Refactor image generation process into a single API endpoint for improved clarity and efficiency

app.py

@@ -256,22 +256,33 @@ with gr.Blocks(title="CRM: 3D Character Generation from Single Image") as demo:
             output_xyz = gr.Image(label="Generated XYZ", type="pil")
             output_glb = gr.Model3D(label="Generated 3D Model")
     
-    # Connect the functions with explicit API names
+    def process_and_generate(input_image, background_choice, foreground_ratio, back_groud_color, seed, scale, step):
+        # First check input
+        input_image = check_input_image(input_image)
+        # Then preprocess
+        processed = preprocess_image(input_image, background_choice, foreground_ratio, back_groud_color)
+        # Finally generate
+        output_img, output_xyz_img, glb_path = gen_image(processed, seed, scale, step)
+        return processed, output_img, output_xyz_img, glb_path
+
+    # Connect the functions with a single API endpoint
     generate_btn.click(
-        fn=check_input_image,
-        inputs=[input_image],
-        outputs=[input_image],
-        api_name="check_input_image"
-    ).success(
-        fn=preprocess_image,
-        inputs=[input_image, background_choice, foreground_ratio, back_groud_color],
-        outputs=[processed_image],
-        api_name="preprocess_image"
-    ).success(
-        fn=gen_image,
-        inputs=[processed_image, seed, scale, step],
-        outputs=[output_image, output_xyz, output_glb],
-        api_name="gen_image"
+        fn=process_and_generate,
+        inputs=[
+            input_image,
+            background_choice,
+            foreground_ratio,
+            back_groud_color,
+            seed,
+            scale,
+            step
+        ],
+        outputs=[
+            processed_image,
+            output_image,
+            output_xyz,
+            output_glb
+        ]
     )
 
 # For Hugging Face Spaces, use minimal configuration