modified the app.py

app.py

@@ -1,19 +1,25 @@
 import gradio as gr
-from transformers import AutoModelForImageClassification, AutoFeatureExtractor, pipeline
+from transformers import AutoModelForImageClassification, AutoImageProcessor, pipeline
 
+# Carregar o modelo e o processador de imagens
 model = AutoModelForImageClassification.from_pretrained("mestrevh/computer-vision-beans", use_safetensors=True)
-feature_extractor = AutoFeatureExtractor.from_pretrained("mestrevh/computer-vision-beans")
+image_processor = AutoImageProcessor.from_pretrained("mestrevh/computer-vision-beans")
 
-classifier = pipeline("image-classification", model=model, feature_extractor=feature_extractor)
+# Criar o pipeline
+classifier = pipeline("image-classification", model=model, feature_extractor=image_processor)
 
 # Função de classificação
 def predict_image(image):
-    return classifier(image)
+    # A saída do classifier é uma lista de dicionários, pegar o label e a confiança
+    result = classifier(image)
+    label = result[0]['label']
+    confidence = result[0]['score']
+    return f"Class: {label}, Confidence: {confidence:.2f}"
 
 # Interface Gradio
 interface = gr.Interface(fn=predict_image, 
-                        inputs = gr.Image(type="pil"), 
-                         outputs="label", 
+                         inputs=gr.Image(type="pil"), 
+                         outputs="text", 
                          live=True)
 
 interface.launch()