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README.md

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 ---
-title: Multimodal Recipe Rag
-emoji: 🚀
-colorFrom: purple
-colorTo: red
+title: Multimodal Recipe RAG
+emoji: 🍳
+colorFrom: red
+colorTo: pink
 sdk: gradio
-sdk_version: 6.4.0
+sdk_version: 4.44.1
 app_file: app.py
 pinned: false
+license: apache-2.0
+tags:
+  - multimodal
+  - rag
+  - recipe-search
+  - nvidia-nemotron
+  - computer-vision
+  - food
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🍳 Multimodal Recipe RAG System
+
+Search **500 recipes** using text queries across images and documents with three powerful search modes:
+
+- 🖼️ **Image**: Visual similarity search (find similar-looking dishes)
+- 📝 **Text**: Semantic text search (find by ingredients, instructions, reviews)
+- 🎨 **Image+Text**: Combined multimodal search (best of both worlds)
+
+## 🔧 Technology
+
+**Models:**
+- Embedding: [nvidia/llama-nemotron-embed-vl-1b-v2](https://huggingface.co/nvidia/llama-nemotron-embed-vl-1b-v2)
+- Reranking: [nvidia/llama-nemotron-rerank-vl-1b-v2](https://huggingface.co/nvidia/llama-nemotron-rerank-vl-1b-v2)
+
+**Dataset:** [TurkishCodeMan/recipe-synthetic-images-10k](https://huggingface.co/datasets/TurkishCodeMan/recipe-synthetic-images-10k)
+
+**Embeddings:**
+- Image: 2048-dim visual embeddings
+- Text: 2048-dim semantic embeddings  
+- Image+Text: 2048-dim multimodal embeddings
+
+## 💡 Usage
+
+1. Enter your search query (e.g., "chocolate cake", "healthy breakfast")
+2. Choose search modality (Image/Text/Image+Text)
+3. Optionally enable reranking for better results
+4. Click Search!
+
+## 🎯 Example Queries
+
+**Image Mode:**
+- "recipes with steak"
+- "chocolate desserts"
+- "colorful salads"
+
+**Text Mode:**
+- "healthy breakfast ideas"
+- "vegetarian meals with pasta"
+- "quick dinner under 30 minutes"
+
+**Image+Text Mode (Recommended):**
+- "creamy pasta dishes"
+- "spicy chicken recipes"
+- "fresh summer salads"
+
+## ⚡ Performance
+
+- Average search time: ~1-2 seconds
+- With reranking: ~2-3 seconds
+- GPU recommended for optimal performance
+
+## 🏗️ Architecture
+Query → Embedding Model → Similarity Search → [Optional Reranking] → Top Results
+
+
+
+## 📊 Dataset
+
+500 recipes from TurkishCodeMan/recipe-synthetic-images-10k:
+- Recipe images (PNG)
+- Full recipe text (markdown)
+- Ingredients, steps, nutrition info
+- User reviews
+
+## 🙏 Credits
+
+Built with:
+- NVIDIA Nemotron models
+- Hugging Face Transformers
+- Gradio for UI
+
+# Yemek temalı
+colorFrom: red
+colorTo: pink
+
+# Alternatifler
+colorFrom: yellow
+colorTo: red
+
+colorFrom: purple
+colorTo: pink
+
+colorFrom: blue
+colorTo: indigo
+
+colorFrom: green
+colorTo: blue

