Update app.py

app.py

@@ -1,7 +1,16 @@
 import gradio as gr
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, StoppingCriteria
-from modeling_llava_qwen2 import LlavaQwen2ForCausalLM
+import gradio as gr
+import spaces
+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
+
 from threading import Thread
 import re
 import time 
@@ -13,117 +22,138 @@ subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENT
 
 torch.set_default_device('cuda')
 
-tokenizer = AutoTokenizer.from_pretrained(
-    'qnguyen3/nanoLLaVA-1.5',
-    trust_remote_code=True)
-
-model = LlavaQwen2ForCausalLM.from_pretrained(
-    'qnguyen3/nanoLLaVA-1.5',
-    torch_dtype=torch.float16,
-    attn_implementation="flash_attention_2",
-    trust_remote_code=True)
-
-model.to('cuda')
-
-class KeywordsStoppingCriteria(StoppingCriteria):
-    def __init__(self, keywords, tokenizer, input_ids):
-        self.keywords = keywords
-        self.keyword_ids = []
-        self.max_keyword_len = 0
-        for keyword in keywords:
-            cur_keyword_ids = tokenizer(keyword).input_ids
-            if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
-                cur_keyword_ids = cur_keyword_ids[1:]
-            if len(cur_keyword_ids) > self.max_keyword_len:
-                self.max_keyword_len = len(cur_keyword_ids)
-            self.keyword_ids.append(torch.tensor(cur_keyword_ids))
-        self.tokenizer = tokenizer
-        self.start_len = input_ids.shape[1]
-        
-    def call_for_batch(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
-        self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
-        for keyword_id in self.keyword_ids:
-            truncated_output_ids = output_ids[0, -keyword_id.shape[0]:]
-            if torch.equal(truncated_output_ids, keyword_id):
-                return True
-        outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
-        for keyword in self.keywords:
-            if keyword in outputs:
-                return True
-        return False
-        
-    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        outputs = []
-        for i in range(output_ids.shape[0]):
-            outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores))
-        return all(outputs)
+
+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):
+    image = Image.open(image_file).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 = AutoModel.from_pretrained(
+    "5CD-AI/Viet-InternVL2-1B",
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+).eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained("5CD-AI/Viet-InternVL2-1B", trust_remote_code=True, use_fast=False)
 
 
 @spaces.GPU
-def bot_streaming(message, history):
-    messages = []
-    if message["files"]:
-      image = message["files"][-1]["path"]
+def chat(message, history):
+    print(history)
+    print(message)
+    if len(history) == 0 or len(message["files"]) != 0:
+        test_image = message["files"][0]["path"]
     else:
-      for i, hist in enumerate(history):
-        if type(hist[0])==tuple:
-          image = hist[0][0]
-          image_turn = i
-            
-    if len(history) > 0 and image is not None:
-        messages.append({"role": "user", "content": f'<image>\n{history[1][0]}'})
-        messages.append({"role": "assistant", "content": history[1][1] })
-        for human, assistant in history[2:]:
-            messages.append({"role": "user", "content": human })
-            messages.append({"role": "assistant", "content": assistant })
-        messages.append({"role": "user", "content": message['text']})
-    elif len(history) > 0 and image is None:
-        for human, assistant in history:
-            messages.append({"role": "user", "content": human })
-            messages.append({"role": "assistant", "content": assistant })
-        messages.append({"role": "user", "content": message['text']})
-    elif len(history) == 0 and image is not None:
-        messages.append({"role": "user", "content": f"<image>\n{message['text']}"})
-    elif len(history) == 0 and image is None:
-        messages.append({"role": "user", "content": message['text'] })
-
-    # if image is None:
-    #     gr.Error("You need to upload an image for LLaVA to work.")
-    image = Image.open(image).convert("RGB")
-    text = tokenizer.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True)
-    text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
-    input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
-    stop_str = '<|im_end|>'
-    keywords = [stop_str]
-    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
-    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+        test_image = history[0][0][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)
+    
     
-    image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)
-    generation_kwargs = dict(input_ids=input_ids.to('cuda'), 
-                             images=image_tensor.to('cuda'), 
-                             streamer=streamer, max_new_tokens=512, 
-                             stopping_criteria=[stopping_criteria], temperature=0.01)
-    generated_text = ""
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-    text_prompt =f"<|im_start|>user\n{message['text']}<|im_end|>"
     
+    if len(history) == 0:
+        question = '<image>\n'+message["text"]
+        response, conv_history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+    else:
+        conv_history = []
+        for chat_pair in history:
+            if chat_pair[1] is not None:
+                if len(conv_history) == 0 and len(message["files"]) == 0:
+                    chat_pair[0] = '<image>\n' + chat_pair[0]
+                conv_history.append(tuple(chat_pair))
+        print(conv_history)
+        if len(message["files"]) != 0:
+            question = '<image>\n'+message["text"]
+        else:
+            question = message["text"]
+        response, conv_history = model.chat(tokenizer, pixel_values, question, generation_config, history=conv_history, return_history=True)
+        
+    print(f'User: {question}\nAssistant: {response}')
+
     buffer = ""
-    for new_text in streamer:
-      
+    for new_text in response:
       buffer += new_text
-      
       generated_text_without_prompt = buffer[:]
-      time.sleep(0.04)
+      time.sleep(0.06)
       yield generated_text_without_prompt
+    # return response
 
-
-demo = gr.ChatInterface(fn=bot_streaming, title="🚀nanoLLaVA-1.5", examples=[{"text": "Who is this guy?", "files":["./demo_1.jpg"]},
-                                                                      {"text": "What does the text say?", "files":["./demo_2.jpeg"]}], 
-                        description="Try [nanoLLaVA](https://huggingface.co/qnguyen3/nanoLLaVA-1.5) in this demo. Built on top of [Quyen-SE-v0.1](https://huggingface.co/vilm/Quyen-SE-v0.1) (Qwen1.5-0.5B) and [Google SigLIP-400M](https://huggingface.co/google/siglip-so400m-patch14-384). Upload an image and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error.",
-                        stop_btn="Stop Generation", multimodal=True)
+demo = gr.ChatInterface(
+    fn=chat,
+    chatbot=gr.Chatbot(height=500),
+    description="""Try [Vintern-1B](https://huggingface.co/5CD-AI/Viet-InternVL2-1B) in this demo. Vintern 1B is a multimodal large language model series, featuring models of various sizes. For each size, we release instruction-tuned models optimized for multimodal tasks. Vintern-1B consists of InternViT-300M-448px, an MLP projector, and Qwen2-0.5B-Instruct.""",
+    examples=[{"text": "Tổng giá tiền trong hóa đơn là bao nhiêu ?", "files":["./demo_1.jpg"]}, 
+              {"text": "Mô tả hình ảnh một cách chi tiết.", "files":["./demo_2.jpg"]}],
+    title="❄️ Vintern-1B ❄️",
+    multimodal=True,
+)
 demo.queue().launch()