Update app.py

app.py

@@ -44,23 +44,37 @@ vision_client = InferenceClient(model="Salesforce/blip2-opt-2.7b")
 # multimodal_client = InferenceClient(model="microsoft/DialoGPT-medium")  # For conversational AI
 # multimodal_client = InferenceClient(model="facebook/opt-iml-max-30b")   # For instruction following
 
-# ── Multimodal Embeddings ───────────────────────────────────────────────────
-# Primary: CLIP embeddings for excellent text-image alignment
-try:
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/clip-ViT-B-32",
-        model_kwargs={'device': 'cpu'},  # Ensure CPU usage for HF Spaces
-        encode_kwargs={'normalize_embeddings': True}
-    )
-    print("✅ Using CLIP embeddings for multimodal support")
-except Exception as e:
-    print(f"⚠️ CLIP failed, falling back to BGE: {e}")
-    # Fallback to BGE embeddings
-    embeddings = HuggingFaceEmbeddings(
-        model_name="BAAI/bge-base-en-v1.5",
-        model_kwargs={'device': 'cpu'},
-        encode_kwargs={'normalize_embeddings': True}
-    )
+# ── Open Source Multimodal Embeddings ──────────────────────────────────────
+# Primary choices - all open source, no OpenAI dependency
+embedding_models = [
+    "sentence-transformers/all-mpnet-base-v2",      # Excellent general purpose
+    "BAAI/bge-large-en-v1.5",                      # Best Chinese model, great English
+    "intfloat/e5-large-v2",                        # Microsoft's open model
+    "sentence-transformers/all-MiniLM-L12-v2",     # Good balance speed/quality
+    "BAAI/bge-base-en-v1.5"                        # Fallback option
+]
+
+def initialize_embeddings():
+    """Initialize embeddings with fallback options"""
+    for model_name in embedding_models:
+        try:
+            embeddings = HuggingFaceEmbeddings(
+                model_name=model_name,
+                model_kwargs={'device': 'cpu', 'trust_remote_code': True},
+                encode_kwargs={'normalize_embeddings': True, 'batch_size': 16}
+            )
+            print(f"✅ Successfully loaded: {model_name}")
+            return embeddings
+        except Exception as e:
+            print(f"⚠️ Failed to load {model_name}: {e}")
+            continue
+    
+    # Ultimate fallback - should always work
+    print("🔄 Using basic sentence-transformers model")
+    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# Initialize embeddings
+embeddings = initialize_embeddings()
 
 def create_multimodal_embeddings(text_chunks, image_descriptions):
     """