Suvadeep Das commited on
Commit
7aec7ed
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verified Β·
1 Parent(s): e6bb622

Update app.py

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Files changed (1) hide show
  1. app.py +60 -74
app.py CHANGED
@@ -21,9 +21,8 @@ if HF_TOKEN:
21
  _model = None
22
  _tokenizer = None
23
 
24
- @spaces.GPU
25
  def load_model():
26
- """Load MiniCPM model on GPU when needed"""
27
  global _model, _tokenizer
28
 
29
  if _model is not None and _tokenizer is not None:
@@ -39,7 +38,7 @@ def load_model():
39
  "openbmb/MiniCPM-V-2_6",
40
  trust_remote_code=True,
41
  torch_dtype=torch.float16,
42
- device_map="auto"
43
  )
44
  return _model, _tokenizer
45
  except Exception as e:
@@ -58,7 +57,7 @@ def load_model():
58
  return _model, _tokenizer
59
 
60
  def pdf_to_images(pdf_file):
61
- """Convert PDF file to list of PIL images"""
62
  try:
63
  if hasattr(pdf_file, 'read'):
64
  pdf_bytes = pdf_file.read()
@@ -151,12 +150,9 @@ INSTRUCTIONS:
151
  6. If information is not visible, leave field empty but still include it
152
  7. Return ONLY the JSON, no other text"""
153
 
154
- @spaces.GPU
155
- def extract_data_from_image(image, extraction_prompt):
156
- """Extract data from a single image using MiniCPM on GPU"""
157
  try:
158
- model, tokenizer = load_model()
159
-
160
  # Convert PIL image to proper format if needed
161
  if hasattr(image, 'convert'):
162
  image = image.convert('RGB')
@@ -169,7 +165,7 @@ def extract_data_from_image(image, extraction_prompt):
169
  "content": extraction_prompt
170
  }],
171
  tokenizer=tokenizer,
172
- sampling=False, # Use deterministic output
173
  temperature=0.1,
174
  max_new_tokens=2048
175
  )
@@ -274,9 +270,9 @@ def combine_page_data(pages_data):
274
  }
275
  }
276
 
277
- @spaces.GPU(duration=180) # 3 minutes for processing
278
  def extract_efax_from_pdf(pdf_file, custom_prompt=None):
279
- """Main function to process multi-page PDF eFax on GPU"""
280
  try:
281
  if pdf_file is None:
282
  return {
@@ -286,7 +282,8 @@ def extract_efax_from_pdf(pdf_file, custom_prompt=None):
286
  "pages_data": []
287
  }
288
 
289
- # Convert PDF to images
 
290
  images = pdf_to_images(pdf_file)
291
 
292
  if not images:
@@ -297,30 +294,38 @@ def extract_efax_from_pdf(pdf_file, custom_prompt=None):
297
  "pages_data": []
298
  }
299
 
300
- # Use custom prompt or default medical extraction prompt
 
 
 
 
 
301
  extraction_prompt = custom_prompt if custom_prompt else get_medical_extraction_prompt()
302
 
303
- # Process each page
304
  pages_data = []
305
  for i, image in enumerate(images):
306
- print(f"Processing page {i+1}/{len(images)}")
307
- page_result = extract_data_from_image(image, extraction_prompt)
308
  pages_data.append({
309
  "page_number": i + 1,
310
  "page_data": page_result
311
  })
312
 
313
- # Combine data from all pages
 
 
314
  combined_result = combine_page_data(pages_data)
315
 
316
- # Final result structure
317
  result = {
318
  "status": "success",
319
  "total_pages": len(images),
320
  "pages_data": pages_data,
321
  "combined_extraction": combined_result,
322
  "model_used": "MiniCPM-V-2_6-ZeroGPU",
323
- "hardware": "ZeroGPU"
 
324
  }
325
 
326
  return result
@@ -335,16 +340,16 @@ def extract_efax_from_pdf(pdf_file, custom_prompt=None):
335
 
