Gradio app file

app.py

@@ -0,0 +1,35 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+nltk.download('punkt')
+
+tokenizer = AutoTokenizer.from_pretrained("anukvma/bart-base-medium-email-subject-generation-v5")
+model = AutoModelForSeq2SeqLM.from_pretrained("anukvma/bart-base-medium-email-subject-generation-v5")
+
+text = """
+Harry - I got kicked out of the system, so I'm sending this from Tom's account.
+He can fill you in on the potential deal with STEAG.
+I left my resume on your chair.
+I'll e-mail a copy when I have my home account running.
+My contact info is:
+"""
+
+inputs = ["provide email subject: " + text]
+
+inputs = tokenizer(inputs, max_length=512, truncation=True, return_tensors="pt")
+output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=1, max_length=10)
+decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+predicted_title = nltk.sent_tokenize(decoded_output.strip())[0]
+
+print(predicted_title)
+
+def generate_subject(text):
+  inputs = ["provide email subject: " + text]
+
+  inputs = tokenizer(inputs, max_length=512, truncation=True, return_tensors="pt")
+  output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=1, max_length=10)
+  decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+  predicted_title = nltk.sent_tokenize(decoded_output.strip())[0]
+  return predicted_title
+
+import gradio as gr
+gr.Interface(fn = generate_subject, inputs="text",outputs="text").launch()