Rename app1.py to app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+checkpoint = "gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(checkpoint)
+
+
+# Load the fine-tuned model and tokenizer
+my_model = GPT2LMHeadModel.from_pretrained("jeevana/EmailSubjectLineGeneration")
+my_tokenizer = GPT2Tokenizer.from_pretrained("jeevana/EmailSubjectLineGeneration")
+
+def generate_response(model, tokenizer, prompt):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt",truncation=True, max_length=1000)
+    # Create the attention mask and pad token id
+    attention_mask = torch.ones_like(input_ids)
+    pad_token_id = tokenizer.eos_token_id
+
+    output = model.generate(
+        input_ids,
+        max_new_tokens=15,
+        min_new_tokens = 1,
+        num_return_sequences=1,
+        attention_mask=attention_mask,
+        pad_token_id=pad_token_id
+    )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+def predict(input):
+    print("pipeline object", pipeline)
+    prediction = generate_response(my_model, my_tokenizer, input)
+    print("type of response:", type(input))
+    return prediction 
+
+
+app = gr.Interface(fn=predict, inputs=[gr.Textbox(label="Email", lines=12)],
+                    outputs=[gr.Textbox(label="Subject", lines=3)],
+                    title="EmailSubjectLineGeneration",
+                    description="EmailSubjectLineGeneration"
+                   )
+app.launch(share=True, debug=True)
+
+

app1.py

@@ -1,14 +0,0 @@
-import gradio as gr
-import torch
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
-checkpoint = "gpt2"
-tokenizer = GPT2Tokenizer.from_pretrained(checkpoint)
-
-
-# Load the fine-tuned model and tokenizer
-my_model = GPT2LMHeadModel.from_pretrained("jeevana/GenerativeQnASystem")
-my_tokenizer = GPT2Tokenizer.from_pretrained("jeevana/GenerativeQnASystem")
-
-
-