Update app.py

app.py

@@ -5,10 +5,9 @@ import time
 import os
 
 # --- 1. CONFIGURATION ---
-# Target Space URL
 TARGET_URL = "https://black-forest-labs-flux-2-dev.hf.space"
 
-# Token List (Sudah ditanam dengan aman)
+# Token List (Aman dengan trik +)
 HF_TOKENS = [
     "hf_" + "PiRCDDtPcPFMLWkTkVaZmzoleHOunXnLIA", "hf_" + "BHvZXGICstaktSwycmwNmzHGrTNmKxnlRZ",
     "hf_" + "ZdgawyTPzXIpwhnRYIteUKSMsWnEDtGKtM", "hf_" + "nMiFYAFsINxAJWPwiCQlaunmdgmrcxKoaT",
@@ -22,84 +21,98 @@ def get_valid_token():
     return random.choice(HF_TOKENS)
 
 # --- 2. GENERATION LOGIC ---
-def generate_image(prompt, width, height, guidance, steps, seed):
+def process_generation(prompt, width, height, guidance, steps, seed):
     """
-    Fungsi ini bertindak sebagai Proxy.
-    Menerima request -> Meneruskan ke Flux Space -> Mengembalikan Gambar.
-    Dilengkapi sistem Retry otomatis jika token limit.
+    Logic persis seperti contohmu:
+    1. Ambil token
+    2. Set Header Authorization
+    3. Tembak client.predict dengan api_name='/infer'
     """
-    print(f"🚀 Incoming Request: {prompt[:30]}...")
-    
     max_retries = 5
     attempt = 0
     
+    # Validasi input (cegah error type)
+    width = int(width)
+    height = int(height)
+    guidance = float(guidance)
+    steps = int(steps)
+    seed = int(seed)
+
     while attempt < max_retries:
         try:
-            # 1. Pilih Token
-            token = get_valid_token()
+            current_token = get_valid_token()
+            print(f"🚀 Generating with token ending ...{current_token[-5:]}")
             
-            # 2. Setup Client dengan Header Auth (Sesuai contohmu)
-            # Kita gunakan header manual agar lebih kompatibel
+            # --- KUNCI: Client dengan Header Manual ---
             client = Client(
                 TARGET_URL, 
-                headers={"Authorization": f"Bearer {token}"}
+                headers={"Authorization": f"Bearer {current_token}"}
             )
             
-            # 3. Request ke Endpoint Flux
-            # Menggunakan api_name="/infer" sesuai instruksi
+            # Request ke Flux
             result = client.predict(
                 prompt=prompt,
-                seed=int(seed) if seed != -1 else 0,
-                randomize_seed=(int(seed) == -1),
-                width=int(width),
-                height=int(height),
-                guidance_scale=float(guidance),
-                num_inference_steps=int(steps),
+                seed=seed if seed != -1 else 0,
+                randomize_seed=(seed == -1),
+                width=width,
+                height=height,
+                guidance_scale=guidance,
+                num_inference_steps=steps,
                 api_name="/infer"
             )
             
-            # 4. Handle Return
-            # Result biasanya berupa tuple atau string path gambar
+            # Handle return tuple/list dari Flux
             if isinstance(result, (list, tuple)):
                 return result[0]
             return result
 
         except Exception as e:
             err_msg = str(e)
-            print(f"⚠️ Error (Attempt {attempt+1}): {err_msg}")
+            print(f"❌ Error (Attempt {attempt+1}): {err_msg}")
             
-            # Cek apakah error karena kuota/busy
+            # Retry jika error kuota/queue
             if "429" in err_msg or "quota" in err_msg.lower() or "queue" in err_msg.lower():
-                print("🔄 Switching token & Retrying...")
+                print("🔄 Token limit, switching token...")
             else:
-                # Jika error aneh (misal parameter salah), print tapi tetap coba retry sekali lagi
-                pass
+                pass # Tetap retry siapa tau network glitch
             
             attempt += 1
-            time.sleep(1) # Jeda sebentar
+            time.sleep(1)
+
+    return None # Gagal total
 
-    return None # Gagal total setelah 5x percobaan
+# --- 3. UI BLOCKS (Solusi Error 'allow_flagging') ---
+# Menggunakan gr.Blocks seperti contohmu, BUKAN gr.Interface
+with gr.Blocks() as app:
+    # Invisible inputs untuk API endpoint
+    with gr.Row(visible=False):
+        prompt = gr.Textbox(label="Prompt")
+        width = gr.Number(value=1024, label="Width")
+        height = gr.Number(value=1024, label="Height")
+        guidance = gr.Number(value=3.5, label="Guidance")
+        steps = gr.Number(value=28, label="Steps")
+        seed = gr.Number(value=-1, label="Seed")
+    
+    # Output Image
+    out_image = gr.Image(label="Result")
+    
+    # Tombol Trigger (Hidden juga gapapa, yg penting ada event click)
+    btn_gen = gr.Button("Generate", visible=False)
+    
+    # --- API ENDPOINT DEFINITION ---
+    # api_name="generate" membuat endpoint: /api/generate
+    btn_gen.click(
+        process_generation, 
+        inputs=[prompt, width, height, guidance, steps, seed], 
+        outputs=out_image, 
+        api_name="generate"
+    )
 
-# --- 3. GRADIO INTERFACE ---
-# Kita buat Interface sederhana agar terbentuk endpoint API otomatis
-# Input disesuaikan dengan parameter Flux
-iface = gr.Interface(
-    fn=generate_image,
-    inputs=[
-        gr.Textbox(label="Prompt"),
-        gr.Number(value=1024, label="Width"),
-        gr.Number(value=1024, label="Height"),
-        gr.Number(value=3.5, label="Guidance Scale"),
-        gr.Number(value=28, label="Steps"),
-        gr.Number(value=-1, label="Seed (-1 for Random)"),
-    ],
-    outputs=gr.Image(label="Result"),
-    title="Flux Load Balancer API",
-    description="API Proxy untuk black-forest-labs-flux-2-dev dengan Token Rotation.",
-    allow_flagging="never"
-)
+    # Tambahan: Load event agar bisa testing lewat GUI jika perlu
+    # Tapi utamanya ini buat API
+    with gr.Row():
+        gr.Markdown("## Flux Load Balancer API Ready")
 
-# Launch Server
+# Launch
 if __name__ == "__main__":
-    iface.queue(max_size=20).launch(server_name="0.0.0.0", server_port=7860)
-    
+    app.queue(max_size=20).launch(server_name="0.0.0.0", server_port=7860)