Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+# Load your trained model
+model = load_model("model.h5")
+
+# Define class names (update according to your model)
+class_names = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
+
+# Prediction function
+def predict_expression(image):
+    image = image.convert("L")               # Convert to grayscale
+    image = image.resize((48, 48))           # Resize to match model input
+    img_array = np.array(image) / 255.0      # Normalize
+    img_array = img_array.reshape(1, 48, 48, 1)  # Add batch and channel dims
+
+    prediction = model.predict(img_array)
+    class_index = np.argmax(prediction)
+    confidence = np.max(prediction)
+
+    return f"Expression: {class_names[class_index]} ({confidence:.2%} confidence)"
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_expression,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Facial Expression Classifier",
+    description="Upload a face image and get the predicted emotion"
+)
+
+iface.launch()