Update app.py

app.py

@@ -1,3 +1,74 @@
-import gradio as gr
+import streamlit as st
+from PIL import Image
+from transformers import pipeline
+import requests
 
-gr.load("models/vm24/net_dfm_myimg").launch()
+# Set the title and sidebar
+st.set_page_config(page_title="Deepfake vs Real Image Detection", page_icon="🤖")
+
+st.title("Deepfake vs Real Image Detection")
+st.sidebar.title("Options")
+
+# Description
+st.markdown(
+    """
+    Welcome to the Deepfake vs Real Image Detection app! 
+    Upload an image and let our model determine if it is real or a deepfake.
+    """
+)
+
+# Load the pipeline
+model_name = "vm24bho/net_dfm_myimg"
+try:
+    pipe = pipeline('image-classification', model=model_name)
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.stop()
+
+# Upload image
+st.sidebar.subheader("Upload Image")
+uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+# Add a sample image option
+sample_image = None
+if st.sidebar.button("Use Sample Image"):
+    sample_url = "https://drive.google.com/file/d/15zh_XjwH9gGAzNdNUEgHraYAzE4GdSRU/view?usp=drive_link"  # Replace with a valid sample image URL
+    try:
+        response = requests.get(sample_url, stream=True)
+        sample_image = Image.open(response.raw)
+    except Exception as e:
+        st.error(f"Error loading sample image: {e}")
+        st.stop()
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+elif sample_image is not None:
+    image = sample_image
+else:
+    image = None
+
+# Display the uploaded image
+if image is not None:
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    # Apply the model
+    try:
+        result = pipe(image)
+    except Exception as e:
+        st.error(f"Error classifying image: {e}")
+        st.stop()
+
+    # Determine if the image is classified as real or fake
+    if result[0]['label'] == 'REAL':
+        st.write("Output is: Real")
+    else:
+        st.write("Output is: Fake")
+
+    # Display the result
+    st.subheader("Classification Result")
+    for res in result:
+        st.write(f"**{res['label']}**: {res['score']*100:.2f}%")
+else:
+    st.sidebar.info("Upload an image to get started or use the sample image.")