app.py

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+import gradio as gr
+import torch
+from safetensors.torch import load_file
+from pathlib import Path
+import json
+from functools import lru_cache
+
+# Import utilities
+from models.model_loader import load_embed_model, load_rerank_model
+from utils.search import search_embeddings, rerank_results
+from utils.visualization import plot_results
+
+# ============================================================================
+# Data Loading Functions
+# ============================================================================
+
+@lru_cache(maxsize=1)
+def load_metadata(metadata_path: str = "data/recipes/metadata.json") -> dict:
+    """Load metadata.json (cached)."""
+    with open(metadata_path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def get_recipes_and_paths(metadata_path: str = "data/recipes/metadata.json"):
+    """Load recipes, image paths, and full data."""
+    print("📋 Loading metadata...")
+    metadata = load_metadata(metadata_path)
+    recipes = metadata["recipes"]
+    
+    all_recipes_markdown = [r["markdown"] for r in recipes]
+    all_image_paths = [r["image_path"] for r in recipes]
+    
+    print(f"✅ Loaded {len(recipes)} recipes")
+    return all_recipes_markdown, all_image_paths, recipes
+
+
+# ============================================================================
+# Load Models & Data (once at startup)
+# ============================================================================
+
+print("🔄 Loading models...")
+embed_model, embed_device = load_embed_model()
+rerank_model, rerank_processor, rerank_device = load_rerank_model()
+
+print("📦 Loading embeddings...")
+
+# Load Image embeddings
+print("   - Loading image embeddings...")
+image_embeddings = load_file("data/embeddings/image_embedding_500.safetensors")["image_embeddings_500"]
+print(f"     ✅ Image embeddings shape: {image_embeddings.shape}")
+
+# Load Text embeddings
+print("   - Loading text embeddings...")
+text_embeddings = load_file("data/embeddings/text_embeddings_500.safetensors")["text_embeddings_500"]
+print(f"     ✅ Text embeddings shape: {text_embeddings.shape}")
+
+# Load Image+Text embeddings
+print("   - Loading image+text embeddings...")
+image_text_embeddings = load_file("data/embeddings/image_text_embeddings_500.safetensors")["image_text_embeddings"]
+print(f"     ✅ Image+Text embeddings shape: {image_text_embeddings.shape}")
+
+print("📋 Loading recipe data...")
+all_recipes, all_image_paths, full_recipes = get_recipes_and_paths()
+
+print("\n✅ System ready!")
+print(f"   📚 Recipes loaded: {len(all_recipes)}")
+print(f"   🖼️  Images loaded: {len(all_image_paths)}")
+print(f"   📊 Embeddings:")
+print(f"      - Image: {image_embeddings.shape}")
+print(f"      - Text: {text_embeddings.shape}")
+print(f"      - Image+Text: {image_text_embeddings.shape}")
+
+# ============================================================================
+# Gradio Interface
+# ============================================================================
+
+def search_and_display(
+    query: str,
+    modality: str,
+    use_rerank: bool,
+    num_results: int = 3
+):
+    """Main search function."""
+    
+    if not query.strip():
+        return None, {"error": "Please enter a search query"}
+    
+    # Select embeddings and documents based on modality
+    if modality == "Image":
+        embeddings = image_embeddings
+        documents = [""] * len(all_image_paths)  # Empty for image-only
+        search_modality = "image"
+    elif modality == "Text":
+        embeddings = text_embeddings
+        documents = all_recipes
+        search_modality = "text"
+    else:  # Image+Text
+        embeddings = image_text_embeddings
+        documents = all_recipes
+        search_modality = "image_text"
+    
+    print(f"\n🔍 Searching with modality: {modality}")
+    print(f"   Embeddings shape: {embeddings.shape}")
+    print(f"   Query: {query[:50]}...")
+    
+    # Initial search
+    results = search_embeddings(
+        query=query,
+        query_image=None,
+        model=embed_model,
+        embeddings=embeddings,
+        documents=documents,
+        image_paths=all_image_paths,
+        top_k=20,
+        modality=search_modality
+    )
+    
+    print(f"   ✅ Found {len(results)} results")
+    
+    # Optional reranking (only for image/image_text modalities)
+    if use_rerank and modality != "Text":
+        print(f"   🎯 Reranking top {min(10, len(results))} results...")
+        results = rerank_results(
+            query=query,
+            results=results,
+            rerank_model=rerank_model,
+            rerank_processor=rerank_processor,
+            device=rerank_device,
+            top_k=min(10, len(results))
+        )
+        print(f"   ✅ Reranking complete")
+    
+    # Add full recipe details to results
+    for i, result in enumerate(results[:num_results]):
+        # Find matching recipe index
+        img_path = result.get("image_path")
+        if img_path:
+            try:
+                idx = all_image_paths.index(img_path)
+                result["recipe_details"] = {
+                    "name": full_recipes[idx]["name"],
+                    "description": full_recipes[idx]["description"][:200] + "...",
+                    "tags": full_recipes[idx]["tags"][:5],
