Update app.py

app.py

@@ -1,69 +1,69 @@
-import json
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from gtts import gTTS
-from diffusers import StableDiffusionPipeline
-import gradio as gr
-
-def load_fairytale(file_obj):
-    data = json.load(file_obj)
-    return data['title'], data['content']
-
-def generate_grandma_voice(text):
-    grandma_text = f"에구구 얘야, 잘 들어보렴. {text.strip()} ... 옛날 옛적 이야기란다~"
-    tts = gTTS(text=grandma_text, lang='ko')
-    audio_path = "grandma_voice.mp3"
-    tts.save(audio_path)
-    return audio_path
-
-emotion_tokenizer = AutoTokenizer.from_pretrained("monologg/koelectra-base-discriminator")
-emotion_model = AutoModelForSequenceClassification.from_pretrained("monologg/koelectra-base-discriminator")
-
-def classify_emotion(text):
-    inputs = emotion_tokenizer(text, return_tensors="pt", truncation=True)
-    with torch.no_grad():
-        outputs = emotion_model(**inputs)
-    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
-    label = torch.argmax(probs).item()
-    emotions_ko = ["기쁨", "슬픔", "분노", "불안", "중립"]
-    emotions_en = ["joy", "sadness", "anger", "anxiety", "neutral"]
-    return emotions_en[label], emotions_ko[label]
-
-stable_pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
-)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-stable_pipe = stable_pipe.to(device)
-
-def generate_emotion_image(emotion_en):
-    prompt = f"A dreamy digital painting that represents the feeling of {emotion_en}"
-    image = stable_pipe(prompt).images[0]
-    image_path = f"{emotion_en}_image.png"
-    image.save(image_path)
-    return image_path
-
-def run_all(fairytale_file, child_feeling_text):
-    title, content = load_fairytale(fairytale_file)
-    audio_path = generate_grandma_voice(content[:300])
-    emotion_en, emotion_ko = classify_emotion(child_feeling_text)
-    image_path = generate_emotion_image(emotion_en)
-    return title, audio_path, emotion_ko, image_path
-
-demo = gr.Interface(
-    fn=run_all,
-    inputs=[
-        gr.File(label="동화 JSON 파일 업로드"),
-        gr.Textbox(label="아이의 감상문")
-    ],
-    outputs=[
-        gr.Text(label="동화 제목"),
-        gr.Audio(label="할머니 목소리"),
-        gr.Text(label="감정 분석 결과 (한국어)"),
-        gr.Image(label="감정 표현 이미지")
-    ],
-    title="AI 할머니가 읽어주는 감성 동화책",
-    description="동화를 업로드하면 할머니가 읽어주고, 아이 감상문에 맞춰 감정 이미지를 생성합니다."
-)
-
-if __name__ == "__main__":
+import json
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from gtts import gTTS
+from diffusers import StableDiffusionPipeline
+import gradio as gr
+
+def load_fairytale(file_obj):
+    data = json.loads(file_obj.read().decode("utf-8"))  # read & decode
+    return data['title'], data['content']
+
+def generate_grandma_voice(text):
+    grandma_text = f"에구구 얘야, 잘 들어보렴. {text.strip()} ... 옛날 옛적 이야기란다~"
+    tts = gTTS(text=grandma_text, lang='ko')
+    audio_path = "grandma_voice.mp3"
+    tts.save(audio_path)
+    return audio_path
+
+emotion_tokenizer = AutoTokenizer.from_pretrained("monologg/koelectra-base-discriminator")
+emotion_model = AutoModelForSequenceClassification.from_pretrained("monologg/koelectra-base-discriminator")
+
+def classify_emotion(text):
+    inputs = emotion_tokenizer(text, return_tensors="pt", truncation=True)
+    with torch.no_grad():
+        outputs = emotion_model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    label = torch.argmax(probs).item()
+    emotions_ko = ["기쁨", "슬픔", "분노", "불안", "중립"]
+    emotions_en = ["joy", "sadness", "anger", "anxiety", "neutral"]
+    return emotions_en[label], emotions_ko[label]
+
+stable_pipe = StableDiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
+)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+stable_pipe = stable_pipe.to(device)
+
+def generate_emotion_image(emotion_en):
+    prompt = f"A dreamy digital painting that represents the feeling of {emotion_en}"
+    image = stable_pipe(prompt).images[0]
+    image_path = f"{emotion_en}_image.png"
+    image.save(image_path)
+    return image_path
+
+def run_all(fairytale_file, child_feeling_text):
+    title, content = load_fairytale(fairytale_file)
+    audio_path = generate_grandma_voice(content[:300])
+    emotion_en, emotion_ko = classify_emotion(child_feeling_text)
+    image_path = generate_emotion_image(emotion_en)
+    return title, audio_path, emotion_ko, image_path
+
+demo = gr.Interface(
+    fn=run_all,
+    inputs=[
+        gr.File(label="동화 JSON 파일 업로드"),
+        gr.Textbox(label="아이의 감상문")
+    ],
+    outputs=[
+        gr.Text(label="동화 제목"),
+        gr.Audio(label="할머니 목소리"),
+        gr.Text(label="감정 분석 결과 (한국어)"),
+        gr.Image(label="감정 표현 이미지")
+    ],
+    title="AI 할머니가 읽어주는 감성 동화책",
+    description="동화를 업로드하면 할머니가 읽어주고, 아이 감상문에 맞춰 감정 이미지를 생성합니다."
+)
+
+if __name__ == "__main__":
     demo.launch()