update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-import csv 
+import csv
 
 MODEL_URL = "https://huggingface.co/dsfsi/PuoBERTa-News"
 WEBSITE_URL = "https://www.kodiks.com/ai_solutions.html"
@@ -22,18 +22,20 @@ categories = {
 }
 
 def prediction(news):
-    clasifer = pipeline("text-classification", tokenizer=tokenizer, model=model, return_all_scores=True)
-    preds = clasifer(news)
+    classifier = pipeline("text-classification", tokenizer=tokenizer, model=model, return_all_scores=True)
+    preds = classifier(news)
     preds_dict = {categories.get(pred['label'], pred['label']): pred['score'] for pred in preds[0]}
     return preds_dict
 
 def file_prediction(file):
+    news_list = []
+
     if file.name.endswith('.csv'):
-        file.seek(0)  
+        file.seek(0)
         reader = csv.reader(file.read().decode('utf-8').splitlines())
-        news_list = [row[0] for row in reader if row] 
+        news_list = [row[0] for row in reader if row]
     else:
-        file.seek(0)  
+        file.seek(0)
         file_content = file.read().decode('utf-8')
         news_list = file_content.splitlines()
 
@@ -41,9 +43,9 @@ def file_prediction(file):
     for news in news_list:
         if news.strip():
             pred = prediction(news)
-            results.append([news, pred])
+            results.append([news, pred])  # Return each news and its prediction as a row
 
-    return results
+    return results  # Gradio expects a list of lists or dicts for DataFrame
 
 gradio_ui = gr.Interface(
     fn=prediction,
@@ -67,4 +69,3 @@ gradio_file_ui = gr.Interface(
 gradio_combined_ui = gr.TabbedInterface([gradio_ui, gradio_file_ui], ["Text Input", "File Upload"])
 
 gradio_combined_ui.launch()
-