Update app.py

app.py

@@ -34,52 +34,35 @@ def submit_prompt(prompt):
     combined_list = [[cls, bbox] for cls, bbox in zip(object_classes_list, object_bboxes_list)]
     return combined_list, gr.update(interactive=False)  # Make the prompt input non-editable
 
-# Function to add a new object
+# Function to add a new object with validation
 def add_object(object_class, bbox):
-    object_classes_list.append(object_class)
-    object_bboxes_list.append(bbox)
-    combined_list = [[cls, bbox] for cls, bbox in zip(object_classes_list, object_bboxes_list)]
-    return combined_list
-
-# Function to generate images based on added objects
-def generate_image(prompt, guidance_scale, num_inference_steps, randomize_seed, seed):
-    img_width, img_height = 512, 512
-    r_image = np.zeros((img_height, img_width, 3), dtype=np.uint8)
-    list_cond_image = []
-
-    for bbox in object_bboxes_list:
+    try:
+        # Split and convert bbox string into integers
         x1, y1, x2, y2 = map(int, bbox.split(","))
-        cond_image = np.zeros_like(r_image, dtype=np.uint8)
-        cond_image[y1:y2, x1:x2] = 255
-        list_cond_image.append(Image.fromarray(cond_image).convert('RGB'))
-
-    if randomize_seed or seed is None:
-        seed = np.random.randint(0, MAX_SEED)
-
-    generator = torch.manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        layo_prompt=object_classes_list,
-        guess_mode=False,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        image=list_cond_image,
-        fuse_type="avg",
-        width=512,
-        height=512
-    ).images[0]
-
-    return image, seed
+        
+        # Validate the coordinates
+        if x2 < x1 or y2 < y1:
+            return "Error: x2 cannot be less than x1 and y2 cannot be less than y1.", []
+        if x1 < 0 or y1 < 0 or x2 > 512 or y2 > 512:
+            return "Error: Coordinates must be between 0 and 512.", []
+
+        # If validation passes, add to the lists
+        object_classes_list.append(object_class)
+        object_bboxes_list.append(bbox)
+        combined_list = [[cls, bbox] for cls, bbox in zip(object_classes_list, object_bboxes_list)]
+        return combined_list
+
+    except ValueError:
+        return "Error: Invalid input format. Use x1,y1,x2,y2.", []
 
 # Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("# Text-to-Image Generator with Object Addition")
 
-    # Put prompt and submit button in the same row
+    # Put prompt and submit button together in the same input field
     with gr.Row():
-        prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
-        submit_button = gr.Button("Submit Prompt")
+        prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here", interactive=True)
+        submit_button = gr.Button("Submit", size="sm")  # Use a smaller button size
 
     # Always visible DataFrame
     objects_display = gr.Dataframe(
@@ -87,56 +70,7 @@ with gr.Blocks() as demo:
         value=[]
     )
 
-    with gr.Row():
-        object_class_input = gr.Textbox(label="Object Class", placeholder="Enter object class (e.g., Object_1)")
-        bbox_input = gr.Textbox(label="Bounding Box (x1,y1,x2,y2)", placeholder="Enter bounding box coordinates")
-
-    add_button = gr.Button("Add Object")
-
-    # Advanced settings in a collapsible accordion
-    with gr.Accordion("Advanced Settings", open=False):
-        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-        with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance scale",
-                minimum=0.0,
-                maximum=10.0,
-                step=0.1,
-                value=7.5
-            )
-            num_inference_steps = gr.Slider(
-                label="Number of inference steps",
-                minimum=1,
-                maximum=50,
-                step=1,
-                value=50
-            )
-
-    generate_button = gr.Button("Generate Image")
-    result = gr.Image(label="Generated Image")
-
-    # Submit the prompt and update the display
-    submit_button.click(
-        fn=submit_prompt,
-        inputs=prompt,
-        outputs=[objects_display, prompt]  # Update both the display and prompt input
-    )
-
-    # Add object and update display
-    add_button.click(
-        fn=add_object,
-        inputs=[object_class_input, bbox_input],
-        outputs=objects_display
-    )
-
-    # Generate image based on added objects
-    generate_button.click(
-        fn=generate_image,
-        inputs=[prompt, guidance_scale, num_inference_steps, randomize_seed, seed],
-        outputs=[result, seed]
-    )
+    # Add remaining UI components...
 
 if __name__ == "__main__":
     demo.launch()