Added description for bloom

app.py

@@ -25,6 +25,7 @@ model_DialoGPT_large = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-
 
 def predict_common_bloom(model, tokenizer, input_text, history, person_description, number_of_new_tokens):
     new_user_input_ids = tokenizer.encode(input_text + '\n', return_tensors='pt')
+    person_description_ids = tokenizer.encode(person_description + '\n', return_tensors='pt')
     print('Started predict_common_bloom')
     print(f'history: {history}')
     if history != []:
@@ -32,13 +33,15 @@ def predict_common_bloom(model, tokenizer, input_text, history, person_descripti
     else:
         bot_input_ids = new_user_input_ids
     print(f'bot_input_ids: {bot_input_ids}')
+    input_with_desc_ids = torch.cat([person_description_ids, bot_input_ids], dim=-1)
 
     history = model.generate(
-        bot_input_ids,
+        input_with_desc_ids,
         max_new_tokens=number_of_new_tokens,
         pad_token_id=tokenizer.eos_token_id
     ).tolist()
     print(f'history: {history}')
+    history[0] = history[0][len(person_description_ids[0]):]
 
     decode_all = tokenizer.decode(history[0][:len(bot_input_ids[0])])
     all_responses = tokenizer.decode(history[0][len(bot_input_ids[0]):]).split('\n')
@@ -128,6 +131,7 @@ gr.Interface(
                 'DialoGPT-medium',
                 'DialoGPT-large',
                 'bloom-petals',
+                'bloom-petals-cluster',
             ]
         ),
         gr.Radio(