Update app.py

app.py

@@ -1,5 +1,51 @@
-import gradio as gr
-# Assuming gr.load() is loading a model
-model = gr.load("models/vm24bho/net_dfm_myimg")
-# Launch the model with share=True
-model.launch(share=True)
+
+import streamlit as st
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+
+# Load your deep fake detection model
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model = tf.keras.models.load_model("models/vm24bho/net_dfm_myimg")
+    return model
+
+# Function to preprocess the image
+def preprocess_image(image):
+    image = np.array(image)
+    # Add your preprocessing steps here, like resizing, normalization, etc.
+    return image
+
+def main():
+    st.title("Deep Fake Image Detection")
+    st.markdown("""
+    Detect whether an image has been manipulated or synthetically generated using our state-of-the-art deep fake detection model.
+    ### How to Use
+    1. Upload an image by clicking the "Browse Files" button below.
+    2. Our model will analyze the image and determine if it's real or fake.
+    3. The result will be displayed, along with a confidence score.
+    Give it a try! 📷
+    """)
+
+    uploaded_image = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_image is not None:
+        model = load_model()
+        image = Image.open(uploaded_image)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Preprocess the image
+        processed_image = preprocess_image(image)
+
+        # Make prediction
+        prediction = model.predict(np.expand_dims(processed_image, axis=0))[0][0]
+
+        if prediction > 0.5:
+            st.error("This image is predicted to be a deep fake.")
+        else:
+            st.success("This image is predicted to be real.")
+
+        st.write(f"Confidence Score: {prediction:.2f}")
+
+if __name__ == "__main__":
+    main()