Update app.py

app.py

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+import gradio as gr
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+from PIL import Image
+import torch
+import requests
+
+# Load caption model
+caption_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+# Load ChatTTS (via inference API)
+CHAT_TTS_API = "https://api-inference.huggingface.co/models/2Noise/ChatTTS"
+headers = {"Authorization": f"Bearer {YOUR_HF_TOKEN}"}
+
+def generate_caption(image):
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+    output_ids = caption_model.generate(pixel_values, max_length=50, num_beams=4)
+    caption = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return caption
+
+def tts_audio(text):
+    payload = {"inputs": text}
+    response = requests.post(CHAT_TTS_API, headers=headers, json=payload)
+    audio = response.content
+    return audio
+
+def process(image):
+    caption = generate_caption(image)
+    audio = tts_audio(caption)
+    return caption, (audio, "result.wav")
+
+demo = gr.Interface(
+    fn=process,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Text(label="설명"), gr.Audio(label="TTS 음성")],
+    title="🎨 AI 그림 설명 낭독기",
+)
+
+demo.launch()