Uploading Gradio multimodal RAG demo

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+kitchen_bench.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,32 @@
 ---
-title: Multimodal Rag With Nemotron
-emoji: 📉
-colorFrom: red
-colorTo: gray
+title: Multimodal RAG with Nemotron Embed and Rerank VL
+emoji: 👁️📑
+colorFrom: green
+colorTo: green
 sdk: gradio
-sdk_version: 6.8.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Multimodal RAG with Nemotron Embed and Rerank VL Models
+
+Search for recipes using input queries of text or images.
+
+Dataset is 10,000 recipes from [Food.com](https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions) turned into images and markdown and embedded into the same feature space.
+
+Options: 
+
+* Default is to retrieve most similar recipes via visual or text search
+* Optional: Rerank initial results with reranker model (note: this increases latency)
+* Optional: Generate a summary of the returned recipes (note: this increases latency)
+
+Details:
+
+* **Dataset used:** https://huggingface.co/datasets/mrdbourke/recipe-synthetic-images-10k
+* **Embedding model used:** https://huggingface.co/nvidia/llama-nemotron-embed-vl-1b-v2
+    * **Note:** By default we use the image + text embeddings as we have access to image and text pairs in our dataset, and according to the [launch blog post](https://huggingface.co/blog/nvidia/llama-nemotron-vl-1b), these work the best.
+* **Rerank model used:** https://huggingface.co/nvidia/llama-nemotron-rerank-vl-1b-v2
+* **Generation model used:** https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct (note: you could use a larger model such as [Nemotron v3](https://huggingface.co/collections/nvidia/nvidia-nemotron-v3), however, this will require more compute resources)
+
+**Note:** This README.md was authored in a [live tutorial recorded for YouTube](https://youtube.com/live/XWmqcFiBvpU?feature=share).

app.py

@@ -0,0 +1,655 @@
+"""
+Multimodal RAG Demo with Nemotron Embed VL and Rerank VL
+
+A Gradio demo for multimodal retrieval augmented generation using:
+- Dataset: mrdbourke/recipe-synthetic-images-10k
+- Embedding model: nvidia/llama-nemotron-embed-vl-1b-v2
+- Rerank model: nvidia/llama-nemotron-rerank-vl-1b-v2
+- Generation model: Qwen/Qwen3-VL-2B-Instruct
+"""
+
+import spaces
+import time
+import torch
+import gradio as gr
+from PIL import Image
+from datasets import load_dataset
+from safetensors.torch import load_file
+from transformers import (
+    AutoModel,
+    AutoModelForSequenceClassification,
+    AutoProcessor,
+    Qwen3VLForConditionalGeneration,
+)
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Model paths and commit hashes (required for sdpa attention on HF Spaces)
+EMBED_MODEL_PATH = "nvidia/llama-nemotron-embed-vl-1b-v2"
+EMBED_COMMIT_HASH = "5b5ca69c35bf6ec1484d2d5ff238626e67a745e2"
+
+RERANK_MODEL_PATH = "nvidia/llama-nemotron-rerank-vl-1b-v2"
+RERANK_COMMIT_HASH = "47e5a355d1a050c3e5f69d53f14964b1d34bcd9d"
+
+# GENERATION_MODEL_ID = "Qwen/Qwen3-VL-2B-Instruct"
+GENERATION_MODEL_ID = "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8"
+
+# ============================================================================
+# Load Dataset and Embeddings
+# ============================================================================
+
+print("[INFO] Loading dataset...")
+dataset = load_dataset(path="mrdbourke/recipe-synthetic-images-10k")
+print(f"[INFO] Dataset loaded with {len(dataset['train'])} samples")
+
+print("[INFO] Loading embeddings...")
+image_text_embeddings = load_file("image_text_embeddings_10k.safetensors")
+# Note: Load embeddings to CPU first and then move them to GPU *inside* the retrieve function to
+# make use of the @spaces.GPU decorator.
