deleted unneeded stuff

app.py

@@ -1,27 +1,8 @@
 import gradio as gr
-import numpy as np
-import os
-import zipfile
 from transformers import pipeline
 
-with zipfile.ZipFile("examples.zip","r") as zip_ref:
-    zip_ref.extractall(".")
-
-EXAMPLES_FOLDER = 'examples'
-examples_names = os.listdir(EXAMPLES_FOLDER)
-examples = []
-for example_name in examples_names:
-    example_path = os.path.join(EXAMPLES_FOLDER, example_name)
-    label = example_name.split('_')[0]
-    example = {
-        'path': example_path,
-        'label': label
-    }
-    examples.append(example)
-np.random.shuffle(examples)
-
 # Load the Hugging Face model
-model = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection")
+model = pipeline("image-classification", model="alkatraz445/deepfake_detection")
 
 # Define the prediction function
 def classify_image(image):
@@ -33,9 +14,8 @@ interface = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(type="pil"),  # Accepts images in PIL format
     outputs=gr.Label(num_top_classes=2),  # Displays top two classifications with probabilities
-    title="Deepfake vs Real Image Detection",
+    title="Deepfake image detector",
     description="Upload an image to determine whether it's real or deepfake.",
-    examples=[[examples[i]["path"], examples[i]["label"]] for i in range(10)]
 )
 
 # Launch the Gradio app

examples.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9c719c2f16bad2e71d0d33d6ae59fa646dac82a812e1a90578c4b97ef6e8f36c
-size 28750945