Update app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModel, AutoTokenizer
+import spaces
+import os
+import tempfile
+from PIL import Image, ImageDraw
+import re # Import thư viện regular expression
+
+# --- 1. Load Model and Tokenizer (Done only once at startup) ---
+print("Loading model and tokenizer...")
+model_name = "deepseek-ai/DeepSeek-OCR"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+# Load the model to CPU first; it will be moved to GPU during processing
+model = AutoModel.from_pretrained(
+    model_name,
+    _attn_implementation="flash_attention_2",
+    trust_remote_code=True,
+    use_safetensors=True,
+)
+model = model.eval()
+print("✅ Model loaded successfully.")
+
+# --- Helper function to find pre-generated result images ---
+def find_result_image(path):
+    for filename in os.listdir(path):
+        if "grounding" in filename or "result" in filename:
+            try:
+                image_path = os.path.join(path, filename)
+                return Image.open(image_path)
+            except Exception as e:
+                print(f"Error opening result image {filename}: {e}")
+    return None
+
+# --- 2. Main Processing Function (UPDATED for multi-bbox drawing) ---
+@spaces.GPU
+def process_ocr_task(image, model_size, task_type, ref_text):
+    """
+    Processes an image with DeepSeek-OCR for all supported tasks.
+    Now draws ALL detected bounding boxes for ANY task.
+    """
+    if image is None:
+        return "Please upload an image first.", None
+
+    print("🚀 Moving model to GPU...")
+    model_gpu = model.cuda().to(torch.bfloat16)
+    print("✅ Model is on GPU.")
+
+    with tempfile.TemporaryDirectory() as output_path:
+        # Build the prompt... (same as before)
+        if task_type == "📝 Free OCR":
+            prompt = "<image>\nFree OCR."
+        elif task_type == "📄 Convert to Markdown":
+            prompt = "<image>\n<|grounding|>Convert the document to markdown."
+        elif task_type == "📈 Parse Figure":
+            prompt = "<image>\nParse the figure."
+        elif task_type == "🔍 Locate Object by Reference":
+            if not ref_text or ref_text.strip() == "":
+                raise gr.Error("For the 'Locate' task, you must provide the reference text to find!")
+            prompt = f"<image>\nLocate <|ref|>{ref_text.strip()}<|/ref|> in the image."
+        else:
+            prompt = "<image>\nFree OCR."
+
+        temp_image_path = os.path.join(output_path, "temp_image.png")
+        image.save(temp_image_path)
+
+        # Configure model size... (same as before)
+        size_configs = {
+            "Tiny": {"base_size": 512, "image_size": 512, "crop_mode": False},
+            "Small": {"base_size": 640, "image_size": 640, "crop_mode": False},
+            "Base": {"base_size": 1024, "image_size": 1024, "crop_mode": False},
+            "Large": {"base_size": 1280, "image_size": 1280, "crop_mode": False},
+            "Gundam (Recommended)": {"base_size": 1024, "image_size": 640, "crop_mode": True},
+        }
+        config = size_configs.get(model_size, size_configs["Gundam (Recommended)"])
+
+        print(f"🏃 Running inference with prompt: {prompt}")
+        text_result = model_gpu.infer(
+            tokenizer,
+            prompt=prompt,
+            image_file=temp_image_path,
+            output_path=output_path,
+            base_size=config["base_size"],
+            image_size=config["image_size"],
+            crop_mode=config["crop_mode"],
+            save_results=True,
+            test_compress=True,
+            eval_mode=True,
+        )
+
+        print(f"====\n📄 Text Result: {text_result}\n====")
+
+        # --- NEW LOGIC: Always try to find and draw all bounding boxes ---
+        result_image_pil = None
+        
+        # Define the pattern to find all coordinates like [[280, 15, 696, 997]]
+        pattern = re.compile(r"<\|det\|>\[\[(\d+),\s*(\d+),\s*(\d+),\s*(\d+)\]\]<\|/det\|>")
+        matches = list(pattern.finditer(text_result)) # Use finditer to get all matches
+
+        if matches:
+            print(f"✅ Found {len(matches)} bounding box(es). Drawing on the original image.")
