Update app.py

app.py

@@ -378,7 +378,7 @@ sample_RAG = {
 
 col1, col2 = st.columns(2)
 with col1:
-    st.title("Image Classification Result")
+    st.title("Image Classification")
     if not uploaded_image and not recipe_submit: 
         placeholder = Image.open("dish-placeholder.jpg")
         st.image(placeholder, caption="Placeholder Image.", use_container_width=True)
@@ -392,10 +392,11 @@ with col1:
         # Display the image
         st.image(input_image, caption="Uploaded Image.", use_container_width=True)
 with col2:
-    st.title('RAG Recipe Result')
+    st.title('RAG Recipe')
     if not query and not uploaded_image and not recipe_submit:
         display_response(sample_RAG)
-
+    if uploaded_image and not recipe_submit:
+        st.warning("Please click 'Chain Recipe' to generate a recipe from your uploaded image!", icon=':material/no_meals:')
         
 # Image Classification Section
 if recipe_submit and uploaded_image:
@@ -403,18 +404,34 @@ if recipe_submit and uploaded_image:
         predictions = classifyImage(input_image)
         print("Predictions: ", predictions)
         fpredictions = ""
+        predictions_data = []
     
         # 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}%")
+            fpredictions += f"{class_name}: {confidence:.2f}%,"
+            class_name = class_name.replace("_", " ")
+            class_name = class_name.title()
+            predictions_data.append({"class_name": class_name, "confidence": confidence})
+            st.markdown(f"*{class_name}*: {confidence:.2f}%")
         print(fpredictions)
 
+        #display as a graph
+        df = pd.DataFrame(predictions_data)
+        bar_chart = alt.Chart(df).mark_bar().encode(
+            x='confidence:Q',  # Quantitative axis for confidence
+            y='class_name:N',  # Nominal axis for class names
+            color=alt.Color('confidence:Q', scale=alt.Scale(domain=[0, 1], range=['gray', 'orange'])),  # Color scale from gray to orange
+            tooltip=['class_name:N', 'confidence:Q']  # Tooltip shows class name and confidence
+        ).properties(
+            width=500,  # Adjust the width of the chart
+            height=300  # Adjust the height of the chart
+        )
+
+        # Display the bar chart in the app
+        st.altair_chart(bar_chart, use_container_width=True)
+        
+
     # call openai to pick the best classification result based on query
     openAICall = [
         SystemMessage(