+                    "ingredients_count": len(full_recipes[idx]["ingredients"]),
+                    "steps_count": len(full_recipes[idx]["steps"]),
+                }
+            except (ValueError, IndexError):
+                pass
+    
+    # Visualize
+    if modality != "Text":
+        output_img = plot_results(results, query, num_images=num_results)
+        return output_img, results[:num_results]
+    else:
+        # For text-only, return formatted text
+        text_output = "\n\n".join([
+            f"**Rank {r['rank']}** (Score: {r['score']:.4f})\n{r.get('text', '')[:500]}..."
+            for r in results[:num_results]
+        ])
+        return None, results[:num_results]
+
+
+# ============================================================================
+# Gradio UI
+# ============================================================================
+
+with gr.Blocks(title="🍳 Multimodal Recipe Search", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🍳 Multimodal Recipe RAG System
+        Search **500 recipes** using text queries across images and documents.
+        
+        **Three Search Modes:**
+        - 🖼️ **Image**: Visual similarity search (find similar-looking dishes)
+        - 📝 **Text**: Semantic text search (find by ingredients, instructions, reviews)
+        - 🎨 **Image+Text**: Combined multimodal search (best of both worlds)
+        
+        Powered by **NVIDIA Nemotron Embed-VL** and **Rerank-VL** models.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            query_input = gr.Textbox(
+                label="🔍 Search Query",
+                placeholder="e.g., 'chocolate cake recipe', 'healthy breakfast', 'pasta with tomatoes'",
+                lines=2
+            )
+            
+            modality_radio = gr.Radio(
+                choices=["Image", "Text", "Image+Text"],
+                value="Image+Text",  # ✅ Default to Image+Text
+                label="📊 Search Modality",
+                info="Choose how to search: visual, text, or combined"
+            )
+            
+            rerank_check = gr.Checkbox(
+                label="🎯 Use Reranking (Cross-Encoder)",
+                value=True,
+                info="Rerank top results for better accuracy (adds ~1-2s)"
+            )
+            
+            num_results_slider = gr.Slider(
+                minimum=1,
+                maximum=5,
+                value=3,
+                step=1,
+                label="📈 Number of Results to Display"
+            )
+            
+            search_btn = gr.Button("🚀 Search", variant="primary", size="lg")
+            
+            # Add info box
+            gr.Markdown(
+                """
+                **💡 Tips:**
+                - Use **Image** for visual similarity (e.g., "desserts with chocolate")
+                - Use **Text** for ingredient/instruction search (e.g., "vegetarian pasta")
+                - Use **Image+Text** for best overall results
+                """
+            )
+        
+        with gr.Column(scale=2):
+            output_image = gr.Image(label="🖼️ Top Recipe Results", type="pil")
+            output_json = gr.JSON(label="📋 Detailed Results")
+    
+    # Examples for each modality
+    with gr.Accordion("💡 Example Queries", open=False):
+        gr.Examples(
+            examples=[
+                # Image searches
+                ["recipes with steak", "Image", True, 3],
+                ["chocolate desserts", "Image", True, 3],
+                ["colorful salads", "Image", False, 4],
+                
+                # Text searches
+                ["healthy breakfast ideas", "Text", False, 5],
+                ["vegetarian meals with pasta", "Text", False, 3],
+                ["quick dinner under 30 minutes", "Text", False, 4],
+                
+                # Image+Text searches (best results)
+                ["creamy pasta dishes", "Image+Text", True, 3],
+                ["spicy chicken recipes", "Image+Text", True, 3],
+                ["fresh summer salads", "Image+Text", True, 4],
+            ],
+            inputs=[query_input, modality_radio, rerank_check, num_results_slider],
+            label="Try these examples"
+        )
+    
+    # Footer
+    gr.Markdown(
+        """
+        ---
+        ### 🔧 Technical Details
+        
+        **Models:**
+        - 🔍 Embedding: [nvidia/llama-nemotron-embed-vl-1b-v2](https://huggingface.co/nvidia/llama-nemotron-embed-vl-1b-v2)
+        - 🎯 Reranking: [nvidia/llama-nemotron-rerank-vl-1b-v2](https://huggingface.co/nvidia/llama-nemotron-rerank-vl-1b-v2)
+        
+        **Dataset:** [TurkishCodeMan/recipe-synthetic-images-10k](https://huggingface.co/datasets/TurkishCodeMan/recipe-synthetic-images-10k) (500 recipes)
+        
+        **Embeddings:**
+        - Image: 2048-dim visual embeddings
+        - Text: 2048-dim semantic embeddings  
+        - Image+Text: 2048-dim multimodal embeddings
+        """
+    )
+    
+    # Connect button
+    search_btn.click(
+        fn=search_and_display,
+        inputs=[query_input, modality_radio, rerank_check, num_results_slider],
+        outputs=[output_image, output_json]
+    )
+
+# ============================================================================
+# Launch
+# ============================================================================
+
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
+    )

