updating with proper gradio

app.py

@@ -1,7 +1,52 @@
+from transformers import LEDTokenizer, LEDForConditionalGeneration
+import torch
+import re
+tokenizer = LEDTokenizer.from_pretrained("patrickvonplaten/led-large-16384-pubmed")
+model = LEDForConditionalGeneration.from_pretrained("patrickvonplaten/led-large-16384-pubmed").to("cuda").half()
+
 import gradio as gr
+import os
+import docx2txt
+
+
+tokenizer = LEDTokenizer.from_pretrained("patrickvonplaten/led-large-16384-pubmed")
+model = LEDForConditionalGeneration.from_pretrained("patrickvonplaten/led-large-16384-pubmed", return_dict_in_generate=True).to("cuda")
+
+
+
+def summarize(text_file):
+    file_extension = os.path.splitext(text_file.name)[1]
+    if file_extension == ".txt":
+        # Load text from a txt file
+        with open(text_file.name, "r", encoding="utf-8") as f:
+            text = f.read()
+    elif file_extension == ".docx":
+        # Load text from a Word file
+        text = docx2txt.process(text_file.name)
+    else:
+        raise ValueError(f"Unsupported file type: {file_extension}")
+    
+    input_ids = tokenizer(text, return_tensors="pt").input_ids.to("cuda")
+    global_attention_mask = torch.zeros_like(input_ids)
+    # set global_attention_mask on first token
+    global_attention_mask[:, 0] = 1
+
+    sequences = model.generate(input_ids, global_attention_mask=global_attention_mask).sequences
+
+    summary = tokenizer.batch_decode(sequences)[0]
+
+
+    
+    return text, summary
+
+
 
-def greet(name):
-    return "Hello " + name + "!!"
+iface = gr.Interface(
+    fn=summarize,
+    inputs=gr.inputs.File(label="Upload a txt file or a Word file for the input text"),
+    outputs=[gr.outputs.Textbox(label="Original text"), gr.outputs.Textbox(label="Summary")],
+    title="Academic Paper Summarization Demo",
+    description="Upload a txt file or a Word file for the input text. Get a summary generated by a small T5 model from Hugging Face.",
+)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+iface.launch()