+image_text_embeddings = image_text_embeddings["image_text_embeddings"]
+print(f"[INFO] Embeddings loaded: {image_text_embeddings.shape}")
+
+# ============================================================================
+# Load Models
+# ============================================================================
+modality_to_tokens = {
+    "image": 2048,
+    "image_text": 10240,
+    "text": 8192
+}
+
+print(f"[INFO] Loading embedding model from: {EMBED_MODEL_PATH} with commit: {EMBED_COMMIT_HASH}")
+embed_model = AutoModel.from_pretrained(
+    EMBED_MODEL_PATH,
+    revision=EMBED_COMMIT_HASH,
+    dtype=torch.bfloat16,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+).eval()
+
+# Set embed processor kwargs
+# Note: These are the suggest settings from the embed model card
+embed_modality = "image_text"
+embed_processor_kwargs = {
+    "max_input_tiles": 6,
+    "use_thumbnail": True,
+    "p_max_length": modality_to_tokens[embed_modality]
+}
+
+embed_processor = AutoProcessor.from_pretrained(
+    EMBED_MODEL_PATH,
+    revision=EMBED_COMMIT_HASH,
+    trust_remote_code=True,
+    **embed_processor_kwargs
+)
+print(f"[INFO] Embedding model loaded!")
+print(f"[INFO] Embed processor using p_max_length: {embed_processor.p_max_length}")
+
+print(f"[INFO] Loading rerank model from: {RERANK_MODEL_PATH} with commit: {RERANK_COMMIT_HASH}")
+rerank_model = AutoModelForSequenceClassification.from_pretrained(
+    RERANK_MODEL_PATH,
+    revision=RERANK_COMMIT_HASH,
+    dtype=torch.bfloat16,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+).eval()
+
+# Set rerank processor kwargs
+# Note: These are the suggest settings from the rerank model card
+rerank_modality = "image_text"
+rerank_processor_kwargs = {
+    "max_input_tiles": 6,
+    "use_thumbnail": True,
+    "rerank_max_length": modality_to_tokens[rerank_modality]
+}
+
+rerank_processor = AutoProcessor.from_pretrained(
+    RERANK_MODEL_PATH,
+    revision=RERANK_COMMIT_HASH,
+    trust_remote_code=True,
+    **rerank_processor_kwargs
+)
+print(f"[INFO] Rerank processor using rerank_max_length: {rerank_processor.rerank_max_length}")
+
+print(f"[INFO] Rerank model loaded!")
+
+print("[INFO] Loading generation model...")
+qwen_model = Qwen3VLForConditionalGeneration.from_pretrained(
+    GENERATION_MODEL_ID,
+    dtype="auto",
+    device_map="auto"
+)
+qwen_processor = AutoProcessor.from_pretrained(GENERATION_MODEL_ID)
+print(f"[INFO] Generation model loaded")
+
+# ============================================================================
+# Helper Functions
+# ============================================================================
+
+def _l2_normalize(x: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:
+    """L2 normalize a tensor along the last dimension."""
+    return x / (x.norm(p=2, dim=-1, keepdim=True) + eps)
+
+
+def match_query_to_embeddings(
+    query: str | Image.Image,
+    target_embeddings_to_match: torch.Tensor,
+    top_k: int = 100
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Match a query (text or image) to target embeddings.
+
+    Returns:
+        Tuple of (sorted_scores, sorted_indices)
+    """
+    with torch.inference_mode():
+        if isinstance(query, Image.Image):
+            query_embeddings = embed_model.encode_documents(images=[query])
+        else:
+            query_embeddings = embed_model.encode_queries([query])
+
+    cos_sim = _l2_normalize(query_embeddings) @ _l2_normalize(target_embeddings_to_match).T
+    cos_sim_flat = cos_sim.flatten()
+    sorted_indices = torch.argsort(cos_sim_flat, descending=True)[:top_k]
+    sorted_scores = cos_sim_flat[sorted_indices][:top_k]
+
+    return sorted_scores, sorted_indices
+
+
+def rerank_samples(
+    dataset,
+    query_text: str,
+    sorted_indices: list | torch.Tensor,
+    num_samples_to_rerank: int,
+    rerank_model,
+    rerank_processor,
+    device: str = DEVICE,
+    text_column: str = "recipe_markdown",
+    image_column: str = "image",
+    dataset_split: str = "train",
+) -> tuple:
+    """
+    Rerank top samples using the vision-language reranker model.