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models/model_loader.py

@@ -0,0 +1,47 @@
+import torch
+from transformers import AutoModel, AutoModelForSequenceClassification, AutoProcessor, AutoConfig
+from functools import lru_cache
+
+@lru_cache(maxsize=2)
+def load_embed_model(model_path: str = "nvidia/llama-nemotron-embed-vl-1b-v2"):
+    """Load embedding model (cached)."""
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+    config._attn_implementation = "sdpa"
+    if hasattr(config, 'llm_config'):
+        config.llm_config._attn_implementation = "sdpa"
+    
+    model = AutoModel.from_pretrained(
+        model_path,
+        config=config,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True,
+    ).eval()
+    
+    return model, device
+
+
+@lru_cache(maxsize=2)
+def load_rerank_model(model_path: str = "nvidia/llama-nemotron-rerank-vl-1b-v2"):
+    """Load reranking model (cached)."""
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_path,
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True,
+        attn_implementation="eager",
+        device_map="auto"
+    ).eval()
+    
+    processor = AutoProcessor.from_pretrained(
+        model_path,
+        trust_remote_code=True,
+        max_input_tiles=6,
+        use_thumbnail=True,
+        rerank_max_length=2048
+    )
+    
+    return model, processor, device

requirements.txt

@@ -0,0 +1,7 @@
+gradio>=4.0.0
+torch>=2.0.0
+transformers>=4.35.0
+safetensors>=0.4.0
+Pillow>=10.0.0
+matplotlib>=3.7.0
+datasets>=2.14.0

utils/search.py

@@ -0,0 +1,104 @@
+import torch
+from typing import List, Dict, Optional
+from PIL import Image
+
+def l2_normalize(x: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:
+    """L2 normalization."""
+    return x / (torch.norm(x, dim=-1, p=2, keepdim=True) + eps)
+
+
+def search_embeddings(
+    query: str,
+    query_image: Optional[Image.Image],
+    model,
+    embeddings: torch.Tensor,
+    documents: List[str],
+    image_paths: List[str],
+    top_k: int = 20,
+    modality: str = "image"
+) -> List[Dict]:
+    """
+    Search embeddings using query.
+    
+    Returns:
+        List of dicts with 'image_path', 'text', 'rank', 'score'
+    """
+    device = next(model.parameters()).device
+    
+    # Encode query
+    with torch.inference_mode():
+        if query_image and query:
+            query_emb = model.encode_documents(images=[query_image], texts=[query])
+        elif query:
+            query_emb = model.encode_queries([query])
+        else:
+            query_emb = model.encode_documents(images=[query_image])
+    
+    # Compute similarity
+    cos_sim = l2_normalize(query_emb.to(device)) @ l2_normalize(embeddings.to(device)).T
+    cos_sim_flat = cos_sim.flatten()
+    sorted_indices = torch.argsort(cos_sim_flat, descending=True)
+    
+    # Format results
+    results = []
+    for rank, idx in enumerate(sorted_indices[:top_k], 1):
+        doc_idx = idx.item()
+        score = cos_sim_flat[doc_idx].item()
+        
+        result = {
+            "rank": rank,
+            "score": score,
+            "image_path": image_paths[doc_idx] if modality != "text" else None,
+            "text": documents[doc_idx] if modality != "image" else None
+        }
+        results.append(result)
+    
+    return results
+
+
+def rerank_results(
+    query: str,
+    results: List[Dict],
+    rerank_model,
+    rerank_processor,
+    device: str,
+    top_k: int = 10
+) -> List[Dict]:
+    """
+    Rerank top results using cross-encoder.
+    
+    Returns:
+        Reranked list of results
+    """
+    from transformers.image_utils import load_image
+    
+    # Prepare examples
+    examples = []
+    for result in results[:top_k]:
+        img = load_image(result["image_path"])
+        examples.append({
+            "question": query,
+            "doc_text": result.get("text", ""),
+            "doc_image": img
+        })
+    
+    # Process batch
+    batch = rerank_processor.process_queries_documents_crossencoder(examples)
+    batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}
+    
+    # Get logits
+    with torch.no_grad():
+        outputs = rerank_model(**batch, return_dict=True)
+    
+    logits = outputs.logits.squeeze(-1)
+    rerank_indices = torch.argsort(logits, descending=True)
+    
+    # Reorder results
+    reranked = []
+    for new_rank, idx in enumerate(rerank_indices, 1):
+        old_result = results[idx.item()]
+        old_result["rank"] = new_rank
+        old_result["score"] = logits[idx].item()
+        reranked.append(old_result)
+    
+    return reranked

utils/visualization.py

@@ -0,0 +1,39 @@
+import matplotlib.pyplot as plt
+from typing import List, Dict
+import io
+from PIL import Image
+
+def plot_results(results: List[Dict], query: str, num_images: int = 3) -> Image.Image:
+    """
+    Plot search results as image grid.
+    
+    Returns:
+        PIL Image of the plot
+    """
+    fig, axes = plt.subplots(1, num_images, figsize=(20, 7))
+    if num_images == 1:
+        axes = [axes]
+    
+    fig.suptitle(f"🔍 Query: {query}", fontsize=18, fontweight='bold')
+    
+    for i, ax in enumerate(axes):
+        if i < len(results):
+            result = results[i]
+            img = plt.imread(result["image_path"])
+            ax.imshow(img)
+            ax.set_title(
+                f"Rank {result['rank']} | Score: {result['score']:.4f}",
+                fontsize=14
+            )
+        ax.axis('off')
+    
+    plt.tight_layout()
+    
+    # Convert to PIL Image
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png', bbox_inches='tight', dpi=100)
+    buf.seek(0)
+    img_pil = Image.open(buf)
+    plt.close()
+    
+    return img_pil