Update pages/Text-to-Text.py

pages/Text-to-Text.py

@@ -51,7 +51,7 @@ else:
 
 
 length = form.number_input("Select how long you want the generated text to be", value = 100)
-number_of_tokens_to_sample = form.number_input("Select how many tokens we want to search through when we do the filtering", value = int(25000))
+number_of_tokens_to_sample = form.number_input("Select how many tokens we want to search through when we do the filtering", value = 25000)
 form.caption("Settings this to higher numbers will improve the experience but will cause generating to slow. Low numbers may cause lots of blank or failed generations")
 temperature = form.number_input("How spicy/interesting do we want our models output to be", value = 0.10, min_value = 0.0)
 form.caption("Setting this higher decreases the likelihood of high probability words and increases the likelihood of low probability (and presumably more interesting) words")
@@ -90,10 +90,10 @@ def get_next_word_without_e():
     if temperature != 1.0:
         next_token_candidates_logits = next_token_candidates_logits / temperature
     # filter
-    filtered_next_token_candidates_logits = top_k_top_p_filtering(next_token_candidates_logits, top_k=number_of_tokens_to_sample, top_p=number_of_tokens_to_sample)
+    filtered_next_token_candidates_logits = top_k_top_p_filtering(next_token_candidates_logits, top_k=int(number_of_tokens_to_sample), top_p=int(number_of_tokens_to_sample))
     # sample and get a probability distribution
     probs = F.softmax(filtered_next_token_candidates_logits, dim=-1)
-    next_token_candidates = torch.multinomial(probs, num_samples=number_of_tokens_to_sample) ## 10000 random samples
+    next_token_candidates = torch.multinomial(probs, num_samples=int(number_of_tokens_to_sample)) ## 10000 random samples
     word_list = []
     for candidate_string in next_token_candidates:
         for candidate in candidate_string: