Create app.py

app.py

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+import gradio as gr
+from transformers import LEDForConditionalGeneration, LEDTokenizer
+import torch
+
+# Load the model and tokenizer
+model = LEDForConditionalGeneration.from_pretrained("./summary_generation_led_4")
+tokenizer = LEDTokenizer.from_pretrained("./summary_generation_led_4")
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+
+# Define the function for generating summaries
+def generate_summary(plot_synopsis):
+    inputs = tokenizer(plot_synopsis, max_length=3000, truncation=True, padding="max_length", return_tensors="pt")
+    inputs = inputs.to(device)
+    outputs = model.generate(inputs['input_ids'], max_length=315, min_length=20, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return summary
+
+# Create a Gradio interface
+interface = gr.Interface(fn=generate_summary,
+                         inputs=gr.Textbox(label="Plot Synopsis", lines=10, placeholder="Enter plot synopsis here..."),
+                         outputs=gr.Textbox(label="Plot Summary"),
+                         title="Plot Summary Generator",
+                         description="This demo generates a plot summary based on a plot synopsis using a fine-tuned LED model.")
+
+# Launch the interface
+interface.launch()