Update app.py

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
-from transformers import AutoProcessor, AutoModel
+from transformers import AutoProcessor, AutoModelForImageTextToText
 import torch
 from PIL import Image
 
-# --- KONFIGURASI ALAM SEMESTA ---
+# --- KONFIGURASI INTI ---
 MODEL_PATH = "zai-org/GLM-OCR"
 
-# 1. HUKUM KEKALAN HARDWARE (Otomatis Deteksi CPU/GPU)
+# 1. HUKUM KEKALAN HARDWARE (Auto Deteksi CPU/GPU)
 if torch.cuda.is_available():
     device = "cuda"
     dtype = torch.float16
@@ -16,90 +16,108 @@ else:
 
 print(f"🚀 ENGINE STARTED: Device={device} | Dtype={dtype}")
 
-# 2. INISIASI MODEL (TRUST REMOTE CODE MUTLAK)
+# 2. INISIASI MODEL (DENGAN SAFE LOAD)
 try:
-    print("⏳ Menyiapkan Otak Buatan...")
+    print("⏳ Menyiapkan Otak GLM...")
+    
+    # Processor (Penyortir Data)
     processor = AutoProcessor.from_pretrained(
         MODEL_PATH, 
         trust_remote_code=True
     )
     
-    # Kita pakai AutoModel karena GLM-OCR arsitekturnya unik
-    model = AutoModel.from_pretrained(
+    # Model (Otak Utama) - Balik ke AutoModelForImageTextToText karena Library GIT udah support
+    model = AutoModelForImageTextToText.from_pretrained(
         MODEL_PATH,
         torch_dtype=dtype,
         trust_remote_code=True,
         low_cpu_mem_usage=True,
         device_map="auto"
     )
-    # Kunci model ke mode evaluasi biar irit memori
-    model.eval() 
+    
+    # Mode Hemat Energi (Evaluasi)
+    model.eval()
 
 except Exception as e:
-    print(f"⚠️ Warning Loading Model (Abaikan jika UI Muncul): {e}")
-    # Kita biarkan script lanjut karena warning weights mismatch itu wajar di custom model
+    print(f"⚠️ Warning Model Loading: {e}")
+    # Biasanya warning doang, gas terus.
     pass
 
-# 3. PROSES INFERENSI (INTELIJEN VISUAL)
+# 3. PROSES REAKSI FISIKA (Input -> Tensor -> Output)
 def proses_intelijen(image):
     if image is None:
-        return "⚠️ Gambarnya mana Bro? Fisika butuh materi buat bereaksi."
+        return "⚠️ Upload dulu gambarnya Bos! Jangan scan angin."
 
-    # Format Prompt Khusus GLM
-    pesan = [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": "Text Recognition:"}]}]
+    # Format Pesan Sesuai Standar GLM
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": "Text Recognition:"}
+            ],
+        }
+    ]
 
     try:
+        # --- PERBAIKAN FATAL ADA DISINI ---
+        # tokenize=True adalah KUNCINYA. Biar dia jadi Tensor (Angka), bukan Teks doang.
         inputs = processor.apply_chat_template(
-            pesan,
+            messages,
             add_generation_prompt=True,
+            tokenize=True,      # <--- INI YG DULU KURANG!
             return_dict=True,
-            return_tensors="pt"
-        ).to(model.device)
+            return_tensors="pt" # Pastikan keluar format PyTorch
+        )
+        
+        # Buang data sampah yang gak perlu
+        inputs.pop("token_type_ids", None)
+        
+        # Pindahkan ke Mesin (Device) secara Manual biar aman
+        inputs = {k: v.to(device) for k, v in inputs.items()}
 
+        # GEBER MESINNYA (GENERATE)
         with torch.no_grad():
-            output_ids = model.generate(
+            generated_ids = model.generate(
                 **inputs, 
-                max_new_tokens=1500, # Batasi biar ga timeout
+                max_new_tokens=2048, # Naikkin dikit biar dokumen panjang kebaca
                 do_sample=False
             )
 
