Update app.py

app.py

@@ -3,6 +3,7 @@ import efficientnet.tfkeras as efn
 from tensorflow.keras.layers import Input, GlobalAveragePooling2D, Dense
 import numpy as np
 import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
 
 # Dimensões da imagem
 IMG_HEIGHT = 224
@@ -66,15 +67,32 @@ def predict_image(input_image):
     # For example:
     # formatted_text = f"Predicted Class: {predicted_class}\nProbability: {probability:.2%}\nObject Detection: {bounding_box_coordinates}"
 
-    # Return the formatted result
-    formatted_text = f"Predicted Class: {predicted_class}\nProbability: {probability:.2%}"
-    return formatted_text
+    # Create an output image with object detection
+    output_image = input_image  # Replace this with your object detection visualization
+
+    # Convert the output image to bytes
+    output_image_bytes = Image.fromarray(np.uint8(output_image * 255))
+
+    # Create an image with the label "Normal" or "Cataract" outside the image
+    draw = ImageDraw.Draw(output_image_bytes)
+    font = ImageFont.load_default()  # You can customize the font and size here
+    label_text = f"Predicted Class: {predicted_class}"
+    label_size = draw.textsize(label_text, font=font)
+    label_position = (10, 10)  # You can adjust the label position
+    draw.rectangle([label_position, (label_position[0] + label_size[0], label_position[1] + label_size[1])], fill="white")
+    draw.text(label_position, label_text, fill="black", font=font)
+
+    # Convert the image with the label to bytes
+    labeled_image_bytes = output_image_bytes.tobytes()
+
+    # Return both the image with object detection and the labeled image
+    return [labeled_image_bytes, f"Predicted Class: {predicted_class}"]
 
 # Crie uma interface Gradio para fazer previsões
 iface = gr.Interface(
     fn=predict_image,
-    inputs="image",
-    outputs="text",
+    inputs=gr.inputs.Image(label="Upload an Image", type="file"),
+    outputs=[gr.outputs.Image(type="pil"), gr.outputs.Text(label="Prediction", type="markdown")],
     interpretation="default"
 )