Update app.py

app.py

@@ -1,36 +1,47 @@
-# from PIL import Image
-# import requests
 
-# from transformers import CLIPProcessor, CLIPModel
+# import os
+# import zipfile
+# from huggingface_hub import hf_hub_url, cached_download
+# import gdown
+# # Specify the Google Drive link to the archive file
+# archive_url = 'https://drive.google.com/uc?id=14QhofCbby053kWbVeWEBHCxOROQS-bjN'
+# # Specify the destination directory within the Hugging Face space
+# # Download the archive
+# output_path = 'SaviAnna/PicturesText'
+# #destination_dir = 'SaviAnna/PicturesText'  # Replace with your desired destination directory
+# os.makedirs(output_path,exist_ok=True)
+# # Construct the destination path
+# #destination_path = hf_hub_url(destination_dir)
+# # Download the archive to the destination path
+# # cached_download(archive_url, output_path, quiet=False)
+# gdown.download(archive_url, output_path, quiet=False)
+# extracted_directory = 'PicturesText/find_pic'
+# # Extract the archive
+# with zipfile.ZipFile(output_path, 'r') as zip_ref:
+#     zip_ref.extractall(extracted_directory)
 
-# model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-# processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
 
-# url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-# image = Image.open(requests.get(url, stream=True).raw)
-
-# inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
-
-# outputs = model(**inputs)
-# logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
-# probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
 import os
 import zipfile
-from huggingface_hub import hf_hub_url, cached_download
 import gdown
+
 # Specify the Google Drive link to the archive file
 archive_url = 'https://drive.google.com/uc?id=14QhofCbby053kWbVeWEBHCxOROQS-bjN'
+
 # Specify the destination directory within the Hugging Face space
+destination_dir = 'SaviAnna/PicturesText'  # Replace with your desired destination directory
+os.makedirs(destination_dir, exist_ok=True)
+
+# Specify the output file path for the downloaded archive
+output_path = os.path.join(destination_dir, 'archive.zip')
+
 # Download the archive
-output_path = 'SaviAnna/PicturesText/archive.zip'
-#destination_dir = 'SaviAnna/PicturesText'  # Replace with your desired destination directory
-os.makedirs(output_path,exist_ok=True)
-# Construct the destination path
-#destination_path = hf_hub_url(destination_dir)
-# Download the archive to the destination path
-# cached_download(archive_url, output_path, quiet=False)
 gdown.download(archive_url, output_path, quiet=False)
-extracted_directory = 'PicturesText/find_pic'
+
+# Specify the directory to extract the archive into
+extracted_directory = os.path.join(destination_dir, 'find_pic')
+
 # Extract the archive
 with zipfile.ZipFile(output_path, 'r') as zip_ref:
     zip_ref.extractall(extracted_directory)
+