Malignant** or **Benign**. 
-The input fields below are based on the columns from your `breast-cancer.xls` file.
+This application uses a pre-trained model to predict whether a breast tumor is **Malignant** or **Benign**.
 """)
 
 st.write("---")
@@ -64,19 +82,14 @@ st.sidebar.markdown("Use the sliders to provide the feature values.")
 # Dictionary to hold the user's input
 input_features = {}
 
-# Create sliders for all features based on your Excel file
-for feature in feature_names:
-    # Set min/max/default values from the actual data for better usability
-    min_val = float(feature_data[feature].min())
-    max_val = float(feature_data[feature].max())
-    mean_val = float(feature_data[feature].mean())
-
-    # Create a slider for each feature
+# Create sliders for all features based on the hardcoded info
+for feature, values in FEATURE_INFO.items():
+    min_val, mean_val, max_val = values
     input_features[feature] = st.sidebar.slider(
         label=f"{feature.replace('_', ' ').title()}",
-        min_value=min_val,
-        max_value=max_val,
-        value=mean_val,
+        min_value=float(min_val),
+        max_value=float(max_val),
+        value=float(mean_val), # Default to the mean value
         key=f"slider_{feature}"
     )
 
@@ -86,7 +99,6 @@ st.sidebar.write("---")
 # --- 4. Prediction Logic ---
 
 # Convert the dictionary of input features into a NumPy array
-# The order of features must match the order in the feature_names list
 input_data = np.array([list(input_features.values())])
 
 # Main section for displaying inputs and results
@@ -100,17 +112,16 @@ with col1:
 # "Predict" button
 if st.button("✨ Predict Diagnosis", key="predict_button"):
     try:
-        # Make prediction. This returns the string label directly (e.g., 'M' or 'B').
+        # Make prediction
         prediction_label = model.predict(input_data)[0]
 
-        # Get prediction probabilities. The order corresponds to model.classes_
+        # Get prediction probabilities
         prediction_proba = model.predict_proba(input_data)[0]
 
         with col2:
             st.subheader("Diagnosis")
-            # Display the predicted label directly
-            # We check for 'M' or 'B' as is common in this dataset
-            if prediction_label.upper() == 'M':
+            # Display the predicted label
+            if str(prediction_label).upper() == 'M':
                 st.error("Predicted Diagnosis: **Malignant**")
             else:
                 st.success("Predicted Diagnosis: **Benign**")
@@ -119,9 +130,9 @@ if st.button("✨ Predict Diagnosis", key="predict_button"):
             # Get the class labels from the model itself to ensure correct order
             class_labels = list(model.classes_)
             
-            # Display probabilities for each class using the model's class order
+            # Display probabilities for each class
             for i, label in enumerate(class_labels):
-                display_label = "Malignant" if label.upper() == 'M' else "Benign"
+                display_label = "Malignant" if str(label).upper() == 'M' else "Benign"
                 st.write(f"Confidence for **{display_label}**: `{prediction_proba[i]:.2%}`")
 
     except Exception as e: