Update app.py

app.py

@@ -1,13 +1,10 @@
 import numpy as np
-from tensorflow.keras.preprocessing.image import img_to_array, load_img
-import gradio as gr
+from tensorflow.keras.preprocessing.image import img_to_array
 import gradio as gr
 import tensorflow as tf
-import numpy as np
 from PIL import Image
-import cv2
-from tensorflow.keras.preprocessing import image
 
+# Load your pre-trained model
 model = tf.keras.models.load_model('Final_Resnet50_Best_model.keras')
 
 # Emotion labels dictionary
@@ -16,33 +13,82 @@ 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
+    """Predict the emotion from the given 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
 
+# Define the Gradio interface
 interface = gr.Interface(
     fn=predict_emotion,  # Your prediction function
-    inputs=gr.Image(type="pil"),  # Input for uploading an image, directly compatible with PIL images
+    inputs=[
+        gr.inputs.Image(type="pil", label="Upload Image"),
+        gr.inputs.Image(type="pil", source="webcam", label="Capture Image")
+    ],  # Input options: upload or capture from webcam
     outputs="text",  # Output as text displaying the predicted emotion
     title="Emotion Detection",
-    description="Upload an image and see the predicted emotion."
+    description="Upload an image or capture one from your webcam to see the predicted emotion."
 )
 
 # Launch the Gradio interface
-interface.launch()
+interface.launch()
+
+
+
+# import numpy as np
+# from tensorflow.keras.preprocessing.image import img_to_array, load_img
+# import gradio as gr
+# import gradio as gr
+# import tensorflow as tf
+# import numpy as np
+# from PIL import Image
+# import cv2
+# from tensorflow.keras.preprocessing import image
+
+# model = tf.keras.models.load_model('Final_Resnet50_Best_model.keras')
+
+# # Emotion labels dictionary
+# 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()