Delete app.py

app.py

@@ -1,41 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-from PIL import Image
-
-# 1) Load a pretrained image-based facial emotion model from Hugging Face
-#    The "mehdi-wasi/facial_emotion_recognition" model is an example.
-#    You can search "emotion", "FER" or "facial expression" on huggingface.co/models
-emotion_pipeline = pipeline(
-    task="image-classification",
-    model="mehdi-wasi/facial_emotion_recognition"
-)
-
-# 2) Define a function to handle image input and return predictions
-def predict_emotion(image: Image.Image):
-    """
-    Takes a PIL image, uses the emotion_pipeline to get predictions,
-    and returns them as a list of {label, score}.
-    """
-    # The pipeline will produce a list of predictions sorted by confidence
-    predictions = emotion_pipeline(image)
-
-    # We can return the raw pipeline output or we can format it
-    # e.g., only return top label or return them all
-    # Let’s return them all for clarity.
-    return predictions
-
-# 3) Build the Gradio interface
-demo = gr.Interface(
-    fn=predict_emotion,
-    inputs=gr.Image(type="pil"),  # user uploads an image (PIL format)
-    outputs="json",               # we’ll return the pipeline’s JSON predictions
-    title="Quantum Emotion Detection",
-    description="""
-    **QuantumEmotion** - A simple demo that uses a Hugging Face model to classify 
-    facial emotions. Upload an image with a face, and you'll see predicted emotions.
-    """
-)
-
-# 4) Launch the app (Gradio automatically does this on Spaces)
-if __name__ == "__main__":
-    demo.launch()