336
  # Create Gradio Interface
337
  def create_gradio_interface():
338
- with gr.Blocks(title="eFax PDF Data Extractor - ZeroGPU", theme=gr.themes.Soft()) as demo:
339
  gr.Markdown("# πŸ₯ eFax Medical Data Extraction API")
340
- gr.Markdown("πŸš€ **GPU-Accelerated** processing using MiniCPM-V-2_6 on ZeroGPU")
341
 
342
  with gr.Tab("πŸ“„ PDF Upload & Extraction"):
343
  with gr.Row():
344
  with gr.Column():
345
  pdf_input = gr.File(
346
  file_types=[".pdf"],
347
- label="Upload eFax PDF",
348
  file_count="single"
349
  )
350
 
@@ -356,7 +361,14 @@ def create_gradio_interface():
356
  placeholder="Leave empty to use optimized medical data extraction prompt..."
357
  )
358
 
359
- extract_btn = gr.Button("πŸš€ Extract Medical Data (GPU)", variant="primary", size="lg")
 
 
 
 
 
 
 
360
 
361
  with gr.Column():
362
  status_output = gr.Textbox(label="πŸ“Š Processing Status", interactive=False)
@@ -364,17 +376,14 @@ def create_gradio_interface():
364
 
365
  with gr.Tab("πŸ”Œ API Usage"):
366
  gr.Markdown("""
367
- ## API Endpoints (ZeroGPU Powered)
368
-
369
- Your Space runs on **ZeroGPU** for 10-50x faster processing!
370
 
371
- ### Python API Usage
372
  ```
373
  import requests
374
  import base64
375
 
376
- # Convert PDF to base64
377
- with open("medical_fax.pdf", "rb") as f:
378
  pdf_b64 = base64.b64encode(f.read()).decode()
379
 
380
  response = requests.post(
@@ -382,62 +391,39 @@ def create_gradio_interface():
382
  json={
383
  "data": [
384
  {"name": "medical_fax.pdf", "data": f"application/pdf;base64,{pdf_b64}"},
385
- "" # Leave empty for default prompt
386
  ]
387
  }
388
  )
389
 
 
390
  result = response.json()
391
-
392
- # Access combined medical data
393
  medical_data = result["data"]["combined_extraction"]
394
- print("Patient:", medical_data["data"]["patient_first_name"], medical_data["data"]["patient_last_name"])
395
- print("Insurance:", medical_data["data"]["primary_insurance"]["payer_name"])
396
- ```
397
-
398
- ### Response Format
399
- ```
400
- {
401
- "status": "success",
402
- "total_pages": 13,
403
- "combined_extraction": {
404
- "data": {
405
- "patient_first_name": "John",
406
- "patient_last_name": "Doe",
407
- "primary_insurance": {
408
- "payer_name": "UNITED HEALTHCARE",
409
- "member_id": "123456789"
410
- }
411
- },
412
- "confidence_scores": {...},
413
- "metadata": {...}
414
- }
415
- }
416
  ```
417
  """)
418
 
419
  with gr.Tab("⚑ Performance Info"):
420
  gr.Markdown("""
421
- ## ZeroGPU Performance
422
 
423
- - **πŸ”₯ Hardware**: ZeroGPU (70GB VRAM)
424
- - **⚑ Speed**: 10-50x faster than CPU processing
425
- - **⏱️ Processing Time**: 2-5 minutes for 6-13 page eFax
426
- - **πŸ€– Model**: MiniCPM-V-2_6 optimized for medical documents
427
- - **πŸ’‘ Dynamic Allocation**: GPU activates only during processing
428
 
429
- ## Medical Data Extracted
430
- - βœ… Patient Demographics (Name, DOB, Gender, Address)
431
- - βœ… Contact Information (Phone, Email)
432
- - βœ… Insurance Information (Primary, Secondary, Tertiary)
433
- - βœ… Medical Codes & Diagnoses
434
- - βœ… Referral Source & Priority
435
- - βœ… Confidence Scores for Quality Control
436
 
437
- ## HIPAA Compliance
438
- - πŸ”’ All processing in-memory (no persistent storage)
439
- - πŸ›‘οΈ Secure GPU processing environment
440
- - πŸ“‹ Audit trail with confidence scores
 
 
 
