Update app.py

app.py

@@ -387,32 +387,33 @@ with col1:
         st.markdown(f"*Donuts*: 98.11%")
 with col2:
     st.title('RAG Recipe Result')
-    if not query:
+    if not query and not uploaded_image:
         display_response(sample_RAG)
 
 # Image Classification Section
-if uploaded_image and recipe_submit:
+if uploaded_image:
     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)
+
+        if recipe_submit:
+            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 = [