Update app.py

app.py

@@ -1,36 +1,51 @@
-import gradio as gr
+
+
 from tensorflow.keras.models import load_model
 from PIL import Image
 import numpy as np
+import gradio as gr
 
-# Load the model
-model = load_model('./model.h5')
+# Loading the trained model
+try:
+    model = load_model('/model.h5')  # Replacing with the path to your saved model
+except Exception as e:
+    print("Error loading the model:", e)
 
 def detect_image(input_image):
-    img = Image.fromarray(input_image).resize((256, 256))
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
+    try:
+        # Function to detect image
+        img = Image.fromarray(input_image).resize((256, 256))  # Resize image
+        img_array = np.array(img) / 255.0  # Normalize pixel values
+        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+        prediction = model.predict(img_array)[0][0]
+        probability_real = prediction * 100  # Convert prediction to percentage
+        probability_ai = (1 - prediction) * 100
 
-    prediction = model.predict(img_array)[0][0]
-    probability_real = prediction * 100
-    probability_ai = (1 - prediction) * 100
+        # Determine the final output
+        if probability_real > probability_ai:
+            result = 'Input Image is Real'
+            confidence = probability_real
+        else:
+            result = 'Input Image is AI Generated'
+            confidence = probability_ai
 
-    if probability_real > probability_ai:
-        result = 'Input Image is Real'
-        confidence = probability_real
-    else:
-        result = 'Input Image is AI Generated'
-        confidence = probability_ai
+        return result, confidence
+    except Exception as e:
+        print("Error detecting image:", e)
+        return "Error detecting image", 0
 
-    return result, confidence
+# Define input and output components for Gradio
+input_image = gr.Image()
+output_text = gr.Textbox(label="Result")
+output_confidence = gr.Textbox(label="Confidence (%)")
 
-demo = gr.Interface(
+# Create Gradio interface
+gr.Interface(
     fn=detect_image,
-    inputs=gr.Image(type="numpy", shape=(256, 256)),
-    outputs=[gr.Textbox(label="Result"), gr.Textbox(label="Confidence (%)")],
+    inputs=input_image,
+    outputs=[output_text, output_confidence],
     title="Deepfake Detection",
     description="Upload an image to detect if it's real or AI generated."
-)
+).launch(share=True)
 
-# Deploy the interface on Gradio Hub
-demo.launch(share=True)