test: ui

app.py

@@ -223,44 +223,58 @@ def predict_image_with_html(img, confidence_threshold, augment_methods, rotate_d
     return img_pil, html_content
 
 with gr.Blocks() as iface:
-    gr.Markdown("# AI Generated Image / Deepfake Detection Models Evaluation")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            image_input = gr.Image(label="Upload Image to Analyze", sources=['upload'], type='pil')
-            with gr.Accordion("Settings", open=False, elem_id="settings_accordion"):
-                augment_checkboxgroup = gr.CheckboxGroup(["rotate", "add_noise", "sharpen"], label="Augmentation Methods")
-                rotate_slider = gr.Slider(0, 360, value=0, step=1, label="Rotate Degrees", visible=False)
-                noise_slider = gr.Slider(0, 100, value=0, step=1, label="Noise Level", visible=False)
-                sharpen_slider = gr.Slider(0, 100, value=1, step=1, label="Sharpen Strength", visible=False)
-                confidence_slider = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Confidence Threshold")
-            inputs = [image_input, confidence_slider, augment_checkboxgroup, rotate_slider, noise_slider, sharpen_slider]
-            predict_button = gr.Button("Predict")
-            image_output = gr.Image(label="Processed Image", visible=True)
+    with gr.Tab("AI Image Detection"):
+        gr.Markdown("# AI Generated Image / Deepfake Detection Models Evaluation")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                image_input = gr.Image(label="Upload Image to Analyze", sources=['upload'], type='pil')
+                with gr.Accordion("Settings", open=False, elem_id="settings_accordion"):
+                    augment_checkboxgroup = gr.CheckboxGroup(["rotate", "add_noise", "sharpen"], label="Augmentation Methods")
+                    rotate_slider = gr.Slider(0, 360, value=0, step=1, label="Rotate Degrees", visible=False)
+                    noise_slider = gr.Slider(0, 100, value=0, step=1, label="Noise Level", visible=False)
+                    sharpen_slider = gr.Slider(0, 100, value=1, step=1, label="Sharpen Strength", visible=False)
+                    confidence_slider = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Confidence Threshold")
+                inputs = [image_input, confidence_slider, augment_checkboxgroup, rotate_slider, noise_slider, sharpen_slider]
+                predict_button = gr.Button("Predict")
+                image_output = gr.Image(label="Processed Image", visible=True)
 
-        with gr.Column(scale=2):
-            with gr.Accordion("Project OpenSight - Model Evaluations & Playground", open=False, elem_id="project_accordion"):
-                gr.Markdown("## OpenSight is a SOTA gen. image detection model, in pre-release prep.\n\nThis HF Space is a temporary home for us and the public to evaluate the shortcomings of current open source models.\n\n<-- Feel free to play around by starting with an image as we prepare our formal announcement.")
-            # Custom HTML component to display results in 5 columns
-            results_html = gr.HTML(label="Model Predictions")
-            outputs = [image_output, results_html]
-    
-    # Show/hide rotate slider based on selected augmentation method
-    augment_checkboxgroup.change(lambda methods: gr.update(visible="rotate" in methods), inputs=[augment_checkboxgroup], outputs=[rotate_slider])
-    augment_checkboxgroup.change(lambda methods: gr.update(visible="add_noise" in methods), inputs=[augment_checkboxgroup], outputs=[noise_slider])
-    augment_checkboxgroup.change(lambda methods: gr.update(visible="sharpen" in methods), inputs=[augment_checkboxgroup], outputs=[sharpen_slider])
-    
-    predict_button.click(
-        fn=predict_image_with_html, 
-        inputs=inputs, 
-        outputs=outputs
-    )
-    predict_button.click(
-        fn=None, 
-        js="() => {document.getElementById('project_accordion').open = false;}",  # Close the project accordion
-        inputs=[], 
-        outputs=[]
-    )
+            with gr.Column(scale=2):
+                with gr.Accordion("Project OpenSight - Model Evaluations & Playground", open=False, elem_id="project_accordion"):
+                    gr.Markdown("## OpenSight is a SOTA gen. image detection model, in pre-release prep.\n\nThis HF Space is a temporary home for us and the public to evaluate the shortcomings of current open source models.\n\n<-- Feel free to play around by starting with an image as we prepare our formal announcement.")
+                # Custom HTML component to display results in 5 columns
+                results_html = gr.HTML(label="Model Predictions")
+                outputs = [image_output, results_html]
+        
+        # Show/hide rotate slider based on selected augmentation method
+        augment_checkboxgroup.change(lambda methods: gr.update(visible="rotate" in methods), inputs=[augment_checkboxgroup], outputs=[rotate_slider])
+        augment_checkboxgroup.change(lambda methods: gr.update(visible="add_noise" in methods), inputs=[augment_checkboxgroup], outputs=[noise_slider])
+        augment_checkboxgroup.change(lambda methods: gr.update(visible="sharpen" in methods), inputs=[augment_checkboxgroup], outputs=[sharpen_slider])
+        
+        predict_button.click(
+            fn=predict_image_with_html, 
+            inputs=inputs, 
+            outputs=outputs
+        )
+        predict_button.click(
+            fn=None, 
+            js="() => {document.getElementById('project_accordion').open = false;}",  # Close the project accordion
+            inputs=[], 
+            outputs=[]
+        )
+
+    with gr.Tab("Another Interface"):
+        # Add components for the second interface here
+        gr.Markdown("# Another Interface")
+        # Example: Add a simple text input and output
+        text_input = gr.Textbox(label="Enter Text")
+        text_output = gr.Textbox(label="Processed Text")
+        text_button = gr.Button("Process Text")
+        text_button.click(
+            fn=lambda x: x.upper(),  # Example function to convert text to uppercase
+            inputs=text_input, 
+            outputs=text_output
+        )
 
 # Launch the interface
 iface.launch()