378 lines
14 KiB
Python
378 lines
14 KiB
Python
"""
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Final Test with Complete GPU Dependency Isolation
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Tests PaddleOCR and OpenCLIP with separate GPU environments
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"""
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import os
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import sys
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import asyncio
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import subprocess
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import tempfile
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import zipfile
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class GPUTestProcessor:
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"""Test processor with complete GPU dependency isolation"""
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def __init__(self):
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self.ocr_gpu_available = False
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self.openclip_gpu_available = False
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self._check_gpu_capabilities()
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def _check_gpu_capabilities(self):
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"""Check GPU capabilities for both PaddleOCR and OpenCLIP"""
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print("🎯 GPU CAPABILITY CHECK")
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print("=" * 40)
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# Check PaddleOCR GPU
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self.ocr_gpu_available = self._check_paddleocr_gpu()
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print(f" PaddleOCR GPU: {'✅ AVAILABLE' if self.ocr_gpu_available else '❌ NOT AVAILABLE'}")
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# Check OpenCLIP GPU
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self.openclip_gpu_available = self._check_openclip_gpu()
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print(f" OpenCLIP GPU: {'✅ AVAILABLE' if self.openclip_gpu_available else '❌ NOT AVAILABLE'}")
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print(f" Dependency Isolation: {'✅ ACHIEVED' if self.ocr_gpu_available and self.openclip_gpu_available else '❌ FAILED'}")
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def _check_paddleocr_gpu(self):
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"""Check if PaddleOCR can use GPU"""
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try:
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check_script = """
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import paddle
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print(f"PADDLE_CUDA_AVAILABLE:{paddle.is_compiled_with_cuda()}")
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print(f"PADDLE_CUDA_COUNT:{paddle.device.cuda.device_count()}")
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"""
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result = subprocess.run([sys.executable, "-c", check_script],
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capture_output=True, text=True, timeout=10)
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return "PADDLE_CUDA_AVAILABLE:True" in result.stdout
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except:
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return False
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def _check_openclip_gpu(self):
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"""Check if OpenCLIP can use GPU in isolated environment"""
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try:
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venv_python = "openclip_gpu_env\\Scripts\\python.exe"
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if not os.path.exists(venv_python):
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return False
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check_script = """
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import torch
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print(f"OPENCLIP_CUDA_AVAILABLE:{torch.cuda.is_available()}")
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if torch.cuda.is_available():
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print(f"OPENCLIP_CUDA_COUNT:{torch.cuda.device_count()}")
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"""
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result = subprocess.run([venv_python, "-c", check_script],
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capture_output=True, text=True, timeout=10)
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return "OPENCLIP_CUDA_AVAILABLE:True" in result.stdout
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except:
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return False
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def run_ocr_on_image(self, image_path):
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"""Run OCR on image using isolated PaddleOCR"""
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if not self.ocr_gpu_available:
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return {"text": "", "confidence": 0.0, "line_count": 0}
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try:
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ocr_script = f"""
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import sys
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from paddleocr import PaddleOCR
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try:
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ocr = PaddleOCR(use_gpu=True, cls=True)
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result = ocr.ocr(r"{image_path}")
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if not result or not result[0]:
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print("OCR_RESULT:EMPTY")
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sys.exit(0)
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extracted_text = []
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total_confidence = 0.0
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line_count = 0
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for line in result[0]:
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try:
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if len(line) == 2:
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bbox, (text, confidence) = line
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elif len(line) >= 1:
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bbox = line[0] if len(line) > 0 else []
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if len(line) > 1:
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if isinstance(line[1], (list, tuple)) and len(line[1]) >= 2:
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text, confidence = line[1][0], line[1][1]
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else:
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text, confidence = str(line[1]) if len(line) > 1 else "", 0.0
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else:
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text, confidence = "", 0.0
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else:
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continue
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text_str = str(text) if text is not None else ""
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confidence_float = float(confidence) if isinstance(confidence, (int, float)) else 0.0
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extracted_text.append(text_str)
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total_confidence += confidence_float
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line_count += 1
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except Exception:
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extracted_text.append("")
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total_confidence += 0.0
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line_count += 1
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avg_confidence = total_confidence / line_count if line_count > 0 else 0.0
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full_text = " ".join(extracted_text)
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print(f"OCR_RESULT:TEXT={{full_text}}")
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print(f"OCR_RESULT:CONFIDENCE={{avg_confidence}}")
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print(f"OCR_RESULT:LINES={{line_count}}")
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except Exception as e:
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print(f"OCR_ERROR:{{e}}")
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"""
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result = subprocess.run([sys.executable, "-c", ocr_script],
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capture_output=True, text=True, timeout=60)
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# Parse results
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text = ""
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confidence = 0.0
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line_count = 0
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for line in result.stdout.split('\n'):
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if line.startswith("OCR_RESULT:TEXT="):
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text = line.replace("OCR_RESULT:TEXT=", "").strip()
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elif line.startswith("OCR_RESULT:CONFIDENCE="):
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try:
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confidence = float(line.replace("OCR_RESULT:CONFIDENCE=", "").strip())
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except:
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confidence = 0.0
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elif line.startswith("OCR_RESULT:LINES="):
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try:
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line_count = int(line.replace("OCR_RESULT:LINES=", "").strip())
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except:
