Update app.py

app.py

@@ -1,44 +1,49 @@
 import streamlit as st
-import tensorflow as tf
 import numpy as np
+import tensorflow as tf
 from PIL import Image
-import os
-
-
-model = tf.keras.models.load_model('my_cnn_model_7.h5')  
-
-
-def predict_image(img):
-   
-    img = img.resize((64, 64))  
-    img_array = np.array(img)  
-    img_array = np.expand_dims(img_array, axis=0) 
-    img_array = img_array / 255.0  
-    
-    
-    predictions = model.predict(img_array)
-    
-    prediction_label = (predictions >= 0.5).astype("int32")
-    
-    return prediction_label[0][0]  
-
-# Streamlit UI
-st.title("Image Classifier: Real vs Fake")
-st.write("Upload an image to classify it as 'Real' or 'Fake'.")
-
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    
-    img = Image.open(uploaded_file)
-    img_resized = img.resize((100, 100))
-    st.image(img_resized, caption="Uploaded Image.", use_container_width=False)
+import cv2
+
+# Load the model
+model = tf.keras.models.load_model('cnn_real_fake_4.h5')
+
+# Define a function to preprocess the uploaded image
+def preprocess_image(image):
+    img_size = (256, 256)
+    image = image.resize(img_size)  # Resize image
+    image = np.array(image)  # Convert to numpy array
+    image = image / 255.0  # Normalize to [0, 1]
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Define a function for making predictions
+def predict_image(image):
+    preprocessed_image = preprocess_image(image)
+    prediction = model.predict(preprocessed_image)
+    if prediction > 0.5:
+        return 'Fake'
+    else:
+        return 'Real'
+
+# Streamlit app interface
+st.title('Real or Fake Image Classifier')
+
+st.write("""
+    This app allows you to upload an image and classify it as either Real or Fake.
+""")
+
+# Upload an image
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_image)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
     
+    # Predict the image
+    st.write("")
+    st.write("Classifying the image...")
+    result = predict_image(image)
     
-    if st.button('Classify'):
-        prediction = predict_image(img)
-        if prediction == 1:
-            st.write("Prediction: The given image is **Real**")
-        else:
-            st.write("Prediction: The given image is **Fake**")
+    # Display the result
+    st.write(f"The uploaded image is classified as: **{result}**")