Update app.py

app.py

@@ -1,48 +1,110 @@
-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()
+# Import necessary libraries  
+import os  
+import cv2  
+import numpy as np  
+import gradio as gr  
+from keras.models import load_model  
+
+# Set TensorFlow environment variable  
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'  
+
+# Load the pre-trained emotion classification model  
+classifier = load_model(r'Final_Resnet50_Best_model.keras')  
+
+# Initialize the face classifier with the Haar Cascade model for face detection  
+face_classifier = cv2.CascadeClassifier(r'haarcascade_frontalface_default.xml')  
+
+# Define the list of emotion labels  
+emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']  
+
+# Emotion detection function  
+def detect_emotion(frame):  
+    # Convert the frame to grayscale for the face detection  
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  
+
+    # Detect faces in the grayscale frame  
+    faces = face_classifier.detectMultiScale(gray)  
+
+    # Process each face detected  
+    for (x, y, w, h) in faces:  
+        # Draw a rectangle around each detected face  
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 255), 2)  
+
+        # Extract the region of interest (ROI) as the face area from the grayscale frame  
+        roi_gray = gray[y:y+h, x:x+w]  
+        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)  
+
+        # Proceed if the ROI is not empty  
+        if np.sum([roi_gray]) != 0:  
+            roi = roi_gray.astype('float') / 255.0  # Normalize pixel values  
+            roi = np.expand_dims(roi, axis=0)  # Add batch dimension  
+
+            # Predict the emotion of the face using the pre-trained model  
+            prediction = classifier.predict(roi)[0]  
+            label = emotion_labels[prediction.argmax()]  
+            label_position = (x, y)  
+
+            # Display the predicted emotion label on the frame  
+            cv2.putText(frame, label, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)  
+
+    return frame  
+
+# Define the Gradio interface  
+def process_video(video):  
+    # Convert the video frame to a format Gradio can handle  
+    frame = cv2.cvtColor(video, cv2.COLOR_BGR2RGB)  
+    result_frame = detect_emotion(frame)  
+    return cv2.cvtColor(result_frame, cv2.COLOR_BGR2RGB)  
+
+# Create Gradio Interface  
+gr.Interface(fn=process_video, inputs=gr.inputs.Video(source="webcam", type="numpy"), outputs="video", live=True).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()