Delete CLIP.py

CLIP.py

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-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
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-get_ipython().system('pip install ftfy regex tqdm')
-get_ipython().system('pip install git+https://github.com/openai/CLIP.git')
-get_ipython().system('pip install sentencepiece-0.1.98-cp311-cp311-win_amd64.whl')
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-# In[5]:
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-# prompt: install transformers
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-get_ipython().system('pip install transformers')
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-# In[6]:
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-from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
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-feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
-
-# ## Import the necessary libraries and load the CLIP model:
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-# In[7]:
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-from PIL import Image
-import clip
-import torch
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-device = "cuda" if torch.cuda.is_available() else "cpu"
-clip_model, preprocess = clip.load("ViT-B/32", device=device)
-
-
-# ## Define a function to generate product descriptions:
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-# In[8]:
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-image = Image.open("data/download.jpeg")
-pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
-output_ids = model.generate(pixel_values, max_length=50, num_beams=4, early_stopping=True)
-captions = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
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-# In[9]:
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-image = preprocess(image).unsqueeze(0).to(device)
-with torch.no_grad():
-    image_features = clip_model.encode_image(image)
-
-text_inputs = torch.cat([clip.tokenize(caption).to(device) for caption in captions]).to(device)
-with torch.no_grad():
-    text_features = clip_model.encode_text(text_inputs)
-
-similarity_scores = image_features @ text_features.T
-best_caption_idx = similarity_scores.argmax().item()
-product_description = captions[best_caption_idx]
-print(product_description)
-
-
-# # Using SigLip
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-# In[11]:
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-get_ipython().system('pip install sentencepiece')
-get_ipython().system('pip install protobuf')
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-# In[12]:
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-from transformers import AutoProcessor, AutoModel, VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
-import torch
-from PIL import Image
-
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-model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
-processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")
-
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-image = Image.open("data/avito4.jpeg")
-inputs = processor(images=image, return_tensors="pt")
-
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-feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
-pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
-output_ids = model.generate(pixel_values, max_length=100, num_beams=5, early_stopping=True)
-captions = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-
-image = preprocess(image).unsqueeze(0).to(device)
-with torch.no_grad():
-    image_features = clip_model.encode_image(image)
-
-text_inputs = torch.cat([clip.tokenize(caption).to(device) for caption in captions]).to(device)
-with torch.no_grad():
-    text_features = clip_model.encode_text(text_inputs)
-
-similarity_scores = image_features @ text_features.T
-best_caption_idx = similarity_scores.argmax().item()
-product_description = captions[best_caption_idx]
-print(product_description)
-
-# a vase sitting on a shelf in a store => thuya
-# a wooden bench sitting on top of a wooden floor => avito
-## two old fashioned vases sitting next to each other => avito2
-## three wooden vases sitting on top of a wooden floor => avito3
-# an old fashioned clock sitting on top of a table => avito4
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-# In[ ]:
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-# # Implemeting LLaVa
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-# https://colab.research.google.com/drive/1veefV17NcD1S4ou4nF8ABkfm8-TgU0Dr#scrollTo=XN2vJCPZk1UY
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-# In[ ]:
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