Update app.py

app.py

@@ -9,8 +9,31 @@ import torch
 from transformers import GPT2Tokenizer, GPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling
 from transformers import Trainer, TrainingArguments
 
+__checkpoint = "gpt2"
+__tokenizer = GPT2Tokenizer.from_pretrained(__checkpoint)
+__model = GPT2LMHeadModel.from_pretrained(__checkpoint)
+# Create a Data collator object
+data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="pt")
 def queryGPT(question):
-    return "<question>"+question+"<Answer>Test"
+    return generate_response(__model, __tokenizer, question)
+
+def generate_response(__model, __tokenizer, prompt, max_length=200):
+
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")      # 'pt' for returning pytorch tensor
+
+    # 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_length=max_length,
+        num_return_sequences=1,
+        attention_mask=attention_mask,
+        pad_token_id=pad_token_id
+    )
+
+    return __tokenizer.decode(output[0], skip_special_tokens=True)
     
 with gr.Blocks() as demo: