Update app.py

app.py

@@ -26,30 +26,37 @@ model = load_learner(model_path)
 # Define the emotion classes
 emotion_classes = list(class_mapping.values())  # Get emotion classes from the class mapping
 
-# Gradio interface
-st.title("Emotion Recognition Classifier")
-
-# Upload an image
-file = st.file_uploader("Upload an image of a face", type=["jpeg", "jpg", "png"])
-
-if file is None:
-    st.write("Please upload an image to detect the emotion.")
-else:
-    # Display the uploaded image
-    image = Image.open(file)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-
-    # Convert the image to a format that the model can accept
-    img = PILImage.create(file)
-
-    # Predict the emotion
-    st.write("Classifying the emotion...")
+# Function for Emotion Prediction
+def predict_emotion(image):
+    img = PILImage.create(image)  # Convert the uploaded image into a PIL image
     pred_class, pred_idx, outputs = model.predict(img)
-
-    # Get the predicted label and confidence
     predicted_emotion = emotion_classes[pred_idx]
     confidence = outputs[pred_idx] * 100  # Convert to percentage
-
-    # Show results
-    st.write(f"**Predicted Emotion:** {predicted_emotion}")
-    st.write(f"**Confidence:** {confidence:.2f}%")
+    return predicted_emotion, f"{confidence:.2f}%"
+
+# Gradio interface with xkcd theme
+with gr.Blocks(theme="gstaff/xkcd") as demo:
+    gr.Markdown("# Emotion Recognition Classifier")
+    gr.Markdown("""
+        This app uses a deep learning model to recognize emotions in facial images. 
+        The model has been trained on a dataset to classify images into different emotion categories:
+        * Anger
+        * Fear
+        * Happiness
+        * Sadness
+        * Surprise
+        * Neutral
+    """)
+    
+    # Upload image widget
+    image_input = gr.Image(type="pil", label="Upload an image of a face")
+    
+    # Outputs
+    label_output = gr.Textbox(label="Predicted Emotion")
+    confidence_output = gr.Textbox(label="Confidence Percentage")
+
+    # Button to predict the emotion
+    image_input.upload(predict_emotion, image_input, [label_output, confidence_output])
+
+    # Launch the app
+    demo.launch(share=True)