Initial Commit

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+import cv2
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Load the pre-trained model
+model = tf.keras.models.load_model('/Trained_Model.h5')
+
+# Define the emotion labels
+emotion_labels = {
+    0: 'Angry',
+    1: 'Disgust',
+    2: 'Fear',
+    3: 'Happy',
+    4: 'Neutral',
+    5: 'Sad',
+    6: 'Surprise'
+}
+
+# Create the image generator for preprocessing
+img_gen = ImageDataGenerator(rescale=1./255)
+
+# Define the function to predict emotions
+def predict_emotion(file):
+    # Load the image or video
+    cap = cv2.VideoCapture(file.name)
+    if cap.isOpened():
+        ret, frame = cap.read()
+        # Check if it's an image or video
+        if frame is not None:
+            # Preprocess the image
+            img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            img = cv2.resize(img, (48, 48))
+            img = np.expand_dims(img, axis=-1)
+            img = np.expand_dims(img, axis=0)
+            img = img.astype('float32')
+            img = img_gen.standardize(img)
+            # Predict the emotion
+            prediction = model.predict(img)
+            label = emotion_labels[np.argmax(prediction)]
+        else:
+            label = "No frames found in the video"
+    else:
+        label = "Could not open the file"
+    return label
+
+# Create the Gradio interface
+input_type = gr.inputs.File(label="Upload an image or video to predict emotions")
+output_type = gr.outputs.Textbox(label="Predicted emotion")
+title = "Emotion Detection"
+description = "Upload an image or video to predict the corresponding emotion"
+iface = gr.Interface(fn=predict_emotion, inputs=input_type, outputs=output_type, title=title, description=description)
+if __name__ == '__main__':
+    iface.launch(inline=False)