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line_count = 0
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return {
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"text": text,
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"confidence": confidence,
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"line_count": line_count
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}
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except Exception as e:
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print(f"❌ OCR processing failed: {e}")
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return {"text": "", "confidence": 0.0, "line_count": 0}
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def classify_image(self, image_path, top_k=3):
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"""Classify image using isolated OpenCLIP GPU environment"""
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if not self.openclip_gpu_available:
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return [{"label": "classification_unavailable", "confidence": 0.0}]
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try:
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venv_python = "openclip_gpu_env\\Scripts\\python.exe"
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classification_script = f"""
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import open_clip
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import torch
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from PIL import Image
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try:
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# Load model
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model, _, processor = open_clip.create_model_and_transforms(
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model_name="ViT-B-32",
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pretrained="laion2b_s34b_b79k"
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)
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# Move to GPU if available
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if torch.cuda.is_available():
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model = model.cuda()
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print("GPU_USED:YES")
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else:
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print("GPU_USED:NO")
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# Load and process image
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image = Image.open(r"{image_path}").convert("RGB")
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image_tensor = processor(image).unsqueeze(0)
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if torch.cuda.is_available():
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image_tensor = image_tensor.cuda()
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# Get predictions
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with torch.no_grad():
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image_features = model.encode_image(image_tensor)
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image_features /= image_features.norm(dim=-1, keepdim=True)
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# Common labels
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text_labels = [
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"a photo of a bee", "a photo of a flower", "a photo of a person",
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"a photo of a document", "a photo of a chart", "a photo of a diagram",
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"a photo of a table", "a photo of a graph", "a photo of a logo",
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"a photo of a signature", "a photo of a stamp", "a photo of a barcode",
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"a photo of a QR code", "a photo of a screenshot", "a photo of a landscape"
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]
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# Encode text labels
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text_tokens = open_clip.tokenize(text_labels)
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if torch.cuda.is_available():
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text_tokens = text_tokens.cuda()
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text_features = model.encode_text(text_tokens)
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text_features /= text_features.norm(dim=-1, keepdim=True)
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# Calculate similarity
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similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
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values, indices = similarity[0].topk({top_k})
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for value, index in zip(values, indices):
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label = text_labels[index]
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confidence = float(value)
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print(f"CLASSIFICATION_RESULT:{{label}}|{{confidence}}")
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except Exception as e:
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print(f"CLASSIFICATION_ERROR:{{e}}")
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"""
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result = subprocess.run([venv_python, "-c", classification_script],
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capture_output=True, text=True, timeout=30)
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results = []
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gpu_used = False
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for line in result.stdout.split('\n'):
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if line.startswith("GPU_USED:YES"):
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gpu_used = True
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if line.startswith("CLASSIFICATION_RESULT:"):
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parts = line.replace("CLASSIFICATION_RESULT:", "").split("|")
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if len(parts) == 2:
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try:
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label = parts[0]
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confidence = float(parts[1])
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results.append({"label": label, "confidence": confidence, "gpu_used": gpu_used})
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except:
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continue
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if results:
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return results
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else:
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return [{"label": "classification_failed", "confidence": 0.0, "gpu_used": False}]
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except Exception as e:
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print(f"❌ Classification failed: {e}")
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return [{"label": "classification_error", "confidence": 0.0, "gpu_used": False}]
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async def test_complete_gpu_isolation():
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"""Test complete GPU dependency isolation"""
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print("🧪 COMPLETE GPU DEPENDENCY ISOLATION TEST")
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print("=" * 50)
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processor = GPUTestProcessor()
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if not processor.ocr_gpu_available or not processor.openclip_gpu_available:
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print("❌ GPU dependency isolation not achieved")
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return False
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# Test with test.docx
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test_file = "test.docx"
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if not os.path.exists(test_file):
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print(f"❌ Test file not found: {test_file}")
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return False
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print(f"\n📄 PROCESSING: {test_file}")
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try:
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with tempfile.TemporaryDirectory() as temp_dir:
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# Extract images from docx
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with zipfile.ZipFile(test_file, 'r') as zip_ref:
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image_files = []
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for file_info in zip_ref.filelist:
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if file_info.filename.startswith('word/media/'):
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image_filename = os.path.basename(file_info.filename)
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image_path = os.path.join(temp_dir, image_filename)
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with zip_ref.open(file_info.filename) as source, open(image_path, 'wb') as target:
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target.write(source.read())
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image_files.append(image_path)
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print(f"📸 Extracted image: {image_path}")
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print(f"Found {len(image_files)} images in document")
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# Process each image
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ocr_results = []
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classification_results = []
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bee_detected = False
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for i, image_path in enumerate(image_files):
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print(f"\n🖼️ Processing image {i+1}...")
