Spaces:
Runtime error
Runtime error
File size: 2,915 Bytes
60a481c 11b1efd 60a481c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import streamlit as st
import os
import random
from PIL import Image
from transformers import pipeline
import pandas as pd
import matplotlib.pyplot as plt
# Sidebar with author and contact info
st.sidebar.header('About the App')
st.sidebar.write('This Image Classifier app can classify images as Real or Fake.')
st.sidebar.write('This AI is trained on a dataset of real and deepfake images. It uses a pre-trained model to classify images. You can upload an image or use a random image from the dataset to test the classifier.')
st.sidebar.header('Author')
st.sidebar.write('Jan Mikolon')
st.sidebar.write('📧 Contact: [email protected]')
st.sidebar.markdown('[](https://www.linkedin.com/in/jan-mikolon/)', unsafe_allow_html=True)
# Function to load a random image from a folder
def load_random_image(folder_path):
images = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]
random_image_path = random.choice(images)
return Image.open(random_image_path)
# Path to your images folder
folder_path = 'data/'
# Streamlit app
st.title('Image Classifier - Real or Fake')
# Allow users to upload an image
uploaded_image = st.file_uploader("Upload an image for classification", type=["png", "jpg", "jpeg"])
# Create two columns
col1, col2 = st.columns(2)
# Display the uploaded image or a random image
if uploaded_image is not None:
image = Image.open(uploaded_image)
col1.image(image, caption='Uploaded Image', use_column_width=True)
else:
# Display a random image from the folder if no image is uploaded
if 'image_path' not in st.session_state or st.button('Load Random Image'):
st.session_state.image_path = load_random_image(folder_path)
col1.image(st.session_state.image_path, caption='Random Image', use_column_width=True)
# Classify button
if st.button('Classify'):
# This example uses a fixed classification result.
# You can replace this part with your actual model prediction logic.
pipe = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection")
if uploaded_image is not None:
classification_results = pipe(image)
else:
classification_results = pipe(st.session_state.image_path)
# Convert the classification results to a DataFrame
df_results = pd.DataFrame(classification_results)
# Plotting
fig, ax = plt.subplots()
ax.bar(df_results['label'], df_results['score'], color=['blue', 'orange'])
ax.set_ylabel('Scores')
ax.set_title('Classification Scores')
plt.tight_layout()
# Display the bar chart in Streamlit
col2.pyplot(fig)
# Load a new random image for next classification if no image is uploaded
if uploaded_image is None:
st.session_state.image_path = load_random_image(folder_path)
|