Create app.py

app.py

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+import os
+import time
+from threading import Thread
+from typing import Iterable
+
+import gradio as gr
+import spaces
+import torch
+from PIL import Image
+
+from transformers import (
+    Qwen3VLForConditionalGeneration,
+    AutoProcessor,
+    TextIteratorStreamer,
+)
+
+from gradio.themes import Soft
+from gradio.themes.utils import colors, fonts, sizes
+
+# --- Theme Configuration ---
+colors.steel_blue = colors.Color(
+    name="steel_blue",
+    c50="#EBF3F8",
+    c100="#D3E5F0",
+    c200="#A8CCE1",
+    c300="#7DB3D2",
+    c400="#529AC3",
+    c500="#4682B4",
+    c600="#3E72A0",
+    c700="#36638C",
+    c800="#2E5378",
+    c900="#264364",
+    c950="#1E3450",
+)
+
+class SteelBlueTheme(Soft):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.gray,
+        secondary_hue: colors.Color | str = colors.steel_blue,
+        neutral_hue: colors.Color | str = colors.slate,
+        text_size: sizes.Size | str = sizes.text_lg,
+        font: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
+        ),
+        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            background_fill_primary="*primary_50",
+            background_fill_primary_dark="*primary_900",
+            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
+            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
+            button_primary_text_color="white",
+            button_primary_text_color_hover="white",
+            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
+            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
+            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_800)",
+            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_500)",
+            button_secondary_text_color="black",
+            button_secondary_text_color_hover="white",
+            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
+            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
+            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
+            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
+            slider_color="*secondary_500",
+            slider_color_dark="*secondary_600",
+            block_title_text_weight="600",
+            block_border_width="3px",
+            block_shadow="*shadow_drop_lg",
+            button_primary_shadow="*shadow_drop_lg",
+            button_large_padding="11px",
+            color_accent_soft="*primary_100",
+            block_label_background_fill="*primary_200",
+        )
+
+steel_blue_theme = SteelBlueTheme()
+
+css = """
+#main-title h1 {
+    font-size: 2.3em !important;
+}
+#output-title h2 {
+    font-size: 2.1em !important;
+}
+"""
+
+# --- Device & Model Setup ---
+MAX_MAX_NEW_TOKENS = 4096
+DEFAULT_MAX_NEW_TOKENS = 2048
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
+print("torch.__version__ =", torch.__version__)
+print("torch.version.cuda =", torch.version.cuda)
+print("cuda available:", torch.cuda.is_available())
+if torch.cuda.is_available():
+    print("current device:", torch.cuda.current_device())
+    print("device name:", torch.cuda.get_device_name(torch.cuda.current_device()))
+
+print("Using device:", device)
+
+MODEL_ID = "Qwen/Qwen3-VL-8B-Instruct"
+print(f"Loading model: {MODEL_ID}...")
+
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+model = Qwen3VLForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    attn_implementation="flash_attention_2",
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+print("Model loaded successfully.")
+
+# --- Generation Logic ---
+@spaces.GPU
+def generate_image(text: str, image: Image.Image,
+                   max_new_tokens: int, temperature: float, top_p: float,
+                   top_k: int, repetition_penalty: float):
+    """
+    Generates responses using the Chandra-OCR model.
+    Yields raw text and Markdown-formatted text.
+    """
+    if image is None:
+        yield "Please upload an image.", "Please upload an image."
+        return
+
+    # Prepare messages
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": text},
+        ]
+    }]
+    
+    # Apply template
+    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    # Process inputs
+    inputs = processor(
+        text=[prompt_full],
+        images=[image],
+        return_tensors="pt",
+        padding=True
+    ).to(device)
+
+    # Setup streamer
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    
+    generation_kwargs = {
+        **inputs,
+        "streamer": streamer,
+        "max_new_tokens": max_new_tokens,
+        "do_sample": True,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "repetition_penalty": repetition_penalty,
+    }
+
+    # Start generation thread
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        # Clean specific tokens if necessary
+        buffer = buffer.replace("<|im_end|>", "")
+        time.sleep(0.01)
+        yield buffer, buffer
+
+# --- Gradio Interface ---
+image_examples = [
+    ["OCR the content perfectly.", "examples/3.jpg"],
+    ["Perform OCR on the image.", "examples/1.jpg"],
+    ["Extract the contents. [page].", "examples/2.jpg"],
+]
+
+with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
+    gr.Markdown("# **vibe-voice**", elem_id="main-title")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
+            image_upload = gr.Image(type="pil", label="Upload Image", height=290)
+
+            image_submit = gr.Button("Submit", variant="primary")
+            
+            # Note: Ensure these example paths exist in your environment
+            gr.Examples(
+                examples=image_examples,
+                inputs=[image_query, image_upload]
+            )
+            
+            with gr.Accordion("Advanced options", open=False):
+                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
+                temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.7)
+                top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
+                top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
+                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.1)
+                
+        with gr.Column(scale=3):
+            gr.Markdown("## Output", elem_id="output-title")
+            output = gr.Textbox(label="Raw Output Stream", interactive=True, lines=11)
+            with gr.Accordion("(Result.md)", open=False):
+                markdown_output = gr.Markdown(label="(Result.Md)")
+
+    image_submit.click(
+        fn=generate_image,
+        inputs=[image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=[output, markdown_output]
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=30).launch(ssr_mode=False, show_error=True)