Create app.py

app.py

@@ -0,0 +1,43 @@
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing 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)
+    st.image(img, caption="Uploaded Image.", use_column_width=True)
+    
+    
+    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**")