Update app.py

app.py

@@ -9,14 +9,14 @@ pneumonia_model = tf.keras.models.load_model("pneumonia_model.h5")
 cancer_model = tf.keras.models.load_model("breast_cancer_model.h5")
 diabetes_model = tf.keras.models.load_model("diabetes_model.h5")
 
-# Fix: Properly open image files
+# Function to preprocess images
 def preprocess_image(file_path):
     img = Image.open(file_path).convert("RGB").resize((128, 128))
     img_array = np.array(img) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
     return img_array
 
-# Fix: Ensure only numeric columns are used from CSV files
+# Function to make predictions
 def predict_disease(file, disease):
     if disease == "Pneumonia":
         img_array = preprocess_image(file)
@@ -25,7 +25,8 @@ def predict_disease(file, disease):
 
     elif disease in ["Breast Cancer", "Diabetes"]:
         df = pd.read_csv(file)
-        df = df.select_dtypes(include=[np.number])  # Convert CSV to numeric values
+        df = df.select_dtypes(include=[np.number])  # Keep only numeric values
+        df = df.iloc[:, :8]  # Ensure only the first 8 columns are used
         model = cancer_model if disease == "Breast Cancer" else diabetes_model
         prediction = model.predict(df)
         return ["Positive" if p > 0.5 else "Negative" for p in prediction]