add app

README.md

@@ -1,8 +1,8 @@
 ---
-title: Kogpt Samhaengshi
-emoji: 🌍
-colorFrom: blue
-colorTo: pink
+title: Kogpt Joong 2 Hakgyo
+emoji: 🐢
+colorFrom: yellow
+colorTo: yellow
 sdk: streamlit
 app_file: app.py
 pinned: false

app.py

@@ -0,0 +1,172 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import streamlit as st
+from transformers import AutoModelWithLMHead, PreTrainedTokenizerFast
+
+
+model_dir = "snoop2head/kogpt-conditional-2"
+tokenizer = PreTrainedTokenizerFast.from_pretrained(
+    model_dir,
+    bos_token="<s>",
+    eos_token="</s>",
+    unk_token="<unk>",
+    pad_token="<pad>",
+    mask_token="<mask>",
+)
+
+
+@st.cache
+def load_model(model_name):
+    model = AutoModelWithLMHead.from_pretrained(model_name)
+    return model
+
+
+model = load_model(model_dir)
+
+
+def find_nth(haystack, needle, n):
+    start = haystack.find(needle)
+    while start >= 0 and n > 1:
+        start = haystack.find(needle, start + len(needle))
+        n -= 1
+    return start
+
+
+def infer(input_ids, max_length, temperature, top_k, top_p):
+    output_sequences = model.generate(
+        input_ids=input_ids,
+        max_length=max_length,
+        temperature=temperature,
+        top_k=top_k,
+        top_p=top_p,
+        do_sample=True,
+        num_return_sequences=1,
+    )
+    return output_sequences
+
+
+# prompts
+st.title("삼행시의 달인 KoGPT입니다 🦄")
+st.write("텍스트를 입력하고 CTRL+Enter(CMD+Enter)을 누르세요 🤗")
+
+# text and sidebars
+default_value = "박수민"
+sent = st.text_area("Text", default_value, max_chars=4, height=275)
+max_length = st.sidebar.slider("생성 문장 길이를 선택해주세요!", min_value=42, max_value=64)
+temperature = st.sidebar.slider(
+    "Temperature", value=1.0, min_value=0.0, max_value=1.0, step=0.05
+)
+top_k = st.sidebar.slider("Top-k", min_value=0, max_value=5, value=0)
+top_p = st.sidebar.slider("Top-p", min_value=0.0, max_value=1.0, step=0.05, value=0.9)
+
+print("slider sidebars rendering completed")
+
+# make input sentence
+emotion_list = ["행복", "중립", "분노", "혐오", "놀람", "슬픔", "공포"]
+main_emotion = st.sidebar.radio("주요 감정을 선택하세요", emotion_list)
+sub_emotion = st.sidebar.radio("두 번째 감정을 선택하세요", emotion_list)
+
+print("radio sidebars rendering completed")
+
+# create condition sentence
+random_main_logit = np.random.normal(loc=3.368, scale=1.015, size=1)[0].round(1)
+random_sub_logit = np.random.normal(loc=1.333, scale=0.790, size=1)[0].round(1)
+condition_sentence = f"{random_main_logit}만큼 {main_emotion}감정인 문장이다. {random_sub_logit}만큼 {sub_emotion}감정인 문장이다. "
+condition_plus_input = condition_sentence + sent
+print(condition_plus_input)
+
+
+def infer_sentence(
+    condition_plus_input=condition_plus_input, tokenizer=tokenizer, top_k=3
+):
+    encoded_prompt = tokenizer.encode(
+        condition_plus_input, add_special_tokens=False, return_tensors="pt"
+    )
+    if encoded_prompt.size()[-1] == 0:
+        input_ids = None
+    else:
+        input_ids = encoded_prompt
+    output_sequences = infer(input_ids, max_length, temperature, top_k, top_p)
+    print(output_sequences)
+
+    # exclude item that contains "unk"
+    output_sequences = [
+        output_sequence
+        for output_sequence in output_sequences
+        if "unk" not in output_sequence
+    ]
+    # choose item that length is longer than 1
+    output_sequences = [
+        output_sequence
+        for output_sequence in output_sequences
+        if len(output_sequence) > 1
+    ]
+    generated_sequence = output_sequences[0]
+    print(generated_sequence)
+
+    # print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
+    # generated_sequences = generated_sequence.tolist()
+    # Decode text
+    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
+    print(text)
+    # Remove all text after the stop token
+    stop_token = tokenizer.pad_token
+    print(stop_token)
+    text = text[: text.find(stop_token) if stop_token else None]
+    print(text)
+
+    condition_index = find_nth(text, "문장이다", 2)
+    text = text[condition_index + 5 :]
+    text = text.strip()
+    return text
+
+
+def make_residual_conditional_samhaengshi(input_letter, condition_sentence):
+    # make letter string into
+    list_samhaengshi = []
+
+    # initializing text and index for iteration purpose
+    index = 0
+
+    # iterating over the input letter string
+    for index, letter_item in enumerate(input_letter):
+        # initializing the input_letter
+        if index == 0:
+            residual_text = letter_item
+            # print('residual_text:', residual_text)
+
+        # infer and add to the output
+        conditional_input = f"{condition_sentence} {residual_text}"
+        inferred_sentence = infer_sentence(conditional_input, tokenizer)
+        if index != 0:
+            # remove previous sentence from the output
+            print("inferred_sentence:", inferred_sentence)
+            inferred_sentence = inferred_sentence.replace(
+                list_samhaengshi[index - 1], ""
+            ).strip()
+        else:
+            pass
+        list_samhaengshi.append(inferred_sentence)
+
+        # until the end of the input_letter, give the previous residual_text to the next iteration
+        if index < len(input_letter) - 1:
+            residual_sentence = list_samhaengshi[index]
+            next_letter = input_letter[index + 1]
+            residual_text = (
+                f"{residual_sentence} {next_letter}"  #  previous sentence + next letter
+            )
+            print("residual_text", residual_text)
+
+        elif index == len(input_letter) - 1:  # end of the input_letter
+            # Concatenate strings in the list without intersection
+
+            return list_samhaengshi
+
+
+return_text = make_residual_conditional_samhaengshi(
+    input_letter=sent, condition_sentence=condition_sentence
+)
+
+print(return_text)
+
+st.write(return_text)

requirements.txt

@@ -0,0 +1,4 @@
+transformers
+streamlit
+torch
+numpy