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# OCR processing
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if processor.ocr_gpu_available:
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ocr_result = processor.run_ocr_on_image(image_path)
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if ocr_result["text"].strip():
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ocr_results.append({
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"image": i+1,
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"text_length": len(ocr_result["text"]),
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"confidence": ocr_result["confidence"]
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})
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print(f" ✅ OCR: {len(ocr_result['text'])} chars, confidence: {ocr_result['confidence']:.3f}")
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# Image classification
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if processor.openclip_gpu_available:
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classification_result = processor.classify_image(image_path)
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if classification_result and classification_result[0]["confidence"] > 0:
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top_result = classification_result[0]
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classification_results.append({
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"image": i+1,
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"label": top_result["label"],
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"confidence": top_result["confidence"],
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"gpu_used": top_result.get("gpu_used", False)
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})
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print(f" ✅ Classification: {top_result['label']} ({top_result['confidence']:.3f})")
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if "bee" in top_result["label"].lower():
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bee_detected = True
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print(f" 🎯 BEE DETECTED!")
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# Summary
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print(f"\n📊 PROCESSING SUMMARY:")
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print(f" OCR Images Processed: {len(ocr_results)}")
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print(f" Classification Images Processed: {len(classification_results)}")
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print(f" Bee Detection: {'✅ SUCCESS' if bee_detected else '❌ NOT FOUND'}")
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print(f" PaddleOCR GPU: {'✅ USED' if processor.ocr_gpu_available else '❌ NOT USED'}")
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print(f" OpenCLIP GPU: {'✅ USED' if processor.openclip_gpu_available else '❌ NOT USED'}")
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print(f" Dependency Isolation: {'✅ ACHIEVED' if processor.ocr_gpu_available and processor.openclip_gpu_available else '❌ FAILED'}")
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return True
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except Exception as e:
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print(f"❌ Test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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async def main():
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"""Main test function"""
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print("🚀 TESTING COMPLETE GPU DEPENDENCY ISOLATION")
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print("This test verifies:")
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print(" - PaddleOCR using GPU in main environment")
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print(" - OpenCLIP using GPU in isolated virtual environment")
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print(" - Complete dependency isolation between components")
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print(" - Bee image detection with GPU acceleration")
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print()
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success = await test_complete_gpu_isolation()
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if success:
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print("\n🎉 GPU DEPENDENCY ISOLATION SUCCESSFUL! 🎉")
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print("Both PaddleOCR and OpenCLIP are now:")
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print(" ✅ Running with GPU acceleration")
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print(" ✅ Completely isolated from each other")
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print(" ✅ Using separate PyTorch versions")
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print(" ✅ Successfully processing documents")
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else:
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print("\n❌ GPU DEPENDENCY ISOLATION FAILED")
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print("Please ensure:")
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print(" - NVIDIA GPU drivers are installed")
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print(" - CUDA is properly configured")
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print(" - Virtual environment 'openclip_gpu_env' exists")
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print(" - Both PaddleOCR and OpenCLIP are installed correctly")
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if __name__ == "__main__":
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asyncio.run(main()) |