Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import BartTokenizer, BartForConditionalGeneration
+
+# Load the model and tokenizer from Hugging Face hub
+model_name = "iimran/SAM-TheSummariserV2"
+tokenizer = BartTokenizer.from_pretrained(model_name)
+model = BartForConditionalGeneration.from_pretrained(model_name)
+model.eval()  # Set the model to evaluation mode
+
+# Function to summarize the input text
+def summarize(input_text):
+    # Tokenize the input text with truncation (adjust max_length as needed)
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=1024)
+
+    # Create global attention mask: assign global attention to the first token (required by LED)
+    global_attention_mask = torch.zeros(inputs["input_ids"].shape, dtype=torch.long)
+    global_attention_mask[:, 0] = 1
+
+    # Generate the summary using beam search (you can adjust parameters as needed)
+    summary_ids = model.generate(
+        inputs["input_ids"],
+        attention_mask=inputs["attention_mask"],
+        global_attention_mask=global_attention_mask,
+        max_length=512,
+        num_beams=4,
+        early_stopping=True,
+    )
+
+    # Decode the generated ids to a summary string, skipping special tokens
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Create a Gradio interface with a title, description, submit button, and larger input text area
+iface = gr.Interface(
+    fn=summarize,  # Function that handles the summarization
+    inputs=gr.Textbox(
+        label="Enter Text to Summarize",
+        lines=10,  # Make the input area larger by increasing the number of lines
+        placeholder="Paste or type the text you want to summarize here...",
+    ),
+    outputs=gr.Textbox(
+        label="Summary", 
+        lines=5,  # Adjust output area size (number of lines)
+        placeholder="Summary will appear here..."
+    ),
+    live=False,  # Disable live updates, use the submit button instead
+    allow_flagging="never",  # Optionally disable flagging
+    title="SAM - The Summariser",  # Title of the page
+    description="SAM is a model which will help summarize large knowledge base articles into small summaries.",  # Description of the model
+)
+
+# Launch the interface
+iface.launch()