Update app.py

app.py

@@ -2,27 +2,37 @@ import gradio as gr
 import tensorflow as tf
 from PIL import Image
 import numpy as np
+import cv2
 
 # Loading saved model
 model = tf.keras.models.load_model('gender_recognition.h5')
 
 def predict(input_image):
     try:
+        # Convert PIL Image to OpenCV format (numpy array)
+        input_image = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
         # Resizing and preprocessing input image
-        input_image = np.array(input_image)
-        input_image = np.resize(input_image, (218, 178, 3))/255.
+        input_image = cv2.resize(input_image, (178, 218))
+        input_image = np.array(input_image).astype(np.float32) / 255.0
         input_image = np.expand_dims(input_image, axis=0)  # Add a dimension for the batch size
 
         # Making prediction
         prediction = model.predict(input_image)
 
         # Postprocess prediction
-        output = f"Female: {prediction[0][0] * 100:.2f}% probability. Male: {prediction[0][1] * 100:.2f}% probability."
+        labels = ['Female', 'Male']
+        threshold = 0.5  # threshold for classifying as 'Male'
+        predicted_gender = 'Male' if prediction[0][1] > threshold else 'Female'
+        prediction_probability = prediction[0][1] if predicted_gender == 'Male' else prediction[0][0]
+
+        # Combine the predicted gender and the probability into a single string
+        output = f"{predicted_gender}. {prediction_probability * 100:.2f}% probability."
 
         return output
     except Exception as e:
         return str(e)
 
+
 # Creating Gradio interface
 iface = gr.Interface(
     fn=predict,