Upload app

app.py

@@ -0,0 +1,44 @@
+from transformers import LongT5ForConditionalGeneration, AutoTokenizer
+import time
+
+N = 2 # Number of previous QA pairs to use for context
+MAX_NEW_TOKENS = 128 # Maximum number of tokens for each answer
+
+tokenizer = AutoTokenizer.from_pretrained("tryolabs/long-t5-tglobal-base-blogpost-cqa")
+model = LongT5ForConditionalGeneration.from_pretrained("tryolabs/long-t5-tglobal-base-blogpost-cqa")
+
+with open("context_short.txt", "r") as f:
+ context = f.read()
+
+def build_input(question, user_history=[], bot_history=[]):
+ model_input = f"{context} || "
+ previous = min(len(bot_history[1:]), N)
+ for i in range(previous, 0, -1):
+ prev_question = user_history[-i-1]
+ prev_answer = bot_history[-i]
+ model_input += f"<Q{i}> {prev_question} <A{i}> {prev_answer} "
+ model_input += f"<Q> {question} <A> "
+ return model_input
+
+def get_model_answer(question, user_history=[], bot_history=[]):
+ start = time.perf_counter()
+ model_input = build_input(question, user_history, bot_history)
+ end = time.perf_counter()
+ print(f"Build input: {end-start}")
+ start = time.perf_counter()
+ encoded_inputs = tokenizer(model_input, max_length=3000, truncation=True, return_tensors="pt")
+ input_ids, attention_mask = (
+ encoded_inputs.input_ids,
+ encoded_inputs.attention_mask
+ )
+ end = time.perf_counter()
+ print(f"Tokenize: {end-start}")
+ start = time.perf_counter()
+ encoded_output = model.generate(input_ids=input_ids, attention_mask=attention_mask, do_sample=True, max_new_tokens=MAX_NEW_TOKENS)
+ answer = tokenizer.decode(encoded_output[0], skip_special_tokens=True)
+ end = time.perf_counter()
+ print(f"Generate: {end-start}")
+ user_history.append(question)
+ bot_history.append(answer)
+ return answer, user_history, bot_history
+

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+torch