Updated the gradio app interface

app.py

@@ -1,35 +1,25 @@
-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()
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+nltk.download('punkt')
+
+def generate_subject(model_name,email_body):
+  tokenizer = AutoTokenizer.from_pretrained(model_name)
+  model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+  inputs = ["provide email subject: " + email_body]
+  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
+iface = gr.Interface(
+    fn=generate_subject,
+    inputs=[
+        gr.Dropdown(choices=["anukvma/t5-base-medium-email-subject-generation-v2", "anukvma/bart-base-medium-email-subject-generation-v5"], label="Select Model"),
+        gr.Textbox(lines=5, label="Email Body")
+    ],
+    outputs=gr.Textbox(label="Email Summary")
+)
+
+iface.launch()