ok

app.py

@@ -82,20 +82,94 @@
 
 
 
-import os
+import uuid
+import requests
+from PIL import Image
+import numpy as np
 import gradio as gr
 from encoder import FashionCLIPEncoder
-from process import batch_process_images
+
+# Constants
+REQUESTS_HEADERS = {
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
+}
+BATCH_SIZE = 30  # Define batch size for processing
 
 # Initialize encoder
 encoder = FashionCLIPEncoder()
 
-# Constants
-BATCH_SIZE = 30  # Define batch size for processing
+# Helper function to download images
+def download_image_as_pil(url: str, timeout: int = 10) -> Image.Image:
+    try:
+        response = requests.get(url, stream=True, headers=REQUESTS_HEADERS, timeout=timeout)
+        if response.status_code == 200:
+            return Image.open(response.raw).convert("RGB")  # Ensure consistent format
+        return None
+    except Exception as e:
+        print(f"Error downloading image: {e}")
+        return None
+
+# Embedding function for a batch of images
+def batch_process_images(image_urls: str):
+    # Split the input string by commas and strip whitespace
+    urls = [url.strip() for url in image_urls.split(",") if url.strip()]
+    
+    if not urls:
+        return {"error": "No valid image URLs provided."}
+
+    results = []
+    batch_urls, batch_images = [], []
+
+    for url in urls:
+        try:
+            # Download image
+            image = download_image_as_pil(url)
+            if not image:
+                results.append({"image_url": url, "error": "Failed to download image"})
+                continue
+
+            batch_urls.append(url)
+            batch_images.append(image)
+
+            # Process batch when reaching batch size
+            if len(batch_images) == BATCH_SIZE:
+                process_batch(batch_urls, batch_images, results)
+                batch_urls, batch_images = [], []
+
+        except Exception as e:
+            results.append({"image_url": url, "error": str(e)})
+
+    # Process remaining images in the last batch
+    if batch_images:
+        process_batch(batch_urls, batch_images, results)
+
+    return results
+
+
+# Helper function to process a batch
+def process_batch(batch_urls, batch_images, results):
+    try:
+        # Generate embeddings
+        embeddings = encoder.encode_images(batch_images)
+        
+        for url, embedding in zip(batch_urls, embeddings):
+            # Normalize embedding
+            embedding_normalized = embedding / np.linalg.norm(embedding)
+            
+            # Append results
+            results.append({
+                "image_url": url,
+                "embedding_preview": embedding_normalized[:5].tolist(),  # First 5 values for preview
+                "success": True
+            })
+    except Exception as e:
+        for url in batch_urls:
+            results.append({"image_url": url, "error": str(e)})
+
 
 # Gradio Interface
 iface = gr.Interface(
-    fn=lambda image_urls: batch_process_images(image_urls, encoder, BATCH_SIZE),
+    fn=batch_process_images,
     inputs=gr.Textbox(
         lines=5,
         placeholder="Enter image URLs separated by commas",