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import re
def main():
app_path = "/home/mohammed/cbackup/Coding/R&D/SaasBackend/New/TextExtractor-v1/app.py"
with open(app_path, "r") as f:
original_code = f.read()
commented_backup = "\n".join("# " + line for line in original_code.splitlines())
new_code = """# Main One
import json
import os
import re
from datetime import datetime
import fitz # PyMuPDF
import gradio as gr
import spaces
import torch
from gradio.themes.base import Base
from PIL import Image
from qwen_vl_utils import process_vision_info
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
# 1. Custom Theme Definition
class CustomTheme(Base):
def __init__(self):
super().__init__()
self.primary_hue = "blue"
self.secondary_hue = "sky"
custom_theme = CustomTheme()
DESCRIPTION = "A powerful vision-language model that can understand images and text to provide detailed analysis."
# 2. Safely Downscale & Save Image to prevent CUDA OOM
def prepare_and_save_image(image_filepath, max_width=1250, max_height=1750):
if not image_filepath or not os.path.exists(image_filepath):
raise ValueError("Image file not found.")
img = Image.open(image_filepath).convert("RGB")
width, height = img.size
# Re-calculate dimensions while locking aspect ratio
if width > max_width or height > max_height:
aspect_ratio = width / height
if width > max_width:
width = max_width
height = int(width / aspect_ratio)
if height > max_height:
height = max_height
width = int(height * aspect_ratio)
img = img.resize((width, height), Image.Resampling.LANCZOS)
# We MUST save the resized image to a new path so the GPU actually reads the small version
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
temp_filename = os.path.abspath(f"temp_downscaled_{timestamp}.png")
img.save(temp_filename, "PNG")
return temp_filename, width, height
# 3. PDF Page Extractor
def convert_pdf_to_images(pdf_path):
image_paths = []
doc = fitz.open(pdf_path)
base_name = os.path.splitext(os.path.basename(pdf_path))[0]
for i, page in enumerate(doc):
# dpi=150 is the sweet spot for 7B models to read fine text without blowing out VRAM
pix = page.get_pixmap(dpi=150)
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
image_path = os.path.abspath(f"{base_name}_page_{i + 1}_{timestamp}.png")
pix.save(image_path)
image_paths.append(image_path)
doc.close()
return image_paths
# 4. Bulletproof JSON Extractor
def extract_json_from_text(raw_text):
# Target 1: Look inside markdown json fences
match = re.search(r"\\`\\`\\`(?:json)?\\s*(\\{.*?\\})\\s*\\`\\`\\`", raw_text, re.DOTALL)
if match:
try:
return json.loads(match.group(1))
except json.JSONDecodeError:
pass
# Target 2: Fallback to raw bracket math
try:
start = raw_text.find("{")
end = raw_text.rfind("}") + 1
if start != -1 and end > start:
return json.loads(raw_text[start:end])
except json.JSONDecodeError:
pass
return None
def extract_html_from_text(raw_text):
match = re.search(r"\\`\\`\\`(?:html)?\\s*(<html.*?>.*?</html>)\\s*\\`\\`\\`", raw_text, re.DOTALL | re.IGNORECASE)
if match:
return match.group(1)
if "<html" in raw_text.lower():
start = raw_text.lower().find("<html")
end = raw_text.lower().rfind("</html>") + 7
if start != -1 and end > start:
return raw_text[start:end]
return raw_text
# 5. Global Model Init (Optimized with SDPA & bfloat16)
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-7B-Instruct", torch_dtype=torch.bfloat16, attn_implementation="sdpa"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
@spaces.GPU(duration=180)
def run_inference(uploaded_files, text_input):
if not uploaded_files:
err = json.dumps({"error": "No file uploaded."}, indent=4)
return err, gr.Button(interactive=False)
results = []
files_to_delete_from_disk = []
# Standardize incoming Gradio file objects to raw string paths
raw_paths = [getattr(f, "path", getattr(f, "name", str(f))) for f in uploaded_files]
images_to_process = []
unsupported = []
# Sort files into PDFs vs standard images
for f_path in raw_paths:
ext = os.path.splitext(f_path)[1].lower()
if ext == ".pdf":
try:
generated_pngs = convert_pdf_to_images(f_path)
images_to_process.extend(generated_pngs)
files_to_delete_from_disk.extend(generated_pngs)
except Exception as e:
results.append(
json.dumps(
{"error": f"Corrupt PDF: {os.path.basename(f_path)}"},
indent=4,
)
)
elif ext in [".png", ".jpg", ".jpeg", ".bmp", ".gif", ".webp"]:
images_to_process.append(f_path)
else:
unsupported.append(os.path.basename(f_path))
if unsupported:
results.append(
json.dumps(
{"warning": f"Ignored unknown files: {', '.join(unsupported)}"},
indent=4,
)
)
system_json_injection = (
f"{text_input}\\n\\nBased on the image and the query, respond ONLY with a single, "
"valid JSON object. This object should be well-structured, using nested objects "
"and arrays to logically represent the information."
