fix load model

app.py

@@ -3,7 +3,7 @@ import numpy as np
 from tensorflow.keras.models import load_model
 import gradio as gr
 
-model = load_model(r'D:\CODE\PYTHON\Project\Facial_Emotion\Facial Emotion\best_model.h5')
+model = load_model('best_model.h5')
 faceDetect=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
 labels_dict={
     0:'Angry', 
@@ -17,7 +17,6 @@ labels_dict={
 
 
 def predict_emotion(frame, scaleFactor=1.3, minNeighbors=4,):
-    # frame = cv2.imread(r"D:\CODE\PYTHON\Project\Facial_Emotion\Facial Emotion\pic2.jpg")
     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
     faces = faceDetect.detectMultiScale(gray, scaleFactor, minNeighbors)
     for x, y, w, h in faces:
@@ -42,9 +41,7 @@ with gr.Blocks() as demo:
     <h2 style='text-align: left;'>🤖 Facial Emotion Recognition App</h2>
 
     **This application detects and classifies **7 human emotions** from facial images using a deep learning model. Simply upload a photo, and the system will analyze facial expressions to predict the most likely emotion.**  
-
     **Recognized emotions**:  **Angry**,  **Disgust**,  **Fear**,  **Happy**,  **Neutral**,  **Sad**,  **Surprise**.  
-
     """)
    
     with gr.Row():
@@ -52,7 +49,6 @@ with gr.Blocks() as demo:
         with gr.Column():
             gr.Markdown("<h3 style='text-align: left;'>📸 Input the picture</h3>")
             image_input = gr.Image(label="Upload Image", type="numpy", placeholder="Upload an image here",)
-            # name_input = gr.Textbox(label="Name", placeholder="Enter your name", info="Your full name")
             scaleFator_input = gr.Slider(label="ScaleFactor",  minimum=1.1, maximum=2, step=0.1, value=1.3, info="scaleFator for face detection")
             minNeighbors_input = gr.Slider(label="MinNeighbors",  minimum=1, maximum=10, step=1, value=4, info="minNeighbors for face detection")
             Nut1 = gr.Button("Submit", variant="primary")