Upload 3 files

app.py

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+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+
+# Load the trained model
+model = tf.keras.models.load_model('deepfake_detection.h5')
+
+# Function to load and preprocess the image
+def load_and_preprocess_image(uploaded_image):
+    img = Image.open(uploaded_image)
+    img = img.resize((150, 150))  # Resize image to match the input size expected by the model
+    img_array = image.img_to_array(img)  # Convert the image to a numpy array
+    img_array = np.expand_dims(img_array, axis=0)  # Expand dimensions to match the input shape (1, 150, 150, 3)
+    img_array = img_array / 255.0  # Rescale the image array
+    return img_array
+
+# Function to predict whether the image is real or fake
+def predict_image(uploaded_image):
+    img_array = load_and_preprocess_image(uploaded_image)
+    prediction = model.predict(img_array)
+    
+    if prediction < 0.5:
+        return "Fake"
+    else:
+        return "Real"
+
+# Streamlit app layout
+st.title("Deepfake Image Classification")
+st.write("Upload an image and the model will predict whether it's Real or Fake.")
+
+# Image uploader
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg"])
+
+# Prediction button
+if uploaded_image is not None:
+    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
+    st.write("")
+    
+    if st.button("Predict"):
+        result = predict_image(uploaded_image)
+        if result == "Fake":
+            st.write("The image is **<span style='color:red;'>Fake</span>**", unsafe_allow_html=True)
+        else:
+            st.write("The image is **<span style='color:blue;'>Real</span>**", unsafe_allow_html=True)

deepfake_detection.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:db4a19bec4dfe407ac645770ad9bee77a6d911add57289bec0206b8ad501ff8c
+size 228464648

requirements.txt

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+streamlit
+pillow
+numpy
+tensorflow