Upload 2 files

app.py

@@ -36,19 +36,19 @@ def load_pickle_model(model_path):
 models_info = {
     "ANN Model": {
         "path": "ann_model.pkl",
-        "accuracy": 0.60
+        "accuracy": 71.0  # Use decimal percentage
     },
     "Decision Tree": {
         "path": "decision_tree_classifier_model.pkl",
-        "accuracy": 0.70
+        "accuracy": 71.96
     },
     "Logistic Regression": {
         "path": "logistic_regression_model.pkl",
-        "accuracy": 0.60
+        "accuracy": 71.47
     },
     "CNN Model": {
         "path": "best_model_.keras",
-        "accuracy": 0.90
+        "accuracy": 90.0
     }
 }
 
@@ -61,7 +61,7 @@ model_name = st.selectbox("Choose a model:", list(models_info.keys()))
 
 # Display selected model accuracy
 st.write(f"Selected Model: {model_name}")
-st.write(f"Validation Accuracy: {models_info[model_name]['accuracy'] * 100:.2f}%")
+st.write(f"Validation Accuracy: {models_info[model_name]['accuracy']:.2f}%")
 
 # Load the selected model
 if model_name == "CNN Model":
@@ -91,4 +91,5 @@ if uploaded_file is not None:
     prediction = model.predict(preprocessed_img)
     prediction_label = "Pneumonia" if prediction[0] > 0.5 else "Normal"
 
-    st.write(f"Prediction: {prediction_label} (Model: {model_name})")
+    # Highlight the prediction
+    st.markdown(f"**Prediction:** <span style='color: {'red' if prediction_label == 'Pneumonia' else 'green'}; font-size: 24px;'>{prediction_label}</span> (Model: {model_name})", unsafe_allow_html=True)

requirements.txt

@@ -7,4 +7,3 @@ scipy
 scikit-learn
 keras_tuner
 scikeras
-lime