Update app.py

app.py

@@ -18,7 +18,7 @@ def predict_pneumonia(image):
     prediction = model.predict(img_array)[0][0]
 
     # Use a more robust threshold for determining whether an image has pneumonia
-    threshold = 0.7
+    threshold = 0.5
 
     if prediction >= threshold:
         pneumonia_prediction = 1
@@ -31,13 +31,18 @@ def predict_pneumonia(image):
     # Return the top two possible classifications
     top_classes = model.predict_classes(img_array)
 
+    # Return the class names and the confidence scores for each class
+    class_names = ["Pneumonia", "Normal"]
+    confidence_scores = class_probabilities
+
     return {
         "Pneumonia": pneumonia_prediction,
         "Class probabilities": class_probabilities,
-        "Top classes": top_classes
+        "Top classes": top_classes,
+        "Class names": class_names,
+        "Confidence scores": confidence_scores
     }
 
-
 inputs = gr.inputs.Image(shape=(180, 180))
 outputs = gr.outputs.Label(num_top_classes=2)
 
@@ -45,8 +50,8 @@ gradio_interface = gr.Interface(
     fn=predict_pneumonia,
     inputs=inputs,
     outputs=outputs,
-    title="Classificação de Pneumonia em Raios-X de Tórax",
-    description="Esta aplicação classifica imagens de raios-X de tórax em pneumonia e normal.",
+    title="Pneumonia X-Ray Classification API",
+    description="This API classifies images of chest X-rays as having pneumonia or being normal.",
     examples=[
         ["person1946_bacteria_4875.jpeg"],
         ["person1952_bacteria_4883.jpeg"],
@@ -54,6 +59,8 @@ gradio_interface = gr.Interface(
         ["NORMAL2-IM-1431-0001.jpeg"]
     ],
     theme="default",
+    allow_uploads=True
 )
 
+
 gradio_interface.launch()