Update app.py

app.py

@@ -32,6 +32,22 @@ zip_path = "sample_evaluation.zip"
 extract_path = "sample_evaluation"
 unzip_file(zip_path, extract_path)
 
+import requests
+
+def download_file(url, output_file):
+    response = requests.get(url, stream=True)
+    response.raise_for_status()  # Raises an HTTPError if the status is 4xx, 5xx
+    
+    with open(output_file, 'wb') as file:
+        for chunk in response.iter_content(chunk_size=8192):
+            if chunk:
+                file.write(chunk)
+
+# Example usage
+url = "https://huggingface.co/safinal/compositional-retrieval/resolve/main/weights.pth"
+output_file = "weights.pth"
+download_file(url, output_file)
+
 
 
 def encode_database(model, df: pd.DataFrame) -> np.ndarray :
@@ -59,8 +75,8 @@ def encode_database(model, df: pd.DataFrame) -> np.ndarray :
 
 # Load model and configurations
 def load_model():
-    model = Model(model_name="ViTamin-L-384", pretrained="datacomp1b")
-    # model.load("weights.pth")
+    model = Model(model_name="ViTamin-L-384", pretrained=None)
+    model.load("weights.pth")
     model.eval()
     return model
 
@@ -161,6 +177,8 @@ demo = gr.Interface(
     title="Compositional Image Retrieval",
     description="Select an image and enter a text query to find the most similar image.",
     examples=[
+        ["sample_evaluation/images/261684.png", "Bring cow into the picture, and then follow up with removing bench."],
+        ["sample_evaluation/images/283700.png", "add bowl and bench and remove shoe and elephant"],
         ["sample_evaluation/images/455007.png", "Discard chair in the beginning, then proceed to bring car into play."],
         ["sample_evaluation/images/612311.png", "Get rid of train initially, and then follow up by including snowboard."]
     ]