Update app.py

app.py

@@ -1,64 +1,184 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import json      
+import numpy as np      
+import tensorflow as tf      
+from tensorflow.keras import layers      
+import gradio as gr      
+import re    
+import requests    
+import math    
+import sentencepiece as spm
+
+# SentencePiece 로드 (토크나이저랑 특수 토큰 ID도 동일하게 세팅)
+sp = spm.SentencePieceProcessor()
+sp.load("ko_unigram3.model")
+
+pad_id = sp.piece_to_id("<pad>")
+if pad_id == -1: pad_id = 0
+start_id = sp.piece_to_id("<start>")
+if start_id == -1: start_id = 1
+end_id = sp.piece_to_id("< end >")
+if end_id == -1: end_id = 2
+unk_id = sp.piece_to_id("<unk>")
+if unk_id == -1: unk_id = 3
+
+vocab_size = sp.get_piece_size()
+max_len = 128
+
+def text_to_ids(text):
+    return sp.encode(text, out_type=int)
+
+def ids_to_text(ids):
+    return sp.decode(ids)
+
+# GEGLU 레이어
+class GEGLU(tf.keras.layers.Layer):
+    def __init__(self, d_model, d_ff):
+        super().__init__()
+        self.proj = layers.Dense(d_ff * 2)
+        self.out = layers.Dense(d_model)
+    def call(self, x):
+        x_proj = self.proj(x)
+        x_val, x_gate = tf.split(x_proj, 2, axis=-1)
+        return self.out(x_val * tf.nn.gelu(x_gate))
+
+# GPT 블록
+class GPTBlock(tf.keras.layers.Layer):
+    def __init__(self, d_model, d_ff, num_heads=16, dropout_rate=0.1):
+        super().__init__()
+        self.ln1 = layers.LayerNormalization(epsilon=1e-5)
+        self.attn = layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model // num_heads)
+        self.dropout1 = layers.Dropout(dropout_rate)
+        self.ln2 = layers.LayerNormalization(epsilon=1e-5)
+        self.ffn = GEGLU(d_model, d_ff)
+        self.dropout2 = layers.Dropout(dropout_rate)
+    def call(self, x, training=False):
+        x_norm = self.ln1(x)
+        attn_out = self.attn(query=x_norm, value=x_norm, key=x_norm,
+                             use_causal_mask=True, training=training)
+        x = x + self.dropout1(attn_out, training=training)
+        ffn_out = self.ffn(self.ln2(x))
+        x = x + self.dropout2(ffn_out, training=training)
+        return x
+
+# GPT 모델
+class GPT(tf.keras.Model):
+    def __init__(self, vocab_size, seq_len, d_model, d_ff, n_layers, num_heads=16, dropout_rate=0.1):
+        super().__init__()
+        self.token_embedding = layers.Embedding(vocab_size, d_model)
+        self.pos_embedding = self.add_weight(
+            name="pos_embedding",
+            shape=[seq_len, d_model],
+            initializer=tf.keras.initializers.RandomNormal(stddev=0.01)
+        )
+        self.blocks = [GPTBlock(d_model, d_ff, num_heads, dropout_rate) for _ in range(n_layers)]
+        self.ln_f = layers.LayerNormalization(epsilon=1e-5)
+    def call(self, x, training=False):
+        seq_len = tf.shape(x)[1]
+        x = self.token_embedding(x) + self.pos_embedding[tf.newaxis, :seq_len, :]
+        for block in self.blocks:
+            x = block(x, training=training)
+        x = self.ln_f(x)
+        logits = tf.matmul(x, self.token_embedding.embeddings, transpose_b=True)
+        return logits
+
+# 모델 생성 & 가중치 불러오기
+model = GPT(vocab_size=vocab_size, seq_len=max_len, d_model=128, d_ff=512, n_layers=6)
+dummy_input = tf.zeros((1, max_len), dtype=tf.int32)  # 배치1, 시퀀스길이 max_len
+_ = model(dummy_input)  # 모델이 빌드됨
+model.load_weights("KeraLux3.weights.h5")
+print("모델 가중치 로드 완료!")
+
+def decode_sp_tokens(tokens):
+    text = ''.join(tokens).replace('▁', ' ').strip()
+    return text
+
+def generate_text_topkp_stream(model, prompt, max_len=100, max_gen=98, p=0.9, k=50, temperature=0.8, min_len=20):
+    model_input = text_to_ids(f"<start> {prompt}")
+    model_input = model_input[:max_len]
+    generated = list(model_input)
+    text_so_far = []
+
+    for step in range(max_gen):
+        pad_length = max(0, max_len - len(generated))
+        input_padded = np.pad(generated, (0, pad_length), constant_values=pad_id)
+        input_tensor = tf.convert_to_tensor([input_padded])
+        logits = model(input_tensor, training=False)
+        next_token_logits = logits[0, len(generated) - 1].numpy()
+
+        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
+
+        # 1. 확률 계산
+        probs = tf.nn.softmax(logits_temp).numpy()
+
+        # 2. Top-k 필터링
+        top_k_indices = np.argpartition(probs, -k)[-k:]
+        top_k_probs = probs[top_k_indices]
+
+        # 3. Top-p 필터링 (누적합 계산용 정렬)
+        sorted_idx = np.argsort(top_k_probs)[::-1]
+        top_k_indices = top_k_indices[sorted_idx]
+        top_k_probs = top_k_probs[sorted_idx]
+        cumulative_probs = np.cumsum(top_k_probs)
+
+        # p 넘는 부분 자르기
+        cutoff = np.searchsorted(cumulative_probs, p, side='right') + 1
+
+        filtered_indices = top_k_indices[:cutoff]
+        filtered_probs = top_k_probs[:cutoff]
+
+        # 확률 정규화
+        filtered_probs /= filtered_probs.sum()
+
+        # 샘플링
+        next_token_id = np.random.choice(filtered_indices, p=filtered_probs)
+
+        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)
+
+        if len(generated) >= min_len and next_token_id == end_id:
+            break
+        if len(generated) >= min_len and decoded_text.endswith(('.', '!', '?')):
+            break
+
+        yield decoded_text
+
+def chat(user_input, history):      
+    if history is None:      
+        history = []      
+
+    for partial_response in generate_text_topkp_stream(model, user_input, p=0.9):      
+        yield history + [(user_input, partial_response)], history + [(user_input, partial_response)]
+
+with gr.Blocks(title="KeraLux Chat") as demo:      
+    gr.Markdown(      
+        """      
+        # 💡 KeraLux와 대화해보세요!      
+        대화를 입력하면 KeraLux가 똑똑하게 대답해줄 거예요.      
+        """,      
+        elem_id="title",      
+    )      
+    gr.Markdown("---")      
+      
+    with gr.Row():      
+        with gr.Column(scale=1):      
+            chatbot = gr.Chatbot(label="KeraLux 채팅창", bubble_full_width=False)      
+        with gr.Column(scale=0):      
+            msg = gr.Textbox(      
+                label="당신의 질문을 입력하세요!",      
+                placeholder="ex) 나 좀 도와줄 수 있니?",      
+                lines=1,      
+            )      
+            state = gr.State([])      
+      
+    msg.submit(chat, inputs=[msg, state], outputs=[chatbot, state])      
+    msg.submit(lambda: "", None, msg)  # 입력창 초기화      
+      
+demo.launch(share=True)