Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from gender_classification import gender_classification
 from emotion_classification import emotion_classification
 from dog_breed import dog_breed_classification
+from deepfake_vs_real import deepfake_classification
 
 # Functions to update the model state when a button is clicked.
 def select_gender():
@@ -13,6 +14,9 @@ def select_emotion():
 def select_dog_breed():
     return "dog breed"
 
+def select_deepfake():
+    return "deepfake"
+
 # Main classification function that calls the appropriate model based on selection.
 def classify(image, model_name):
     if model_name == "gender":
@@ -21,18 +25,21 @@ def classify(image, model_name):
         return emotion_classification(image)
     elif model_name == "dog breed":
         return dog_breed_classification(image)
+    elif model_name == "deepfake":
+        return deepfake_classification(image)
     else:
         return {"Error": "No model selected"}
 
 with gr.Blocks() as demo:
     # Sidebar with title and model selection buttons.
     with gr.Sidebar():
-        gr.Markdown("# SigLIP2 224")
+        gr.Markdown("# SigLIP2 Classification")
         with gr.Row():
             gender_btn = gr.Button("Gender Classification")
             emotion_btn = gr.Button("Emotion Classification")
             dog_breed_btn = gr.Button("Dog Breed Classification")
-        
+            deepfake_btn = gr.Button("Deepfake vs Real")
+
         # State to hold the current model choice.
         selected_model = gr.State("gender")
 
@@ -40,6 +47,7 @@ with gr.Blocks() as demo:
         gender_btn.click(fn=select_gender, inputs=[], outputs=selected_model)
         emotion_btn.click(fn=select_emotion, inputs=[], outputs=selected_model)
         dog_breed_btn.click(fn=select_dog_breed, inputs=[], outputs=selected_model)
+        deepfake_btn.click(fn=select_deepfake, inputs=[], outputs=selected_model)
 
         gr.Markdown("### Current Model:")
         model_display = gr.Textbox(value="gender", interactive=False)
@@ -49,7 +57,7 @@ with gr.Blocks() as demo:
     # Main interface: image input, analyze button, and prediction output.
     with gr.Column():
         image_input = gr.Image(type="numpy", label="Upload Image")
-        analyze_btn = gr.Button("Classify / Predict")
+        analyze_btn = gr.Button("Analyze")
         output_label = gr.Label(label="Prediction Scores")
 
         # When the "Analyze" button is clicked, use the selected model to classify the image.