Create main.py

main.py

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+import os
+import uuid
+import time
+from threading import Thread
+
+import gradio as gr
+import torch
+from PIL import Image
+from transformers import (
+    Qwen2VLForConditionalGeneration,
+    AutoProcessor,
+    TextIteratorStreamer,
+)
+
+# Constants
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# Load olmOCR-7B-0225-preview
+MODEL_ID = "allenai/olmOCR-7B-0225-preview"
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+def generate_image(text: str, image: Image.Image,
+                   max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,
+                   temperature: float = 0.6,
+                   top_p: float = 0.9,
+                   top_k: int = 50,
+                   repetition_penalty: float = 1.2):
+    """
+    Generates responses using olmOCR-7B-0225-preview for image input.
+    """
+    if image is None:
+        yield "Please upload an image.", "Please upload an image."
+        return
+
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": text},
+        ]
+    }]
+    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(
+        text=[prompt_full],
+        images=[image],
+        return_tensors="pt",
+        padding=True,
+        truncation=False,
+        max_length=MAX_INPUT_TOKEN_LENGTH
+    ).to(device)
+
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        time.sleep(0.01)
+        yield buffer, buffer
+
+def save_to_md(output_text):
+    file_path = f"result_{uuid.uuid4()}.md"
+    with open(file_path, "w") as f:
+        f.write(output_text)
+    return file_path
+
+# Gradio UI
+image_examples = [
+    ["Convert this page to doc [text] precisely.", "images/3.png"],
+    ["Convert this page to doc [text] precisely.", "images/4.png"],
+    ["Convert this page to doc [text] precisely.", "images/1.png"],
+    ["Convert chart to OTSL.", "images/2.png"]
+]
+
+css = """
+.submit-btn {
+    background-color: #2980b9 !important;
+    color: white !important;
+}
+.submit-btn:hover {
+    background-color: #3498db !important;
+}
+.canvas-output {
+    border: 2px solid #4682B4;
+    border-radius: 10px;
+    padding: 20px;
+}
+"""
+
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    gr.Markdown("# **Doc OCR - olmOCR-7B-0225-preview**")
+
+    with gr.Row():
+        with gr.Column():
+            image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
+            image_upload = gr.Image(type="pil", label="Upload Image")
+            image_submit = gr.Button("Submit", elem_classes="submit-btn")
+            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=0.6)
+                top_p = gr.Slider(label="Top-p", 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=1.2)
+
+        with gr.Column():
+            with gr.Column(elem_classes="canvas-output"):
+                gr.Markdown("## Output")
+                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2)
+                with gr.Accordion("Result.md", open=False):
+                    markdown_output = gr.Markdown(label="(Result.md)")
+
+            gr.Markdown("**Model: olmOCR-7B-0225-preview**")
+            gr.Markdown("> [`olmOCR-7B`](https://huggingface.co/allenai/olmOCR-7B-0225-preview) is optimized for high-fidelity document OCR and LaTeX-aware image-to-text tasks.")
+
+    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(share=True, mcp_server=True, ssr_mode=False, show_error=True)