Update app.py

app.py

@@ -66,12 +66,13 @@ def predict(image, slider_threshold=0.5, fixed_thresholds=None):
     # Create a dictionary of label scores based on the slider threshold
     slider_results = {id2label[str(i)]: float(prob) for i, prob in enumerate(probabilities) if prob > slider_threshold}
     
-    # If fixed thresholds are provided, create a dictionary of label scores based on the fixed thresholds
-    fixed_threshold_results = None
+    # If fixed thresholds are provided, format the labels into a string
+    fixed_threshold_labels_str = None
     if fixed_thresholds is not None:
-        fixed_threshold_results = {id2label[str(i)]: float(prob) for i, prob in enumerate(probabilities) if prob > fixed_thresholds[id2label[str(i)]]}
+        fixed_threshold_labels = [id2label[str(i)] for i, prob in enumerate(probabilities) if prob > fixed_thresholds[id2label[str(i)]]]
+        fixed_threshold_labels_str = ", ".join(fixed_threshold_labels)
 
-    return slider_results, fixed_threshold_results
+    return slider_results, fixed_threshold_labels_str
 
 def predict_wrapper(image, slider_threshold=0.5):
     # Download thresholds from the model repository
@@ -94,7 +95,7 @@ iface = gr.Interface(
     inputs=[gr.components.Image(type="pil"), gr.components.Slider(minimum=0.0, maximum=1.0, value=0.5, label="Threshold")],
     outputs=[
         gr.components.Label(label="Slider Threshold Predictions"),
-        gr.components.Label(label="Fixed Thresholds Predictions")
+        gr.components.Textbox(label="Fixed Threshold Labels")
     ],
     title=title, 
     description=description,