Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, AutoModelForImageTextToText
+import torch
+from PIL import Image
+
+# 1. Konfigurasi Model
+# Pake CPU kalau gak ada GPU, pake cuda kalau ada
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+MODEL_PATH = "zai-org/GLM-OCR"
+
+print(f"Loading model ke {device}...")
+
+# 2. Load Processor dan Model
+try:
+    processor = AutoProcessor.from_pretrained(MODEL_PATH, trust_remote_code=True)
+    model = AutoModelForImageTextToText.from_pretrained(
+        MODEL_PATH,
+        torch_dtype=dtype,
+        device_map="auto",
+        trust_remote_code=True
+    )
+except Exception as e:
+    print(f"Error loading model: {e}")
+    raise e
+
+# 3. Fungsi Inferensi
+def run_ocr(image):
+    if image is None:
+        return "Tolong upload gambar dulu, Bro."
+
+    # Format pesan khusus GLM-OCR
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": image, # Gradio ngasih format PIL Image
+                },
+                {
+                    "type": "text",
+                    "text": "Text Recognition:" 
+                }
+            ],
+        }
+    ]
+
+    # Proses input
+    inputs = processor.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt"
+    ).to(model.device)
+
+    # Generate (OCR)
+    with torch.no_grad():
+        generated_ids = model.generate(**inputs, max_new_tokens=2048) # Token bisa dinaikin kalo dokumen panjang
+        
+    # Decode hasil jadi teks
+    output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+    
+    return output_text
+
+# 4. Bikin Tampilan UI (Gradio)
+with gr.Blocks() as demo:
+    gr.Markdown("# 👁️ GLM-OCR Demo")
+    gr.Markdown("Upload gambar dokumen, surat, atau teks tulisan tangan. Model ini jago baca layout.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(type="pil", label="Upload Gambar")
+            btn_submit = gr.Button("Baca Teks (OCR)", variant="primary")
+        
+        with gr.Column():
+            output_txt = gr.Textbox(label="Hasil Text / Markdown", lines=20)
+            
+    btn_submit.click(fn=run_ocr, inputs=input_img, outputs=output_txt)
+
+# Jalankan App
+demo.launch()