added application

app.py

@@ -0,0 +1,213 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, StoppingCriteria
+import gradio as gr
+import torch
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+import os
+from threading import Thread
+import re
+import time 
+from PIL import Image
+import torch
+import spaces
+import subprocess
+from time import sleep
+import base64
+from io import BytesIO
+
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+# torch.set_default_device('cuda')
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    if isinstance(image_file, str):  # Check if it's a file path
+        image = Image.open(image_file).convert('RGB')
+    else:  # Assume it's a base64 string
+        image_data = base64.b64decode(image_file)
+        image = Image.open(BytesIO(image_data)).convert('RGB')
+    
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+model_name = "lycaoduong/KLPintern-v2-1B"
+
+access_token = os.getenv("LPInternVL21B")
+
+model = AutoModel.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+    token=access_token
+).eval()
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False, token=access_token)
+
+
+@spaces.GPU
+def chat(message, history):
+    # print(history)
+    # print(message)
+    model.to('cuda')
+    
+    test_image = message["files"][0]
+        
+    pixel_values = load_image(test_image, max_num=12).to(torch.bfloat16).cuda()
+    generation_config = dict(max_new_tokens= 1024, do_sample=True, num_beams = 3, repetition_penalty=2.5)
+    
+    question = '<image>\n'+message["text"]
+    response, conv_history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+
+    # print(f'User: {question}\nAssistant: {response}')
+
+    txt_stream = ''
+    for c in response:
+        sleep(0.01)
+        txt_stream = txt_stream + c
+        yield txt_stream
+
+    # return response
+
+
+CSS ="""
+# @media only screen and (max-width: 600px){
+#     #component-3 {
+#       height: 90dvh !important;
+#       transform-origin: top; /* Ensure that the element expands from top to bottom. */
+#       border-style: solid;
+#       overflow: hidden;
+#       flex-grow: 1;
+#       min-width: min(160px, 100%);
+#       border-width: var(--block-border-width);
+#     }
+# }
+#component-3 {
+  height: 50dvh !important;
+  transform-origin: top; /* Ensure that the element expands from top to bottom. */
+  border-style: solid;
+  overflow: hidden;
+  flex-grow: 1;
+  min-width: min(160px, 100%);
+  border-width: var(--block-border-width);
+}
+/* Ensure that the image inside the button is displayed correctly for buttons with a specified aria-label. */
+button.svelte-1lcyrx4[aria-label="user's message: a file of type image/jpeg, "] img.svelte-1pijsyv {
+  width: 100%;
+  object-fit: contain;
+  height: 100%;
+  border-radius: 13px; /* Add rounded corners to the image. */
+  max-width: 50vw;     /* Limit the image width. */
+}
+/* Set the height for the button and allow text selection only for buttons with a specified aria-label. */
+button.svelte-1lcyrx4[aria-label="user's message: a file of type image/jpeg, "] {
+  user-select: text;
+  text-align: left;
+  height: 300px;
+}
+/* Add border-radius and limit the width for images that do not belong to the avatar container. */
+.message-wrap.svelte-1lcyrx4 > div.svelte-1lcyrx4 .svelte-1lcyrx4:not(.avatar-container) img {
+  border-radius: 13px;
+  max-width: 50vw;
+}
+.message-wrap.svelte-1lcyrx4 .message.svelte-1lcyrx4 img {
+    margin: var(--size-2);
+    max-height: 500px;
+}
+"""
+
+
+demo = gr.ChatInterface(
+    fn=chat,
+    description="Test Korean License Plate OCR VLMs in this demo.",
+    examples=[{"text": "Extract LPR information, return in JSON format.", "files":["./demo/P0.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P1.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P2.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P3.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P4.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P5.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P6.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P7.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P8.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P9.jpg"]},
+              {"text": "Extract LPR information.", "files":["./demo/P10.jpg"]},
+                ],
+    title="❄️ InternVL2-1B fine-tuned for Korean License Plate recognition.❄️",
+    multimodal=True,
+    css=CSS
+)
+demo.queue().launch()

requirements.txt

@@ -0,0 +1,13 @@
+torch
+# git+https://github.com/huggingface/transformers.git
+spaces
+pillow
+accelerate
+pypandoc
+fastapi
+wheel
+torchvision
+imageio
+timm
+transformers==4.44.2
+gradio==4.44.1