app.py

import gradio as gr
from transformers.image_utils import load_image
from threading import Thread
import time
import torch
import spaces
import cv2
import numpy as np
from PIL import Image
from transformers import (
    Qwen2VLForConditionalGeneration,
    AutoProcessor,
    TextIteratorStreamer,
)
from transformers import Qwen2_5_VLForConditionalGeneration

# ---------------------------
# Helper Functions
# ---------------------------
def progress_bar_html(label: str, primary_color: str = "#4B0082", secondary_color: str = "#9370DB") -> str:
    """
    Returns an HTML snippet for a thin animated progress bar with a label.
    Colors can be customized; default colors are used for Qwen2VL/Aya‑Vision.
    """
    return f'''
<div style="display: flex; align-items: center;">
    <span style="margin-right: 10px; font-size: 14px;">{label}</span>
    <div style="width: 110px; height: 5px; background-color: {secondary_color}; border-radius: 2px; overflow: hidden;">
        <div style="width: 100%; height: 100%; background-color: {primary_color}; animation: loading 1.5s linear infinite;"></div>
    </div>
</div>
<style>
@keyframes loading {{
    0% {{ transform: translateX(-100%); }}
    100% {{ transform: translateX(100%); }}
}}
</style>
    '''

def downsample_video(video_path):
    """
    Downsamples a video file by extracting 10 evenly spaced frames.
    Returns a list of tuples (PIL.Image, timestamp).
    """
    vidcap = cv2.VideoCapture(video_path)
    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = vidcap.get(cv2.CAP_PROP_FPS)
    frames = []
    if total_frames <= 0 or fps <= 0:
        vidcap.release()
        return frames
    # Determine 10 evenly spaced frame indices.
    frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)
    for i in frame_indices:
        vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
        success, image = vidcap.read()
        if success:
            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            pil_image = Image.fromarray(image)
            timestamp = round(i / fps, 2)
            frames.append((pil_image, timestamp))
    vidcap.release()
    return frames

# Model and Processor Setup
# Qwen2VL OCR (default branch)
QV_MODEL_ID = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct"  # [or] prithivMLmods/Qwen2-VL-OCR2-2B-Instruct
qwen_processor = AutoProcessor.from_pretrained(QV_MODEL_ID, trust_remote_code=True)
qwen_model = Qwen2VLForConditionalGeneration.from_pretrained(
    QV_MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.float16
).to("cuda").eval()

# RolmOCR branch (@RolmOCR)
ROLMOCR_MODEL_ID = "reducto/RolmOCR" 
rolmocr_processor = AutoProcessor.from_pretrained(ROLMOCR_MODEL_ID, trust_remote_code=True)
rolmocr_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    ROLMOCR_MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
).to("cuda").eval()

# Main Inference Function
@spaces.GPU
def model_inference(input_dict, history):
    text = input_dict["text"].strip()
    files = input_dict.get("files", [])

    # RolmOCR Inference (@RolmOCR)
    if text.lower().startswith("@rolmocr"):
        # Remove the tag from the query.
        text_prompt = text[len("@rolmocr"):].strip()
        # Check if a video is provided for inference.
        if files and isinstance(files[0], str) and files[0].lower().endswith((".mp4", ".avi", ".mov")):
            video_path = files[0]
            frames = downsample_video(video_path)
            if not frames:
                yield "Error: Could not extract frames from the video."
                return
            # Build the message: prompt followed by each frame with its timestamp.
            content_list = [{"type": "text", "text": text_prompt}]
            for image, timestamp in frames:
                content_list.append({"type": "text", "text": f"Frame {timestamp}:"})
                content_list.append({"type": "image", "image": image})
            messages = [{"role": "user", "content": content_list}]
            # For video, extract images only.
            video_images = [image for image, _ in frames]
            prompt_full = rolmocr_processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
            inputs = rolmocr_processor(
                text=[prompt_full],
                images=video_images,
                return_tensors="pt",
                padding=True,
            ).to("cuda")
        else:
            # Assume image(s) or text query.
            if len(files) > 1:
                images = [load_image(image) for image in files]
            elif len(files) == 1:
                images = [load_image(files[0])]
            else:
                images = []
            if text_prompt == "" and not images:
                yield "Error: Please input a text query and/or provide an image for the @RolmOCR feature."
                return
            messages = [{
                "role": "user",
                "content": [
                    *[{"type": "image", "image": image} for image in images],
                    {"type": "text", "text": text_prompt},
                ],
            }]
            prompt_full = rolmocr_processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
            inputs = rolmocr_processor(
                text=[prompt_full],
                images=images if images else None,
                return_tensors="pt",
                padding=True,
            ).to("cuda")
        streamer = TextIteratorStreamer(rolmocr_processor, skip_prompt=True, skip_special_tokens=True)
        generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
        thread = Thread(target=rolmocr_model.generate, kwargs=generation_kwargs)
        thread.start()
        buffer = ""
        # Use a different color scheme for RolmOCR (purple-themed).
        yield progress_bar_html("Processing with Qwen2.5VL (RolmOCR)")
        for new_text in streamer:
            buffer += new_text
            buffer = buffer.replace("<|im_end|>", "")
            time.sleep(0.01)
            yield buffer
        return

    # Default Inference: Qwen2VL OCR
    # Process files: support multiple images.
    if len(files) > 1:
        images = [load_image(image) for image in files]
    elif len(files) == 1:
        images = [load_image(files[0])]
    else:
        images = []
    
    if text == "" and not images:
        yield "Error: Please input a text query and optionally image(s)."
        return
    if text == "" and images:
        yield "Error: Please input a text query along with the image(s)."
        return

    messages = [{
        "role": "user",
        "content": [
            *[{"type": "image", "image": image} for image in images],
            {"type": "text", "text": text},
        ],
    }]
    prompt_full = qwen_processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = qwen_processor(
        text=[prompt_full],
        images=images if images else None,
        return_tensors="pt",
        padding=True,
    ).to("cuda")
    streamer = TextIteratorStreamer(qwen_processor, skip_prompt=True, skip_special_tokens=True)
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
    thread = Thread(target=qwen_model.generate, kwargs=generation_kwargs)
    thread.start()
    buffer = ""
    yield progress_bar_html("Processing with Qwen2VL OCR")
    for new_text in streamer:
        buffer += new_text
        buffer = buffer.replace("<|im_end|>", "")
        time.sleep(0.01)
        yield buffer

# Gradio Interface
examples = [
    [{"text": "@RolmOCR OCR the Text in the Image", "files": ["rolm/1.jpeg"]}],
    [{"text": "@RolmOCR Explain the Ad in Detail", "files": ["examples/videoplayback.mp4"]}],
    [{"text": "@RolmOCR OCR the Image", "files": ["rolm/3.jpeg"]}],
    [{"text": "Extract as JSON table from the table", "files": ["examples/4.jpg"]}],
]

demo = gr.ChatInterface(
    fn=model_inference,
    description="# **Multimodal OCR `@RolmOCR and Default Qwen2VL OCR`**",
    examples=examples,
    textbox=gr.MultimodalTextbox(
        label="Query Input", 
        file_types=["image", "video"], 
        file_count="multiple", 
        placeholder="Use tag @RolmOCR for RolmOCR, or leave blank for default Qwen2VL OCR"
    ),
    stop_btn="Stop Generation",
    multimodal=True,
    cache_examples=False,
)

demo.launch(debug=True)