Update app.py

app.py

@@ -2,18 +2,40 @@ 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")
 
-# def generative_qna(input):
-#   response = generate_response(my_model, my_tokenizer, input)
-#   return response
+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=70,
+        min_new_tokens = 1,
+        num_return_sequences=1,
+        attention_mask=attention_mask,
+        pad_token_id=pad_token_id
+    )
+    qna = tokenizer.decode(output[0], skip_special_tokens=True)
+    answer = qna[len(prompt)+9: ]
+    return answer
+
 
 def generative_qna(input):
-    print(input)
-    return input
+  response = generate_response(my_model, my_tokenizer, input)
+  return response
+
+# def generative_qna(input):
+#     print(input)
+#     return input
 
 app = gr.Interface(fn=generative_qna, inputs=[gr.Textbox(label="Question", lines=3)],
                     outputs=[gr.Textbox(label="Answer", lines=6)],