Create app.py

app.py

@@ -0,0 +1,62 @@
+import torch
+import gradio as gr
+from transformers import BartTokenizer, BartForConditionalGeneration
+
+# Define the BART model and tokenizer
+MODEL_NAME = 'VishnuPottabatthini/BART_demo'  # Change this to the model you want to use
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the tokenizer and model
+tokenizer = BartTokenizer.from_pretrained(MODEL_NAME)
+model = BartForConditionalGeneration.from_pretrained(MODEL_NAME).to(device)
+
+# Define the summarization function
+def summarize(text, state):
+    try:
+        # Tokenize the input text
+        inputs = tokenizer(
+            text,
+            return_tensors="pt",
+            truncation=True,
+            max_length=1024  # Adjust max length according to your model's capabilities
+        ).to(device)
+
+        # Generate the summary
+        summary_ids = model.generate(
+            inputs['input_ids'],
+            attention_mask=inputs['attention_mask'],
+            max_length=150,  # Maximum length of the summary
+            min_length=30,   # Minimum length of the summary
+            num_beams=4,     # Beam search to improve the quality of generated text
+            early_stopping=True
+        )
+
+        # Decode the summary
+        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+        return state + "\n" + summary, state + "\n" + summary
+
+    except Exception as e:
+        return str(e), state
+
+# Create the Gradio interface
+mf_summarize = gr.Interface(
+    fn=summarize,
+    inputs=[
+        gr.Textbox(placeholder="Enter text to summarize...", lines=10),
+        gr.State(value="")
+    ],
+    outputs=[
+        gr.Textbox(lines=15, label="Summary"),
+        gr.State()
+    ],
+    theme="huggingface",
+    title="BART Summarization",
+    live=True,
+    description=(
+        "Enter a long piece of text to generate a concise summary using a BART model. "
+        "This demo uses a custom BART model from 🤗 Transformers."
+    )
+)
+
+# Launch the Gradio interface
+mf_summarize.launch()