ui

app.py

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+import gradio as gr
+from transformers import AutoProcessor, AutoModelForImageTextToText, TextIteratorStreamer
+from transformers.image_utils import load_image
+from threading import Thread
+import torch
+import spaces
+
+MODEL_ID = "TIGER-Lab/VL-Rethinker-7B"  
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+model = AutoModelForImageTextToText.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16
+).to("cuda").eval()
+
+@spaces.GPU
+def model_inference(input_dict, history):
+    text = input_dict["text"]
+    files = input_dict["files"]
+
+    """
+    Create chat history
+
+    Example history value:
+    [
+        [('pixel.png',), None], 
+        ['ignore this image. just say "hi" and nothing else', 'Hi!'], 
+        ['just say "hi" and nothing else', 'Hi!']
+    ]
+    """
+    all_images = []
+    current_message_images = []
+    messages = []
+
+    for val in history:
+        if val[0]:
+            if isinstance(val[0], str):
+                messages.append({
+                    "role": "user", 
+                    "content": [
+                        *[{"type": "image", "image": image} for image in current_message_images],
+                        {"type": "text", "text": val[0]},
+                    ],
+                })
+                current_message_images = []
+
+            else:
+                # Load messages. These will be appended to the first user text message that comes after
+                current_message_images = [load_image(image) for image in val[0]]
+                all_images += current_message_images
+
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+    
+    current_message_images = [load_image(image) for image in files]
+    all_images += current_message_images
+    messages.append({
+        "role": "user",
+        "content": [
+            *[{"type": "image", "image": image} for image in current_message_images],
+            {"type": "text", "text": text},
+        ],
+    })
+    
+    print(messages)
+
+    """
+    Generate and stream text
+    """
+    prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(
+        text=[prompt],
+        images=all_images if all_images else None,
+        return_tensors="pt",
+        padding=True,
+    ).to("cuda")
+    
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        yield buffer
+
+examples = [
+    [{"text": "Solve this question.", "files": ["example_images/document.png"]}]
+]
+
+demo = gr.ChatInterface(
+    fn=model_inference,
+    description="# **VL-Rethinker-7B**",
+    examples=examples,
+    fill_height=True,
+    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"),
+    stop_btn="Stop Generation",
+    multimodal=True,
+    cache_examples=False,
+)
+
+demo.launch(debug=True)

requirements.txt

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+gradio_client==1.3.0
+qwen-vl-utils==0.0.2
+transformers-stream-generator==0.0.4
+torch==2.4.0
+torchvision==0.19.0
+git+https://github.com/huggingface/transformers.git
+accelerate
+av
+opencv-python