Update app.py

app.py

@@ -19,12 +19,8 @@ zip_filename = 'Images.zip'
 import os
 import zipfile
 
-
-
-    
 with gr.Blocks(css="style.css") as demo:
-    def get_image_embeddings(valid_df, model_path):
-        # Define the filename
+# Define the filename
         zip_filename = 'Images.zip'
         
         # Check if the file exists
@@ -36,61 +32,9 @@ with gr.Blocks(css="style.css") as demo:
             print(f"'{zip_filename}' has been successfully unzipped.")
         else:
             print(f"'{zip_filename}' not found in the current directory.")
-        tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)
-        valid_loader = build_loaders(valid_df, tokenizer, mode="valid")
-        
-        model = CLIPModel().to(CFG.device)
-        model.load_state_dict(torch.load(model_path, map_location=CFG.device))
-        model.eval()
-        
-        valid_image_embeddings = []
-        with torch.no_grad():
-            for batch in tqdm(valid_loader):
-                image_features = model.image_encoder(batch["image"].to(CFG.device))
-                image_embeddings = model.image_projection(image_features)
-                valid_image_embeddings.append(image_embeddings)
-        return model, torch.cat(valid_image_embeddings)
-
-    _, valid_df = make_train_valid_dfs()
-    model, image_embeddings = get_image_embeddings(valid_df, "best.pt")
-
-    def find_matches(query, n=9):
-        tokenizer = DistilBertTokenizer.from_pretrained(CFG.text_tokenizer)
-        encoded_query = tokenizer([query])
-        batch = {
-            key: torch.tensor(values).to(CFG.device)
-            for key, values in encoded_query.items()
-        }
-        with torch.no_grad():
-            text_features = model.text_encoder(
-                input_ids=batch["input_ids"], attention_mask=batch["attention_mask"]
-            )
-            text_embeddings = model.text_projection(text_features)
-        
-        image_embeddings_n = F.normalize(image_embeddings, p=2, dim=-1)
-        text_embeddings_n = F.normalize(text_embeddings, p=2, dim=-1)
-        dot_similarity = text_embeddings_n @ image_embeddings_n.T
-        
-        _, indices = torch.topk(dot_similarity.squeeze(0), n * 5)
-        matches = [valid_df['image'].values[idx] for idx in indices[::5]]
-        
-        images = []
-        for match in matches:
-            image = cv2.imread(f"{CFG.image_path}/{match}")
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            # images.append(image)
-        
-        return image
-    with gr.Row():
-        textbox = gr.Textbox(label = "Enter a query to find matching images using a CLIP model.")
-        image = gr.Image(type="numpy")
     
-    button = gr.Button("Press")
-    button.click(
-        fn = find_matches,
-        inputs=textbox,
-        outputs=image
-    )
+
+   
     
     # Create Gradio interface
 demo.launch(share=True)