Update app.py

app.py

@@ -232,7 +232,7 @@ def classifyImage(input_image):
     # Prepare the top 5 predictions with their class names and percentages
     top_predictions = [(class_names[i], predictions[i] * 100) for i in top_indices]
     for i, (class_name, confidence) in enumerate(top_predictions, 1):
-        print(f"{i}. Predicted {class_name} with {confidence:.2f}% Confidence")
+        print(f"{i}. Predicted {class_name} with {confidence:.1f}% Confidence")
 
     return top_predictions
 
@@ -385,7 +385,7 @@ with col1:
         placeholder = Image.open("dish-placeholder.jpg")
         st.image(placeholder, caption="Placeholder Image.", use_container_width=True)
         st.header("Top Predictions:")
-        st.markdown(f"*Donuts*: 98.11%")
+        st.markdown(f"*Donuts*: 98.1%")
         
     if uploaded_image:
         # Open the image
@@ -408,17 +408,16 @@ if recipe_submit and uploaded_image:
         fpredictions = ""
         class_names = []
         confidences = []
-        MIN_CONFIDENCE = 7 # 7%
         
         # Show the top predictions with percentages
         # st.write("Top Predictions:")
         for class_name, confidence in predictions:
-            fpredictions += f"{class_name}: {confidence:.2f}%,"
+            fpredictions += f"{class_name}: {confidence:.1f}%,"
             class_name = class_name.replace("_", " ")
             class_name = class_name.title()
             # st.markdown(f"*{class_name}*: {confidence:.2f}%")
             class_names.append(class_name)
-            confidences.append(confidence if confidence > MIN_CONFIDENCE else MIN_CONFIDENCE) #for minimum bar length
+            confidences.append(confidence)
 
         print(fpredictions)