-        hasil = output_ids[0][len(inputs["input_ids"][0]):]
-        teks_final = processor.decode(hasil, skip_special_tokens=True)
+        # DECODE (Angka -> Teks Manusia)
+        # Kita potong input promptnya, ambil hasilnya doang
+        hasil_potong = generated_ids[0][inputs["input_ids"].shape[1]:]
+        teks_final = processor.decode(hasil_potong, skip_special_tokens=False) # Coba False dulu biar detail
+        
         return teks_final
 
     except Exception as e:
-        return f"🚨 ERROR REAKSI: {str(e)}"
-
-# 4. ANTARMUKA VISUAL 2026 (Modern Style)
-# HAPUS 'theme' dan 'show_copy_button' di sini biar 100% Anti-Error
+        return f"🚨 SYSTEM CRITICAL FAILURE: {str(e)}\n(Kemungkinan memori tidak cukup atau input rusak)"
 
+# 4. ANTARMUKA 2026 (UI yang Elegan tapi BODOH-PROOF)
 css_style = """
 .container { max-width: 1200px; margin: auto; padding-top: 20px; }
-h1 { text-align: center; color: #3b82f6; font-family: sans-serif; }
-.gr-button-primary { background: linear-gradient(90deg, #3b82f6 0%, #8b5cf6 100%); border: none; color: white; }
-.info { text-align: center; color: gray; font-size: 0.9em; margin-bottom: 20px;}
+h1 { text-align: center; color: #1e3a8a; font-family: sans-serif; font-weight: 900; }
+.btn-primary { background: linear-gradient(90deg, #1e3a8a 0%, #3b82f6 100%); color: white; border: none; font-size: 1.1rem; }
+.btn-primary:hover { opacity: 0.9; }
 """
 
-with gr.Blocks(css=css_style, title="GLM-OCR PRO") as app:
+with gr.Blocks(css=css_style, title="GLM-OCR V1") as app:
     with gr.Column(elem_classes="container"):
-        gr.Markdown("# 👁️ GLM-OCR INFINITE VISION")
-        gr.Markdown("<p class='info'>Analisis Dokumen Menggunakan Arsitektur Syaraf GLM Multimodal</p>")
+        gr.Markdown("# 👁️ GLM-OCR ULTRA SCANNER")
+        gr.Markdown("Ekstrak teks dari dokumen apapun. Upload, Klik, Jadi.")
         
         with gr.Row():
-            # Kolom Kiri: Input
+            # Kolom Input
             with gr.Column(scale=1):
-                input_img = gr.Image(type="pil", label="Masukkan Materi (Gambar)", sources=["upload", "clipboard"], height=500)
-                # Tombol Ganteng
-                scan_btn = gr.Button("🚀 EKSTRAKSI DATA", variant="primary", size="lg")
+                input_img = gr.Image(type="pil", label="Input Visual (Gambar)", sources=["upload", "clipboard"], height=450)
+                scan_btn = gr.Button("⚡ AKTIFKAN PEMINDAI", variant="primary", elem_classes="btn-primary")
             
-            # Kolom Kanan: Output
+            # Kolom Output (Pake Textbox biasa biar anti error 'show_copy_button')
             with gr.Column(scale=1):
-                # INI DIA FIXNYA: Ganti TextArea jadi Textbox dan HAPUS argumen ilegal
-                # Pake 'show_copy_button=True' HANYA jika komponen Textbox, tapi demi keamanan kita pake standar aja.
-                # interactive=False biar jadi read-only mode
-                output_txt = gr.Textbox(label="Hasil Analisis Molekuler", lines=25, interactive=False, show_label=True)
+                output_txt = gr.Textbox(label="Data Terekstraksi", lines=22, show_copy_button=True, interactive=False)
     
-    # Trigger Reaksi
+    # Pemicu
     scan_btn.click(fn=proses_intelijen, inputs=input_img, outputs=output_txt)
 
 if __name__ == "__main__":