Logging for new embeddings in inference.py

backend/runner/inference.py

@@ -385,8 +385,45 @@ def run_inference(
         print(f"🔍 Loading and preprocessing image: {image_path}")
         image = Image.open(image_path).convert("RGB")
         print(f"✅ Image loaded successfully, size: {image.size}")
-        
-        # Continue with the rest of the function...
+
+        # Compute image embedding
+        inputs = processor(images=image, return_tensors="pt")
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+
+        with torch.no_grad():
+            image_features = model.get_image_features(**inputs)
+            image_embedding = F.normalize(image_features.squeeze(0), dim=-1)
+
+        # Normalize sentence embeddings and compute similarities
+        sentence_embeddings = F.normalize(filtered_embeddings.to(device), dim=-1)
+        similarities = torch.matmul(sentence_embeddings, image_embedding).cpu()
+
+        # Get top-K results
+        k = min(top_k, len(similarities))
+        top_scores, top_indices = torch.topk(similarities, k=k)
+
+        # Build results with full sentence metadata
+        results = []
+        for rank, (idx, score) in enumerate(zip(top_indices.tolist(), top_scores.tolist()), start=1):
+            sentence_id = filtered_sentence_ids[idx]
+            sentence_data = sentences_data.get(
+                sentence_id,
+                {"English Original": f"[Sentence data not found for {sentence_id}]", "Has PaintingCLIP Embedding": True},
+            ).copy()
+            work_id = sentence_id.split("_")[0]
+            sentence_data.setdefault("Work", work_id)
+            results.append({
+                "id": sentence_id,
+                "score": float(score),
+                "english_original": sentence_data.get("English Original", "N/A"),
+                "work": work_id,
+                "rank": rank,
+            })
+
+        print(f"🔍 run_inference returning {len(results)} results")
+        if results:
+            print(f"🔍 First result: {results[0]}")
+        return results
         
     except Exception as e:
         print(f"❌ Error in run_inference: {e}")