Create app.py

app.py

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+import gradio as gr
+import cv2
+from deepface import DeepFace
+import numpy as np
+
+def predict_emotion(image):
+    # Convert Gradio image (PIL format) to an OpenCV image
+    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    
+    # Analyze the emotion using DeepFace
+    result = DeepFace.analyze(img, actions=['emotion'])
+    
+    # Get the dominant emotion
+    dominant_emotion = result[0]['dominant_emotion']
+    
+    return dominant_emotion
+
+# Define the Gradio interface using the new API
+iface = gr.Interface(fn=predict_emotion, 
+                     inputs=gr.Image(type="pil"),  # Updated gr.Image input
+                     outputs="text",  # Text output for dominant emotion
+                     title="Real-time Facial Emotion Recognition",
+                     description="Upload an image and get the predicted emotion")
+
+# Launch the Gradio app
+iface.launch(share=True)