Update app.py

app.py

@@ -204,7 +204,7 @@ with st.expander("Learn More: What is a CNN?"):
     st.write("""
     A Neural Network is a system inspired by the human brain, composed of interconnected nodes (neurons) organized in layers: an input layer, one or more hidden layers, and an output layer.
     Data (like text, numbers, images) is fed into the input layer, encoded as numbers. This information flows through the network, undergoing mathematical transformations at each node based on learned 'weights'.
-    The network 'learns' by adjusting these weights during training to minimize the difference between its predictions and the actual outcomes.
+    The network 'learns' by adjusting these weights during training to minimize the difference between its classifications and the actual outcomes.
     """)
     # Consider adding a simple diagram URL if available
     # st.image("url_to_neural_network_diagram.png")
@@ -359,13 +359,13 @@ formatted_class_names = [food.replace("_", " ").title() for food in class_names]
 st.write(f"The model was built using the **Food-101 dataset**.")
 with st.expander("View All 101 Food Classes"):
     st.write(f"The dataset consists of 101 classes of food: {', '.join(formatted_class_names)}")
-st.info("When predicting, please provide an image belonging to one of these 101 classes. The model has not been trained on other types of food or objects.")
+st.info("When Classifying, please provide an image belonging to one of these 101 classes. The model has not been trained on other types of food or objects.")
 st.divider()
 
 # --- Model Performance ---
 st.subheader("Model Performance Insights")
 st.write("""
-After training, some food classes are predicted more accurately than others.
+After training, some food classes are classified more accurately than others.
 This can be due to factors like the number of training images available for each class, visual similarity between classes, and image quality.
 We use the **F1-score** to evaluate performance per class, as it balances precision and recall.
 """)
@@ -550,7 +550,7 @@ with cols[2]:
 
     # Prediction Button
     predict_button = st.button(
-        label="Predict Food!",
+        label="Classify Food!",
         icon="⚛️",
         type="primary",
         use_container_width=True,
@@ -580,7 +580,7 @@ with cols[3]:
 # --- Column 5: Output ---
 with cols[4]:
     st.markdown('<div class="centered">', unsafe_allow_html=True)
-    st.subheader("3. Prediction Result") # H3 targeted by CSS
+    st.subheader("3. Classification Result") # H3 targeted by CSS
 
     if st.session_state.prediction_result and st.session_state.predicted_image_bytes:
         st.image(st.session_state.predicted_image_bytes, caption="Image Analyzed", width=200)
@@ -598,7 +598,7 @@ with cols[4]:
              st.write(f"Confidence: {probability:.1%}") # Slightly less verbose confidence
 
     elif predict_button:
-           st.error("Prediction failed or image invalid.")
+           st.error("Classification failed or image invalid.")
     else:
         st.info("Result will appear here.")