)
for original_img in images_to_process:
downscaled_img = None
try:
downscaled_img, w, h = prepare_and_save_image(original_img)
files_to_delete_from_disk.append(downscaled_img)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": downscaled_img,
"resized_height": h,
"resized_width": w,
},
{"type": "text", "text": system_json_injection},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
# Optimized generation parameters (Faster + Better JSON)
generated_ids = model.generate(
**inputs, max_new_tokens=2048, do_sample=False, use_cache=True
)
trimmed = [
out[len(in_ids) :]
for in_ids, out in zip(inputs.input_ids, generated_ids)
]
raw_output = processor.batch_decode(
trimmed,
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)[0]
# Format clean output
parsed_json = extract_json_from_text(raw_output)
clean_source_name = re.sub(
r"_\\d{8}_\\d{6}\\.png$", "", os.path.basename(original_img)
)
if parsed_json:
parsed_json["_source_document"] = clean_source_name
results.append(json.dumps(parsed_json, indent=4))
else:
results.append(
json.dumps(
{
"error": "Model failed to format valid JSON",
"source": clean_source_name,
"raw_text": raw_output[:250] + "...",
},
indent=4,
)
)
except Exception as e:
results.append(
json.dumps(
{
"error": f"Inference failed on {os.path.basename(original_img)}",
"trace": str(e),
},
indent=4,
)
)
# Rigorous disk sweep: Delete all generated temp files
for filepath in set(files_to_delete_from_disk):
if filepath and os.path.exists(filepath):
try:
os.remove(filepath)
except OSError:
pass
final_payload = "\\n\\n".join(results)
is_failed = '"error":' in final_payload
return final_payload, gr.Button(interactive=not is_failed)
@spaces.GPU(duration=180)
def run_html_replica(uploaded_files):
if not uploaded_files:
err = "<!-- Error: No file uploaded. -->"
return err, err
files_to_delete_from_disk = []
# Standardize incoming Gradio file objects to raw string paths
raw_paths = [getattr(f, "path", getattr(f, "name", str(f))) for f in uploaded_files]
images_to_process = []
# Sort files into PDFs vs standard images
for f_path in raw_paths:
ext = os.path.splitext(f_path)[1].lower()
if ext == ".pdf":
try:
generated_pngs = convert_pdf_to_images(f_path)
images_to_process.extend(generated_pngs)
files_to_delete_from_disk.extend(generated_pngs)
except Exception as e:
pass
elif ext in [".png", ".jpg", ".jpeg", ".bmp", ".gif", ".webp"]:
images_to_process.append(f_path)
if not images_to_process:
err = "<!-- Error: No valid image or PDF found. -->"
return err, err
system_html_injection = (
"You are an expert frontend web developer. Your task is to recreate the provided image exactly as a single HTML file containing inline CSS. "
"Replicate the color, font, theme, alignment, and icons perfectly (1:1 replica). "
"Output ONLY valid HTML code starting with <html>. Do not include markdown formatting like ```html."
)
final_html_parts = []
for original_img in images_to_process:
downscaled_img = None
try:
downscaled_img, w, h = prepare_and_save_image(original_img)
files_to_delete_from_disk.append(downscaled_img)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": downscaled_img,
"resized_height": h,
"resized_width": w,
},
{"type": "text", "text": system_html_injection},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
generated_ids = model.generate(
**inputs, max_new_tokens=4096, do_sample=False, use_cache=True
)
trimmed = [
out[len(in_ids) :]
for in_ids, out in zip(inputs.input_ids, generated_ids)
]
raw_output = processor.batch_decode(
trimmed,
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)[0]
parsed_html = extract_html_from_text(raw_output)
final_html_parts.append(parsed_html)
except Exception as e:
final_html_parts.append(f"<!-- Inference failed on {os.path.basename(original_img)}: {str(e)} -->")
# Rigorous disk sweep: Delete all generated temp files
for filepath in set(files_to_delete_from_disk):
if filepath and os.path.exists(filepath):
try:
os.remove(filepath)
except OSError:
pass
final_payload = "\\n<hr/>\\n".join(final_html_parts)
return final_payload, final_payload
@spaces.GPU(duration=180)
def generate_explanation(json_text):
if not json_text or '"error":' in json_text:
return "Cannot generate an explanation from an errored JSON payload."
prompt = (
"You are an expert data analyst. Your task is to provide a comprehensive, human-readable explanation "
"of the following JSON data, which may represent one or more pages from a document. First, provide a textual explanation. "
"so the json which is provided try to understand what it is representing like a receipt, table, or list of items. or just some text or just an image and after getting the context then only provide the explanation."
