Update app.py

app.py

@@ -1,22 +1,25 @@
 import gradio as gr
 from transformers import pipeline
+from datasets import load_dataset
 
 classifier = pipeline("text-classification", model="CesarLeblanc/test_model")
+dataset = load_dataset("CesarLeblanc/text_classification_dataset")
 
 def text_classification(text):
     result = classifier(text)
     habitat_label = result[0]['label']
+    habitat_label = dataset['train'].features['label'].names[int(habitat_label.split('_')[1])]
     habitat_score = result[0]['score']
-    formatted_output = f"This sentiment is {habitat_label} with the probability {habitat_score*100:.2f}%"
+    formatted_output = f"This vegetation plot is {habitat_label} with the probability {habitat_score*100:.2f}%"
     return formatted_output
 
-examples=["Vegetation Plot 1", "Vegetation Plot 2"]
+examples=["quercus robur, betula pendula, holcus lanatus, lonicera periclymenum, carex arenaria, poa trivialis", "thinopyrum junceum, cakile maritima"]
 
 io = gr.Interface(fn=text_classification, 
-                         inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter title here..."), 
+                         inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter species here..."), 
                          outputs=gr.Textbox(lines=2, label="Text Classification Result"),
-                         title="Text Classification",
-                         description="Enter a text and see the text classification result!",
+                         title="Vegetation Plot Classification",
+                         description="Enter the species and see the vegetation plot classification result!",
                          examples=examples)
 
-io.launch()
+io.launch()