Update app.py

app.py

@@ -1,110 +1,48 @@
-# 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.Video(sources=["webcam"]), 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()
+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()