Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import torch
 import random
 import time
-import io
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from diffusers import DiffusionPipeline, LCMScheduler
 from PIL import Image, ImageFilter
@@ -41,22 +40,31 @@ pipe.enable_vae_slicing()
 pipe.set_progress_bar_config(disable=True)
 
 # ===============================
-# ULTRA MODE (REMOTE API)
+# ULTRA MODE (FIXED API CALL)
 # ===============================
-def call_ultra_api(prompt, negative, steps, seed):
+def call_ultra_api(prompt, steps, resolution, seed):
     try:
+        # Connect specifically to the Z-Image-Turbo Space
         client = Client("mrfakename/Z-Image-Turbo")
+        
+        # Based on your documentation, the parameters are:
+        # prompt, height, width, num_inference_steps, seed, randomize_seed
         result = client.predict(
             prompt=prompt,
-            negative_prompt=negative,
-            num_inference_steps=steps,
-            seed=seed,
+            height=int(resolution),
+            width=int(resolution),
+            num_inference_steps=int(steps),
+            seed=int(seed),
+            randomize_seed=False, 
             api_name="/predict"
         )
-        # The client returns a path to the image
+        
+        # result[0] is usually the image path in this specific Space
+        if isinstance(result, tuple) or isinstance(result, list):
+            return Image.open(result[0])
         return Image.open(result)
     except Exception as e:
-        print(f"API Error: {e}")
+        print(f"Detailed API Error: {e}")
         return None
 
 # ===============================
@@ -83,12 +91,17 @@ def generate(prompt, user_neg, res, step_val, is_ultra):
     
     if is_ultra:
         yield (None, "🎨 Generating Image...", gr.update(interactive=False))
-        # Ultra mode uses user_neg (field is visible) and skips enhancement
-        image = call_ultra_api(prompt, user_neg, step_val, seed)
+        # Ultra mode uses the direct prompt + user negative (if API supports neg, 
+        # but Z-Turbo usually ignores neg prompt to stay 'Turbo')
+        # We pass the prompt combined with negative for best results if the API is simple
+        full_prompt = f"{prompt} | Negative: {user_neg}" if user_neg else prompt
+        
+        image = call_ultra_api(full_prompt, step_val, size, seed)
+        
         if image:
             yield (image, f"✅ Done in Ultra Mode.", gr.update(interactive=True))
         else:
-            yield (None, "❌ API Error or Limit Reached", gr.update(interactive=True))
+            yield (None, "❌ API Error: The external GPU server is busy.", gr.update(interactive=True))
     else:
         yield (None, "🧠 Analysing Prompt", gr.update(interactive=False))
         enhanced = enhance_prompt(prompt)
@@ -97,7 +110,6 @@ def generate(prompt, user_neg, res, step_val, is_ultra):
         generator = torch.Generator("cpu").manual_seed(seed)
         start = time.time()
         
-        # Normal mode uses default_neg
         image = pipe(
             prompt=enhanced, 
             negative_prompt=default_neg, 
@@ -146,7 +158,6 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="green"), css=custom_css) as dem
                 output_img = gr.Image(label="Result", interactive=False)
                 status = gr.Markdown("🟢 Ready", elem_classes="status-box")
 
-    # Connect the UI logic
     ultra_check.change(
         toggle_ultra, 
         inputs=[ultra_check],