Update app.py

app.py

@@ -15,35 +15,26 @@ zip_ref = zipfile.ZipFile(local_zip, 'r')
 zip_ref.extractall('FINAL-EFFICIENTNETV2-B0')
 zip_ref.close()
 
-local_zip = "FINAL-EFFICIENTNETV2-S.zip"
-zip_ref = zipfile.ZipFile(local_zip, 'r')
-zip_ref.extractall('FINAL-EFFICIENTNETV2-S')
-zip_ref.close()
-
 local_zip = "deepfakes-test-images.zip"
 zip_ref = zipfile.ZipFile(local_zip, 'r')
 zip_ref.extractall('deepfakes-test-images')
 zip_ref.close()
 
 
-model_b0 = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-B0")
-model_s = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-S")
+model = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-B0")
+
 
 detector = MTCNN()
 
 
-def deepfakespredict(select_model, input_img ):
+def deepfakespredict(input_img ):
 
     model = []
     labels = ['real', 'fake']
     pred = [0, 0]
     
-    if select_model == "EfficientNetV2-B0":
-        model = model_b0
-    elif select_model == "EfficientNetV2-B0":
-        model = model_s
-    
     text =""
+    text2 =""
     face = detector.detect_faces(input_img)
 
     if len(face) > 0:
@@ -68,13 +59,15 @@ def deepfakespredict(select_model, input_img ):
             
     else:
         text = "Face is not detected in the image."
+    
+    text2 = "Real: " + str(np.round(pred[0]*100, 2)) + "%, Fake: " + str(np.round(pred[1]*100, 2)) + "%"
 
-    return text, input_img, {labels[i]: float(pred[i]) for i in range(2)}
+    return input_img, text, text2, {labels[i]: float(pred[i]) for i in range(2)}
 
 
 title="EfficientNetV2 Deepfakes Image Detector"
 description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector. To use it, simply upload your image, or click one of the examples to load them."
-examples = [ [],[
+examples = [
                 ['deepfakes-test-images/Fake-1.jpg'],
                 ['deepfakes-test-images/Fake-2.jpg'],
                 ['deepfakes-test-images/Fake-3.jpg'],
@@ -86,12 +79,13 @@ examples = [ [],[
                 ['deepfakes-test-images/Real-3.jpg'],
                 ['deepfakes-test-images/Real-4.jpg'],
                 ['deepfakes-test-images/Real-5.jpg']
-                ]
+                
            ]
             
 gr.Interface(deepfakespredict,
-                     inputs = [gr.inputs.Radio(["EfficientNetV2-B0", "EfficientNetV2-S"], label = "Select model:"), "image"],
-                     outputs=["text", gr.outputs.Image(type="pil", label="Detected face"), gr.outputs.Label(num_top_classes=None, type="auto", label="Confidence")],
+                     inputs = ["image"],
+                     outputs=[gr.outputs.Image(type="pil", label="Detected face"), "text", "text", gr.outputs.Label(num_top_classes=None, type="auto", label="Confidence")],
                      title=title,
-                     description=description
+                     description=description,
+                     examples = examples
                      ).launch()