Update app.py

app.py

@@ -1,58 +1,3 @@
-# import gradio as gr
-# from transformers import BartTokenizer, BartForConditionalGeneration
-# import torch
-
-# # Load the fine-tuned BART model and tokenizer from the local directory
-# MODEL_DIR = './BART model small/model'
-# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# tokenizer = BartTokenizer.from_pretrained(MODEL_DIR)
-# model = BartForConditionalGeneration.from_pretrained(MODEL_DIR).to(device)
-
-# # Define the summarization function
-# def predict(text):
-#     try:
-#         # Tokenize the input article
-#         inputs = tokenizer(
-#             text,
-#             return_tensors="pt",
-#             max_length=1024,
-#             truncation=True
-#         ).to(device)
-
-#         # Generate the summary
-#         summary_ids = model.generate(
-#             inputs['input_ids'],
-#             attention_mask=inputs['attention_mask'],
-#             max_length=150,  # Set maximum length for the summary
-#             min_length=30,   # Set minimum length for the summary
-#             num_beams=4,     # Use beam search to generate the summary
-#             early_stopping=True
-#         )
-
-#         # Decode the summary
-#         summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-
-#         return summary
-    
-#     except Exception as e:
-#         return str(e)
-
-# # Create Gradio interface
-# # Textbox input for the article and output for the summary
-# interface = gr.Interface(
-#     fn=predict,          # The function to summarize the article
-#     inputs="text",         # Input is a text box where users can input the article text
-#     outputs="text",        # Output is a text box displaying the summary
-#     title="BART Summarization",  # The title of the app
-#     description="Enter an article to generate a summary using a fine-tuned BART model."
-# )
-
-# # Launch the Gradio app
-# interface.launch()
-
-
-
 
 import gradio as gr
 from transformers import BartTokenizer, BartForConditionalGeneration
@@ -64,7 +9,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 tokenizer = BartTokenizer.from_pretrained(MODEL_DIR)
 model = BartForConditionalGeneration.from_pretrained(MODEL_DIR).to(device)
 
-def predict(text):
+def summarize(text):
     try:
         inputs = tokenizer(
             text,
@@ -88,7 +33,7 @@ def predict(text):
         return str(e)
 
 interface = gr.Interface(
-    fn=predict,
+    fn=summarize,
     inputs="text",
     outputs="text",
     title="BART Summarization",