Application code

app.py

@@ -11,7 +11,7 @@ os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 model = tf.keras.models.load_model("maheshbabu.h5")
 
 # Define class labels
-classes = ["Normal","Cancerous"]
+classes = ["Normal", "Cancerous"]
 
 # Prediction function
 def predict(image):
@@ -26,7 +26,12 @@ def predict(image):
         class_idx = np.argmax(predictions[0])         # Get index of highest probability
         confidence = predictions[0][class_idx]       # Get confidence score
 
-        return f"{classes[class_idx]} ({confidence:.2f})"  # Return class and confidence
+        # Modify the output based on the prediction
+        if classes[class_idx] == "Normal":
+            return "No Cancer Found"
+        else:
+            return "Cancer Found!"
+
     except Exception as e:
         return f"Error processing image: {str(e)}"
 
@@ -34,10 +39,10 @@ def predict(image):
 interface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="filepath"),  # Use filepath to pass the image path
-    outputs="label",
+    outputs="text",  # Change output type to "text" for customized message
     title="Blood Cancer Detection",
     description="Upload an image to detect whether it is Normal or Cancerous."
 )
 
 if __name__ == "__main__":
-    interface.launch(server_port=7860, server_name="0.0.0.0", share=True)
+    interface.launch(server_port=7860, server_name="0.0.0.0", share=True)