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| import gradio as gr | |
| from transformers import pipeline | |
| import base64 | |
| from PIL import Image | |
| import io | |
| model = pipeline("image-classification", model="dima806/facial_emotions_image_detection") | |
| def predict_emotion(image_b64: str): | |
| image_data = base64.b64decode(image_b64.split(",")[1] if "," in image_b64 else image_b64) | |
| image = Image.open(io.BytesIO(image_data)).convert("RGB") | |
| predictions = model(image) | |
| return [[{"label": p["label"], "score": p["score"]} for p in predictions]] | |
| demo = gr.Interface( | |
| fn=predict_emotion, | |
| inputs=gr.Textbox(), | |
| outputs="json", | |
| api_name="predict" | |
| ) | |
| demo.launch() | |