Removed torch code

cloudzy/embedding/image_embedding.py

@@ -1,53 +0,0 @@
-from transformers import AutoModel, AutoProcessor
-from PIL import Image
-import requests
-import numpy as np
-import torch
-from io import BytesIO
-
-# Load model and processor directly
-model = AutoModel.from_pretrained("jinaai/jina-clip-v2", trust_remote_code=True)
-processor = AutoProcessor.from_pretrained("jinaai/jina-clip-v2", trust_remote_code=True)
-
-texts = ["Woman taking pictures on a road trip.", "delicious fruits glowing under sunlight"]
-# Process and encode text
-text_inputs = processor(text=texts, return_tensors="pt", padding=True)
-with torch.no_grad():
-    text_embeddings = model.get_text_features(**text_inputs)
-text_embeddings = text_embeddings.cpu().numpy()
-print("Text embeddings shape:", text_embeddings.shape)
-
-image_paths = [
-    "/Users/komeilfathi/Documents/hf_deploy_test/cloudzy_ai_challenge/uploads/img_1_20251026_014959_886.jpg",
-    "/Users/komeilfathi/Documents/hf_deploy_test/cloudzy_ai_challenge/uploads/img_9_20251024_185602_319.webp"
-]
-images = []
-for path in image_paths:
-    try:
-        img = Image.open(path).convert("RGB")
-        images.append(img)
-        print(f"✓ Loaded image from {path}")
-    except Exception as e:
-        print(f"✗ Failed to load image from {path}: {e}")
-
-# Process and encode images
-if images:
-    image_inputs = processor(images=images, return_tensors="pt")
-    with torch.no_grad():
-        image_embeddings = model.get_image_features(**image_inputs)
-    image_embeddings = image_embeddings.cpu().numpy()
-    print("Image embeddings shape:", image_embeddings.shape)
-else:
-    print("⚠ No images loaded successfully")
-    image_embeddings = np.array([])
-
-def cosine_similarity(a, b):
-    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
-
-if len(image_embeddings) > 0:
-    for i, t_emb in enumerate(text_embeddings):
-        for j, i_emb in enumerate(image_embeddings):
-            sim = cosine_similarity(t_emb, i_emb)
-            print(f"Similarity between text {i} and image {j}: {sim:.4f}")
-else:
-    print("No images to compare similarity with")