adding intelligent way!

app.py

@@ -10,6 +10,7 @@ from PIL import Image
 @st.cache_resource
 def load_my_model():
     # Load the new, modern .keras file
+    # (Assuming your file is named 'resnet50_dryfruits.keras')
     model = load_model('resnet50_dryfruits.keras') 
     return model
 
@@ -48,7 +49,17 @@ if uploaded_file is not None:
     predicted_class_name = class_names[predicted_index]
     confidence = np.max(prediction[0])
     
-    # 3. Display results
+    # 3. Display results with confidence threshold
+    CONFIDENCE_THRESHOLD = 0.85  # Set your threshold (e.g., 90%)
+    
+    # Always display the uploaded image
     st.image(img, caption="Uploaded Image", use_column_width=True)
-    st.markdown(f"## Prediction: **{predicted_class_name}**")
-    st.markdown(f"### Confidence: **{confidence * 100:.2f}%**")
+
+    if confidence < CONFIDENCE_THRESHOLD:
+        # If confidence is low, show a "Not Sure" message
+        st.markdown(f"## Prediction: Not Sure")
+        st.write(f"This doesn't look like a dry fruit from my dataset. Please upload a clearer image.")
+    else:
+        # If confidence is high, show the prediction
+        st.markdown(f"## Prediction: **{predicted_class_name}**")
+        st.markdown(f"### Confidence: **{confidence * 100:.2f}%**")