Update app.py

app.py

@@ -1,7 +1,8 @@
 import gradio as gr
 import torch
 from transformers import AutoFeatureExtractor, AutoModelForImageClassification
-from PIL import Image
+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
 
 class ThyroidTumorClassifierApp:
     def __init__(self):
@@ -31,17 +32,45 @@ class ThyroidTumorClassifierApp:
         # Predicted class label
         predicted_label = class_labels[predicted_class]
         
-        # Return the predicted label and probability as plain text
-        return predicted_label, probabilities[0].tolist()
+        # Add information to the output image
+        output_image_with_info = self.add_info_to_image(image, predicted_label, probabilities)
+        
+        # Return the modified output image as an array
+        return output_image_with_info
+
+    def add_info_to_image(self, image, predicted_label, probabilities):
+        # Convert the image to Pillow image format
+        image_pil = Image.fromarray(image)
+        
+        # Create a drawing object to add text to the image
+        draw = ImageDraw.Draw(image_pil)
+        
+        # Choose the font and text size
+        font = ImageFont.truetype("arial.ttf", 20)
+        
+        # Add the predicted class label to the image
+        draw.text((10, 10), f"Classe Prevista: {predicted_label}", fill="white", font=font)
+        
+        # Add the probabilities to the image
+        for i, prob in enumerate(probabilities):
+            y_offset = 40 + i * 30
+            class_name = f"Classe {i}:"
+            probability = f"{prob:.2f}"
+            draw.text((10, y_offset), f"{class_name} {probability}", fill="white", font=font)
+        
+        # Convert back to numpy format
+        image_with_info = np.array(image_pil)
+        
+        return image_with_info
 
     def run_interface(self):
         # Create a Gradio interface
         input_interface = gr.Interface(
             fn=self.classify_image,
             inputs=gr.inputs.Image(),
-            outputs=["text", "text"],
+            outputs=gr.outputs.Image(),
             title="Tumor da Tireoide Classificação",
-            description="Faça o upload de uma imagem de um tumor da tireoide para classificação. A saída inclui o rótulo da classe prevista e as probabilidades.",
+            description="Faça o upload de uma imagem de um tumor da tireoide para classificação. A saída inclui o rótulo da classe prevista e as probabilidades com informações adicionais.",
         )
 
         # Launch the Gradio interface