441
  """)
442
 
443
  def process_with_status(pdf_file, custom_prompt):
@@ -450,7 +436,7 @@ def create_gradio_interface():
450
  result = extract_efax_from_pdf(pdf_file, custom_prompt if custom_prompt.strip() else None)
451
 
452
  if result["status"] == "success":
453
- yield f"βœ… Successfully processed {result['total_pages']} pages", result
454
  else:
455
  yield f"❌ Error: {result.get('error', 'Unknown error')}", result
456
 
@@ -477,4 +463,4 @@ if __name__ == "__main__":
477
  server_name="0.0.0.0",
478
  server_port=7860,
479
  show_error=True
480
- )
 
21
  _model = None
22
  _tokenizer = None
23
 
 
24
  def load_model():
25
+ """Load MiniCPM model (CPU loading, GPU usage happens in main function)"""
26
  global _model, _tokenizer
27
 
28
  if _model is not None and _tokenizer is not None:
 
38
  "openbmb/MiniCPM-V-2_6",
39
  trust_remote_code=True,
40
  torch_dtype=torch.float16,
41
+ device_map="auto" # Will move to GPU when @spaces.GPU is active
42
  )
43
  return _model, _tokenizer
44
  except Exception as e:
 
57
  return _model, _tokenizer
58
 
59
  def pdf_to_images(pdf_file):
60
+ """Convert PDF file to list of PIL images (CPU operation)"""
61
  try:
62
  if hasattr(pdf_file, 'read'):
63
  pdf_bytes = pdf_file.read()
 
150
  6. If information is not visible, leave field empty but still include it
151
  7. Return ONLY the JSON, no other text"""
152
 
153
+ def extract_data_from_image(image, extraction_prompt, model, tokenizer):
154
+ """Extract data from a single image using MiniCPM (runs within GPU session)"""
 
155
  try:
 
 
156
  # Convert PIL image to proper format if needed
157
  if hasattr(image, 'convert'):
158
  image = image.convert('RGB')
 
165
  "content": extraction_prompt
166
  }],
167
  tokenizer=tokenizer,
168
+ sampling=False,
169
  temperature=0.1,
170
  max_new_tokens=2048
171
  )
 
270
  }
271
  }
272
 
273
+ @spaces.GPU(duration=600) # 10 minutes for large documents
274
  def extract_efax_from_pdf(pdf_file, custom_prompt=None):
275
+ """Main function to process multi-page PDF eFax - ALL GPU processing happens here"""
276
  try:
277
  if pdf_file is None:
278
  return {
 
282
  "pages_data": []
283
  }
284
 
285
+ # Step 1: Convert PDF to images (CPU operation - do this before GPU)
286
+ print("Converting PDF to images...")
287
  images = pdf_to_images(pdf_file)
288
 
289
  if not images:
 
294
  "pages_data": []
295
  }
296
 
297
+ print(f"Converted {len(images)} pages. Starting GPU processing...")
298
+
299
+ # Step 2: Load model on GPU (happens once GPU session starts)
300
+ model, tokenizer = load_model()
301
+
302
+ # Step 3: Use custom prompt or default
303
  extraction_prompt = custom_prompt if custom_prompt else get_medical_extraction_prompt()
304
 
305
+ # Step 4: Process all pages within single GPU session
306
  pages_data = []
307
  for i, image in enumerate(images):
308
+ print(f"Processing page {i+1}/{len(images)} on GPU...")
309
+ page_result = extract_data_from_image(image, extraction_prompt, model, tokenizer)
310
  pages_data.append({
311
  "page_number": i + 1,
312
  "page_data": page_result
313
  })
314
 
315
+ print("GPU processing complete. Combining results...")
316
+
317
+ # Step 5: Combine data from all pages
318
  combined_result = combine_page_data(pages_data)
319
 
320
+ # Final result
321
  result = {
322
  "status": "success",
323
  "total_pages": len(images),
324
  "pages_data": pages_data,
325
  "combined_extraction": combined_result,
326
  "model_used": "MiniCPM-V-2_6-ZeroGPU",
327
+ "hardware": "ZeroGPU",
328
+ "processing_time": "Within 10-minute GPU session"
329
  }
330
 
