Spaces:
Running
Running
File size: 5,948 Bytes
b49897a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer
from qwen_vl_utils import process_vision_info
import torch
from PIL import Image
import subprocess
import numpy as np
import os
from threading import Thread
import uuid
import io
import re # Import regular expressions for word highlighting
# Model and Processor Loading (Done once at startup)
MODEL_ID = "Qwen/Qwen2-VL-2B-Instruct"
model = Qwen2VLForConditionalGeneration.from_pretrained(
MODEL_ID,
trust_remote_code=True,
torch_dtype=torch.float16
).to("cuda").eval()
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
DESCRIPTION = "[Qwen2-VL-2B Demo](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)"
# Define supported media extensions
image_extensions = Image.registered_extensions()
video_extensions = ("avi", "mp4", "mov", "mkv", "flv", "wmv", "mjpeg", "wav", "gif", "webm", "m4v", "3gp")
def identify_and_save_blob(blob_path):
"""Identifies if the blob is an image or video and saves it accordingly."""
try:
with open(blob_path, 'rb') as file:
blob_content = file.read()
# Try to identify if it's an image
try:
Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image
extension = ".png" # Default to PNG for saving
media_type = "image"
except (IOError, SyntaxError):
# If it's not a valid image, assume it's a video
extension = ".mp4" # Default to MP4 for saving
media_type = "video"
# Create a unique filename
filename = f"temp_{uuid.uuid4()}_media{extension}"
with open(filename, "wb") as f:
f.write(blob_content)
return filename, media_type
except FileNotFoundError:
raise ValueError(f"The file {blob_path} was not found.")
except Exception as e:
raise ValueError(f"An error occurred while processing the file: {e}")
@spaces.GPU
def qwen_inference(media_input, search_word):
"""
Performs OCR on the input media and highlights the search_word in the extracted text.
Args:
media_input (str): Path to the uploaded image or video file.
search_word (str): The word to search and highlight in the OCR result.
Yields:
str: The OCR result with highlighted search words.
"""
text_input = "Extract text" # Hardcoded text query
if isinstance(media_input, str): # If it's a filepath
media_path = media_input
if media_path.endswith(tuple([i for i, f in image_extensions.items()])):
media_type = "image"
elif media_path.endswith(video_extensions):
media_type = "video"
else:
try:
media_path, media_type = identify_and_save_blob(media_input)
print(media_path, media_type)
except Exception as e:
print(e)
raise ValueError(
"Unsupported media type. Please upload an image or video."
)
print(f"Processing media: {media_path} (Type: {media_type})")
messages = [
{
"role": "user",
"content": [
{
"type": media_type,
media_type: media_path,
**({"fps": 8.0} if media_type == "video" else {}),
},
{"type": "text", "text": text_input},
],
}
]
# Apply chat template to format the input for the model
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
# Prepare model inputs
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
# Initialize the streamer for iterative generation
streamer = TextIteratorStreamer(
processor, skip_prompt=True, **{"skip_special_tokens": True}
)
generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
# Start the generation in a separate thread
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
buffer = ""
for new_text in streamer:
buffer += new_text
# Highlight the search_word in the buffer
if search_word:
# Use regex for case-insensitive search and highlight
pattern = re.compile(re.escape(search_word), re.IGNORECASE)
highlighted_text = pattern.sub(lambda m: f"<mark>{m.group(0)}</mark>", buffer)
else:
highlighted_text = buffer
yield highlighted_text
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
with gr.Tab(label="Image/Video Input"):
with gr.Row():
with gr.Column():
input_media = gr.File(
label="Upload Image or Video", type="filepath"
)
search_word = gr.Textbox(
label="Search Word", placeholder="Enter word to highlight", lines=1
)
submit_btn = gr.Button(value="Submit")
with gr.Column():
# Use HTML component to display highlighted text
output_text = gr.HTML(label="Output Text")
submit_btn.click(
qwen_inference,
inputs=[input_media, search_word],
outputs=[output_text]
)
demo.launch(debug=True) |