Update app.py

app.py

@@ -320,38 +320,38 @@ with st.expander("**What is FOOD CHAIN?**"):
 
 col1, col2 = st.columns(2)
 with col1:
+    st.title("Image Classification Result")
     if not uploaded_image: 
         placeholder = Image.open("dish-placeholder.jpg")
         st.image(placeholder, caption="Placeholder Image.", use_container_width=True)
         st.write("Top Predictions:")
         st.markdown(f"*Donuts*: 98.11%")
-    
+with col2:
+    st.title('Results: RAG')
+
 # Image Classification Section
 if uploaded_image and recipe_submit:
-    with st.expander("**Food Classification**", expanded=True, icon=':material/search_insights:'):
-        with col1:
-            st.title("Results: Image Classification")
-
-            # Open the image
-            input_image = Image.open(uploaded_image)
-        
-            # Display the image
-            st.image(input_image, caption="Uploaded Image.", use_container_width=True)
-                
-            predictions = classifyImage(input_image)
-            print("Predictions: ", predictions)
-            fpredictions = ""
-        
-            # Show the top predictions with percentages
-            st.write("Top Predictions:")
-            for class_name, confidence in predictions:
-                if int(confidence) > 0.05:
-                    fpredictions += f"{class_name}: {confidence:.2f}%,"
-                if int(confidence) > 5:
-                    class_name = class_name.replace("_", " ")
-                    class_name = class_name.title()
-                    st.markdown(f"*{class_name}*: {confidence:.2f}%")
-            print(fpredictions)
+    with col1:
+        # Open the image
+        input_image = Image.open(uploaded_image)
+    
+        # Display the image
+        st.image(input_image, caption="Uploaded Image.", use_container_width=True)
+            
+        predictions = classifyImage(input_image)
+        print("Predictions: ", predictions)
+        fpredictions = ""
+    
+        # Show the top predictions with percentages
+        st.write("Top Predictions:")
+        for class_name, confidence in predictions:
+            if int(confidence) > 0.05:
+                fpredictions += f"{class_name}: {confidence:.2f}%,"
+            if int(confidence) > 5:
+                class_name = class_name.replace("_", " ")
+                class_name = class_name.title()
+                st.markdown(f"*{class_name}*: {confidence:.2f}%")
+        print(fpredictions)
 
     # call openai to pick the best classification result based on query
     openAICall = [
@@ -396,42 +396,35 @@ if uploaded_image and recipe_submit:
         else:
             RAGresponse = get_response(predictions[0][0])
         print("RAGresponse: ", RAGresponse)
-        
-        with st.expander("Recipe Generation", expanded=True, icon=':material/menu_book:'):
-            st.title('Results: RAG')
-            display_response(RAGresponse)
+        display_response(RAGresponse)
 elif uploaded_image is not None:
     with col1:
-        with st.expander("**Food Classification**", expanded=True, icon=':material/search_insights:'):
-            st.title("Results: Image Classification")
-            # Open the image
-            input_image = Image.open(uploaded_image)
-        
-            # Display the image
-            st.image(input_image, caption="Uploaded Image.", use_container_width=True)
-        
-            # Classify the image and display the result
-            predictions = classifyImage(input_image)
-            fpredictions = ""
-        
-            # Show the top predictions with percentages
-            st.write("Top Predictions:")
-            for class_name, confidence in predictions:
-                if int(confidence) > 0.05:
-                    fpredictions += f"{class_name}: {confidence:.2f}%,"
-                if int(confidence) > 5:
-                    class_name = class_name.replace("_", " ")
-                    class_name = class_name.title()
-                    st.markdown(f"*{class_name}*: {confidence:.2f}%")
-            print(fpredictions)
+        # Open the image
+        input_image = Image.open(uploaded_image)
+    
+        # Display the image
+        st.image(input_image, caption="Uploaded Image.", use_container_width=True)
+    
+        # Classify the image and display the result
+        predictions = classifyImage(input_image)
+        fpredictions = ""
+    
+        # Show the top predictions with percentages
+        st.write("Top Predictions:")
+        for class_name, confidence in predictions:
+            if int(confidence) > 0.05:
+                fpredictions += f"{class_name}: {confidence:.2f}%,"
+            if int(confidence) > 5:
+                class_name = class_name.replace("_", " ")
+                class_name = class_name.title()
+                st.markdown(f"*{class_name}*: {confidence:.2f}%")
+        print(fpredictions)
 
 elif recipe_submit:
     with col2:
         response = get_response(query)
         print(response)
-        with st.expander("**Recipe Generation**", expanded=True, icon=':material/menu_book:'):
-            st.title("Results: RAG")
-            display_response(response)
+        display_response(response)
 else:
     st.warning("Please input an image and/or a prompt.", icon=':material/no_meals:')