331
  return result
 
340
 
341
  # Create Gradio Interface
342
  def create_gradio_interface():
343
+ with gr.Blocks(title="eFax PDF Data Extractor - Optimized ZeroGPU", theme=gr.themes.Soft()) as demo:
344
  gr.Markdown("# πŸ₯ eFax Medical Data Extraction API")
345
+ gr.Markdown("πŸš€ **Optimized GPU Usage** - Single 10-minute GPU session for entire document")
346
 
347
  with gr.Tab("πŸ“„ PDF Upload & Extraction"):
348
  with gr.Row():
349
  with gr.Column():
350
  pdf_input = gr.File(
351
  file_types=[".pdf"],
352
+ label="Upload eFax PDF (up to 20 pages)",
353
  file_count="single"
354
  )
355
 
 
361
  placeholder="Leave empty to use optimized medical data extraction prompt..."
362
  )
363
 
364
+ extract_btn = gr.Button("πŸš€ Extract Medical Data (10min GPU)", variant="primary", size="lg")
365
+
366
+ gr.Markdown("""
367
+ ### ⚑ Optimized Processing
368
+ - **Single GPU Session**: All pages processed in one 10-minute session
369
+ - **No Timeouts**: Handles up to 20+ page documents
370
+ - **Efficient**: PDF→Images (CPU) → All Processing (GPU) → Results
371
+ """)
372
 
373
  with gr.Column():
374
  status_output = gr.Textbox(label="πŸ“Š Processing Status", interactive=False)
 
376
 
377
  with gr.Tab("πŸ”Œ API Usage"):
378
  gr.Markdown("""
379
+ ## Optimized API (No Timeout Issues)
 
 
380
 
381
+ ### Python Usage
382
  ```
383
  import requests
384
  import base64
385
 
386
+ with open("large_medical_fax.pdf", "rb") as f:
 
387
  pdf_b64 = base64.b64encode(f.read()).decode()
388
 
389
  response = requests.post(
 
391
  json={
392
  "data": [
393
  {"name": "medical_fax.pdf", "data": f"application/pdf;base64,{pdf_b64}"},
394
+ "" # Empty for default prompt
395
  ]
396
  }
397
  )
398
 
399
+ # Now handles 13+ pages without timeout!
400
  result = response.json()
 
 
401
  medical_data = result["data"]["combined_extraction"]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
402
  ```
403
  """)
404
 
405
  with gr.Tab("⚑ Performance Info"):
406
  gr.Markdown("""
407
+ ## Optimized ZeroGPU Performance
408
 
409
+ ### Before Optimization (❌ Had Timeout Issues)
410
+ - GPU session per page = 13 Γ— 30 seconds = 6.5 minutes
411
+ - Model loading repeated = wasted time
412
+ - Timeout around page 11/13
 
413
 
414
+ ### After Optimization (βœ… No Timeouts)
415
+ - **Single 10-minute GPU session** for entire document
416
+ - Model loads once, processes all pages
417
+ - Handles 15-20+ page documents easily
418
+ - PDF conversion on CPU (doesn't count toward GPU time)
 
 
419
 
420
+ ### Processing Flow
421
+ 1. **PDF β†’ Images** (CPU, before GPU starts)
422
+ 2. **πŸš€ GPU Session Starts** (10 minutes allocated)
423
+ 3. **Load Model** (once, on GPU)
424
+ 4. **Process All Pages** (GPU, sequential)
425
+ 5. **GPU Session Ends**
426
+ 6. **Combine Results** (CPU, after GPU)
427
  """)
428
 
429
  def process_with_status(pdf_file, custom_prompt):
 
436
  result = extract_efax_from_pdf(pdf_file, custom_prompt if custom_prompt.strip() else None)
437
 
438
  if result["status"] == "success":
439
+ yield f"βœ… Successfully processed {result['total_pages']} pages in single GPU session", result
440
  else:
441
  yield f"❌ Error: {result.get('error', 'Unknown error')}", result
442
 
 
463
  server_name="0.0.0.0",
464
  server_port=7860,
465
  show_error=True
466
+ )