+            
+            # Create a copy of the original image to draw on
+            image_with_bboxes = image.copy()
+            draw = ImageDraw.Draw(image_with_bboxes)
+            w, h = image.size # Get original image dimensions
+
+            for match in matches:
+                # Extract coordinates as integers
+                coords_norm = [int(c) for c in match.groups()]
+                x1_norm, y1_norm, x2_norm, y2_norm = coords_norm
+                
+                # Scale the normalized coordinates (from 1000x1000 space) to the image's actual size
+                x1 = int(x1_norm / 1000 * w)
+                y1 = int(y1_norm / 1000 * h)
+                x2 = int(x2_norm / 1000 * w)
+                y2 = int(y2_norm / 1000 * h)
+                
+                # Draw the rectangle with a red outline, 3 pixels wide
+                draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
+            
+            result_image_pil = image_with_bboxes
+        else:
+            # If no coordinates are found in the text, fall back to finding a pre-generated image
+            print("⚠️ No bounding box coordinates found in text result. Falling back to search for a result image file.")
+            result_image_pil = find_result_image(output_path)
+            
+        return text_result, result_image_pil
+
+
+# --- 3. Build the Gradio Interface (UPDATED) ---
+with gr.Blocks(title="🐳DeepSeek-OCR🐳", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🐳 Full Demo of DeepSeek-OCR 🐳
+        **💡 How to use:**
+        1.  **Upload an image** using the upload box.
+        2.  Select a **Resolution**. `Gundam` is recommended for most documents.
+        3.  Choose a **Task Type**:
+            - **📝 Free OCR**: Extracts raw text from the image.
+            - **📄 Convert to Markdown**: Converts the document into Markdown, preserving structure.
+            - **📈 Parse Figure**: Extracts structured data from charts and figures.
+            - **🔍 Locate Object by Reference**: Finds a specific object/text.
+        4. If this helpful, please give it a like! 🙏 ❤️
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", label="🖼️ Upload Image", sources=["upload", "clipboard"])
+            model_size = gr.Dropdown(choices=["Tiny", "Small", "Base", "Large", "Gundam (Recommended)"], value="Gundam (Recommended)", label="⚙️ Resolution Size")
+            task_type = gr.Dropdown(choices=["📝 Free OCR", "📄 Convert to Markdown", "📈 Parse Figure", "🔍 Locate Object by Reference"], value="📄 Convert to Markdown", label="🚀 Task Type")
+            ref_text_input = gr.Textbox(label="📝 Reference Text (for Locate task)", placeholder="e.g., the teacher, 20-10, a red car...", visible=False)
+            submit_btn = gr.Button("Process Image", variant="primary")
+
+        with gr.Column(scale=2):
+            output_text = gr.Textbox(label="📄 Text Result", lines=15, show_copy_button=True)
+            output_image = gr.Image(label="🖼️ Image Result (if any)", type="pil")
+
+    # --- UI Interaction Logic ---
+    def toggle_ref_text_visibility(task):
+        return gr.Textbox(visible=True) if task == "🔍 Locate Object by Reference" else gr.Textbox(visible=False)
+
+    task_type.change(fn=toggle_ref_text_visibility, inputs=task_type, outputs=ref_text_input)
+    submit_btn.click(fn=process_ocr_task, inputs=[image_input, model_size, task_type, ref_text_input], outputs=[output_text, output_image])
+
+    # --- UPDATED Example Images and Tasks ---
+    gr.Examples(
+        examples=[
+            ["doc_markdown.png", "Gundam (Recommended)", "📄 Convert to Markdown", ""],
+            ["chart.png", "Gundam (Recommended)", "📈 Parse Figure", ""],
+            ["teacher.jpg", "Base", "🔍 Locate Object by Reference", "the teacher"],
+            ["math_locate.jpg", "Small", "🔍 Locate Object by Reference", "20-10"],
+            ["receipt.jpg", "Base", "📝 Free OCR", ""],
+        ],
+        inputs=[image_input, model_size, task_type, ref_text_input],
+        outputs=[output_text, output_image],
+        fn=process_ocr_task,
+        cache_examples=False, # Disable caching to ensure examples run every time
+    )
+
+# --- 4. Launch the App ---
+if __name__ == "__main__":
+    if not os.path.exists("examples"):
+        os.makedirs("examples")
+    # Make sure to have the correct image files in your "examples" folder
+    # e.g., doc_markdown.png, chart.png, teacher.jpg, math_locate.jpg, receipt.jpg
+    
+    demo.queue(max_size=20).launch(share=True)