Update app.py

app.py

@@ -163,7 +163,7 @@ def decode_sp_tokens(tokens):
     text = ''.join(tokens).replace('▁', ' ').strip()
     return text
 
-def generate_text_topp_stream(model, prompt, max_len=100, max_gen=98, p=0.9, temperature=0.8, min_len=20):
+def generate_text_topp(model, prompt, max_len=100, max_gen=98, p=0.9, temperature=0.8, min_len=20):
     model_input = text_to_ids(f"<start> {prompt}")
     model_input = model_input[:max_len]
     generated = list(model_input)
@@ -176,43 +176,36 @@ def generate_text_topp_stream(model, prompt, max_len=100, max_gen=98, p=0.9, tem
         logits = model(input_tensor, training=False)
         next_token_logits = logits[0, len(generated) - 1].numpy()
 
-        # 특정 토큰들 확률 낮춤
         if len(generated) >= min_len:
             next_token_logits[end_id] -= 5.0
         next_token_logits[pad_id] -= 10.0
 
-        # 온도 적용
         logits_temp = next_token_logits / temperature
         probs = tf.nn.softmax(logits_temp).numpy()
 
-        # 확률 내림차순 정렬
         sorted_idx = np.argsort(probs)[::-1]
         sorted_probs = probs[sorted_idx]
         cumulative_probs = np.cumsum(sorted_probs)
 
-        # 누적합이 p 넘는 위치까지만 선택
         cutoff = np.searchsorted(cumulative_probs, p, side='right') + 1
         filtered_indices = sorted_idx[:cutoff]
         filtered_probs = sorted_probs[:cutoff]
         filtered_probs /= filtered_probs.sum()
 
-        # 샘플링
         next_token_id = np.random.choice(filtered_indices, p=filtered_probs)
 
-        # 결과 누적
         generated.append(int(next_token_id))
         next_word = sp.id_to_piece(int(next_token_id))
         text_so_far.append(next_word)
 
         decoded_text = decode_sp_tokens(text_so_far)
 
-        # 정지 조건
         if len(generated) >= min_len and next_token_id == end_id:
             break
         if len(generated) >= min_len and decoded_text.endswith(('.', '!', '?')):
             break
 
-        return decoded_text
+    return decoded_text
 
 def respond(input_text):
     if "이름" in input_text:
@@ -220,10 +213,7 @@ def respond(input_text):
     if "누구" in input_text:
         return "저는 KeraLux라고 해요."
 
-    # 제너레이터 결과를 모두 이어붙임
-    response = ''.join(generate_text_topp_stream(model, input_text))
-    return response
-
+    return generate_text_topp(model, input_text)
 
 @app.get("/generate", response_class=PlainTextResponse)
 async def generate(request: Request):