Update app.py

app.py

@@ -3,32 +3,43 @@ import tensorflow as tf
 import cv2
 import numpy as np
 
-# Load the trained model
-model = tf.keras.models.load_model("FER_DATA.keras")
+# Load the TFLite model
+interpreter = tf.lite.Interpreter(model_path="FER_DATA.tflite")
+interpreter.allocate_tensors()
 
-# Define a function to predict emotion
+# Function to get input and output tensors
+def get_input_output_tensors(interpreter):
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+    return input_details, output_details
+
+# Function to predict emotion
 def predict_emotion(image):
-    # Convert image to grayscale
+    # Preprocess the image
     image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
     image = cv2.resize(image, (48, 48))
     image = image / 255.0
     image = np.expand_dims(image, axis=-1)
-    image = np.expand_dims(image, axis=0)
+    image = np.expand_dims(image, axis=0).astype(np.float32)
+
+    # Get input and output tensors
+    input_details, output_details = get_input_output_tensors(interpreter)
+
+    # Set the input tensor
+    interpreter.set_tensor(input_details[0]['index'], image)
+    
+    # Run the inference
+    interpreter.invoke()
 
-    # Predict emotion
-    prediction = model.predict(image)
-    emotion_index = np.argmax(prediction)
+    # Get the output
+    output = interpreter.get_tensor(output_details[0]['index'])
+    emotion = np.argmax(output)
+    
+    # Define the emotions list
     emotions = ['Angry', 'Happy', 'Sad', 'Neutral']
-    return emotions[emotion_index]
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=predict_emotion,
-    inputs=gr.Image(type="numpy", shape=(224, 224)),
-    outputs="text",
-    title="MoodSync - Emotion Detection",
-    description="Upload an image of a face to detect the emotion!"
-)
-
-# Launch the app
+    return emotions[emotion]
+
+# Define the Gradio interface
+iface = gr.Interface(fn=predict_emotion, inputs=gr.Image(shape=(224, 224)), outputs="text", title="MoodSync - Emotion Detection")
+
 iface.launch()