Update app.py

app.py

@@ -1,46 +1,46 @@
-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)
+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:cyan;'>Real</span>**", unsafe_allow_html=True)