Update app.py

app.py

@@ -1,18 +1,71 @@
+# 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
 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",
@@ -20,33 +73,26 @@ def predict(text):
             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
+            max_length=150,
+            min_length=30,
+            num_beams=4,
             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
+    fn=predict,
+    inputs="text",
+    outputs="text",
+    title="BART Summarization",
     description="Enter an article to generate a summary using a fine-tuned BART model."
 )
 
-# Launch the Gradio app
 interface.launch()