"If the JSON contains data from multiple sources (pages), explain each one. Then, if the JSON data represents a table, "
"a list of items, or a receipt, you **must** re-format the key information into a Markdown table for clarity.\\n\\n"
f"JSON Data:\\n```json\\n{json_text}\\n```"
)
messages = [{"role": "user", "content": prompt}]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = processor(text=[text], return_tensors="pt").to("cuda")
generated_ids = model.generate(
**inputs, max_new_tokens=1536, do_sample=False, use_cache=True
)
trimmed = [
out[len(in_ids) :] for in_ids, out in zip(inputs.input_ids, generated_ids)
]
return processor.batch_decode(trimmed, skip_special_tokens=True)[0]
# 6. Gradio UI Assembly
css = \"\"\"
.gradio-container { font-family: 'IBM Plex Sans', sans-serif; }
#output-code, #output-code pre, #output-code code {
background-color: #f0f0f0;
border: 1px solid #e0e0e0;
border-radius: 7px;
color: #333;
}
#output-code .token.punctuation { color: #393a34; }
#output-code .token.property, #output-code .token.string { color: #0b7500; }
#output-code .token.number { color: #2973b7; }
#output-code .token.boolean { color: #9a050f; }
#explanation-box {
min-height: 200px;
border: 1px solid #e0e0e0;
padding: 15px;
border-radius: 7px;
}
.dark #output-code, .dark #output-code pre, .dark #output-code code {
background-color: #2b2b2b !important;
border: 1px solid #444 !important;
color: #f0f0f0 !important;
}
.dark #explanation-box { border: 1px solid #444 !important; }
.dark #output-code code span { color: #f0f0f0 !important; }
.dark #output-code .token.punctuation { color: #ccc !important; }
.dark #output-code .token.property, .dark #output-code .token.string { color: #90ee90 !important; }
.dark #output-code .token.number { color: #add8e6 !important; }
.dark #output-code .token.boolean { color: #f08080 !important; }
\"\"\"
with gr.Blocks(theme=custom_theme, css=css) as demo:
gr.Markdown("# Sparrow Qwen2-VL-7B Vision AI 👁️")
gr.Markdown(DESCRIPTION)
with gr.Tabs():
with gr.Tab("JSON Extraction"):
with gr.Row():
with gr.Column(scale=1):
input_files = gr.Files(
label="Upload Images or PDFs",
file_types=[
".pdf",
".png",
".jpg",
".jpeg",
".bmp",
".gif",
".webp",
],
)
text_input = gr.Textbox(
label="Your Query",
placeholder="e.g., Extract all line items into JSON.",
)
submit_btn = gr.Button("Analyze File(s)", variant="primary")
with gr.Column(scale=2):
output_text = gr.Code(
label="Full JSON Response",
language="json",
elem_id="output-code",
interactive=False,
)
explanation_btn = gr.Button(
"📄 Generate Detailed Explanation", interactive=False
)
explanation_output = gr.Markdown(
label="Detailed Explanation", elem_id="explanation-box"
)
submit_btn.click(
fn=run_inference,
inputs=[input_files, text_input],
outputs=[output_text, explanation_btn],
api_name="analyze_document",
)
explanation_btn.click(
fn=generate_explanation,
inputs=[output_text],
outputs=[explanation_output],
api_name="generate_explanation",
)
with gr.Tab("HTML Replica"):
with gr.Row():
with gr.Column(scale=1):
html_input_files = gr.Files(
label="Upload Images or PDFs",
file_types=[
".pdf",
".png",
".jpg",
".jpeg",
".bmp",
".gif",
".webp",
],
)
html_submit_btn = gr.Button("Generate HTML Replica", variant="primary")
with gr.Column(scale=2):
html_rendered_output = gr.HTML(
label="Rendered HTML Replica"
)
html_raw_output = gr.Code(
label="Raw HTML Source",
language="html",
interactive=False,
)
html_submit_btn.click(
fn=run_html_replica,
inputs=[html_input_files],
outputs=[html_rendered_output, html_raw_output],
api_name="generate_html_replica",
)
if __name__ == "__main__":
demo.queue(api_open=True).launch(debug=True)
"""
with open(app_path, "w") as f:
f.write(commented_backup)
f.write("\n\n")
f.write(new_code)
if __name__ == "__main__":
main()
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