app.py

app.py

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+import numpy as np
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import cv2
+from tensorflow.keras.preprocessing import image
+
+
+emotion_labels = {'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}
+index_to_emotion = {v: k for k, v in emotion_labels.items()}
+
+
+def prepare_image(img_pil):
+    """Preprocess the PIL image to fit your model's input requirements."""
+    # Convert the PIL image to a numpy array with the target size
+    img = img_pil.resize((224, 224))
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Convert single image to a batch.
+    img_array /= 255.0  # Rescale pixel values to [0,1], as done during training
+    return img_array
+
+
+
+# Define the Gradio interface
+def predict_emotion(image):
+    # Preprocess the image
+    processed_image = prepare_image(image)
+    # Make prediction using the model
+    prediction = model.predict(processed_image)
+    # Get the emotion label with the highest probability
+    predicted_class = np.argmax(prediction, axis=1)
+    predicted_emotion = index_to_emotion.get(predicted_class[0], "Unknown Emotion")
+    return predicted_emotion
+
+interface = gr.Interface(
+    fn=predict_emotion,  # Your prediction function
+    inputs=gr.Image(type="pil"),  # Input for uploading an image, directly compatible with PIL images
+    outputs="text",  # Output as text displaying the predicted emotion
+    title="Emotion Detection",
+    description="Upload an image and see the predicted emotion."
+)
+
+# Launch the Gradio interface
+interface.launch()