Update app.py

app.py

@@ -12,7 +12,7 @@ model = tf.keras.models.load_model(model_path)
 # Define the core prediction function
 def predict_skin(image):
     # Preprocess image
-    image = image.resize((450, 450))  # Resize the image to 150x150
+    image = image.resize((450, 450))  # Resize the image to 450x450
     image = image.convert('RGB')  # Ensure image has 3 channels
     image = np.array(image)
     image = np.expand_dims(image, axis=0)  # Add batch dimension
@@ -30,8 +30,11 @@ def predict_skin(image):
     return probabilities_dict
 
 # Streamlit interface
-st.title("Skin disease classifier")
-st.write("Choose a picture:")
+st.title("Skin disease detection")
+st.write(" The Skin Disease Detector is a prototype and should not be used as the sole method for diagnosing skin diseases. For accurate diagnosis and treatment, please consult a trained medical professional. This tool is intended for preliminary screening purposes only and should not replace professional medical advice.")
+st.write("")
+st.write("Accurate classification works best if you provide a clear and close-up image with natural lighting.")
+st.write("")
 
 # Upload image
 uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png"])
@@ -45,7 +48,7 @@ if uploaded_image is not None:
     predictions = predict_skin(image)
     
     # Display predictions as a DataFrame
-    st.write("### Prediction Probabilities")
+    st.write("### Probabilities")
     df = pd.DataFrame(predictions.items(), columns=["Condition", "Probability"])
     st.dataframe(df)