+
+    Returns:
+        Tuple of (dataset_samples_to_rerank, rerank_sorted_indices)
+    """
+    top_indices = sorted_indices[:num_samples_to_rerank]
+    dataset_samples_to_rerank = dataset[dataset_split].select(top_indices)
+
+    texts_to_rerank = dataset_samples_to_rerank[text_column]
+    images_to_rerank = dataset_samples_to_rerank[image_column]
+
+    samples_to_rerank = [
+        {"question": query_text, "doc_text": text, "doc_image": image}
+        for text, image in zip(texts_to_rerank, images_to_rerank)
+    ]
+
+    batch_dict_rerank = rerank_processor.process_queries_documents_crossencoder(samples_to_rerank)
+    batch_dict_rerank = {
+        k: v.to(device) if isinstance(v, torch.Tensor) else v
+        for k, v in batch_dict_rerank.items()
+    }
+
+    with torch.inference_mode():
+        rerank_outputs = rerank_model(**batch_dict_rerank, return_dict=True)
+
+    rerank_logits = rerank_outputs.logits.squeeze(-1)
+    rerank_sorted_indices = torch.argsort(rerank_logits, descending=True)
+
+    return dataset_samples_to_rerank, rerank_sorted_indices
+
+
+def generate_recipe_summary(
+    recipe_texts: list[str],
+    model = None,
+    processor = None,
+    max_new_tokens: int = 512
+) -> str:
+    """Generate a markdown summary of multiple recipes."""
+    if model is None:
+        model = qwen_model
+    if processor is None:
+        processor = qwen_processor
+
+    recipes_combined = ""
+    for i, recipe in enumerate(recipe_texts[:3], 1):
+        recipes_combined += f"\n\n--- RECIPE {i} ---\n{recipe}"
+
+    prompt = f"""You are a helpful culinary assistant. Below are {len(recipe_texts[:3])} recipes. 
+Please provide a brief markdown summary with:
+- A short 1-2 sentence overview of each recipe
+- Key ingredients highlighted
+- Estimated difficulty (Easy/Medium/Hard)
+- Which recipe might be best for a quick weeknight dinner
+
+For example use the following format: 
+
+```markdown
+# Recipe summary
+
+## <recipe_name>
+
+[details]
+
+## <recipe_name>
+
+[details]
+
+## <recipe_name>
+
+[details]
+```
+
+Keep the summary concise and well-formatted in markdown. Return in ```markdown``` tags so it can be easily parsed.
+
+<recipes>
+{recipes_combined}
+</recipes>
+
+## Summary:"""
+
+    messages = [{"role": "user", "content": [{"type": "text", "text": prompt}]}]
+
+    inputs = processor.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt"
+    )
+    inputs = inputs.to(model.device)
+
+    with torch.no_grad():
+        generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9
+        )
+
+    generated_ids_trimmed = [
+        out_ids[len(in_ids):] 
+        for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+
+    output_text = processor.batch_decode(
+        generated_ids_trimmed,
+        skip_special_tokens=True,
+        clean_up_tokenization_spaces=False
+    )[0]
+
+    return output_text.strip()
+
+
+def _markdown_to_simple_html(markdown_text: str, max_reviews: int = 1) -> str:
+    """Convert recipe markdown to a simple HTML card."""
+    lines = markdown_text.strip().split('\n')
+
+    title = ""
+    description = ""
+    recipe_id = ""
+    cook_time = ""
+    num_ratings = ""
+    ingredients = []
+    steps = []
+    tags = []
+    reviews = []
+
+    current_section = None
+    in_ingredients = False
+    in_steps = False
+    in_reviews = False
+    in_tags = False
+    review_count = 0
+
+    for line in lines:
+        line = line.strip()
+
+        if line.startswith('# ') and not title:
+            title = line[2:].strip()
+            continue
+
+        if line.startswith('**ID:**'):
+            recipe_id = line.replace('**ID:**', '').strip()
+            continue
+        if line.startswith('**Time:**'):
+            cook_time = line.replace('**Time:**', '').strip()
+            continue
+        if line.startswith('**Number of Ratings:**'):
+            num_ratings = line.replace('**Number of Ratings:**', '').strip()
+            continue
+
+        if line.startswith('## '):
+            section_name = line[3:].strip().lower()
+            in_ingredients = section_name == 'ingredients'
+            in_steps = section_name.startswith('steps')
+            in_reviews = section_name == 'reviews'
+            in_tags = section_name == 'tags'
+            current_section = section_name
+            continue
+
+        if current_section == 'description' and line and not line.startswith('#'):
+            description = line
+            continue
+
+        if in_ingredients and line.startswith('- '):
+            ingredients.append(line[2:].strip())
+            continue
+
+        if in_steps and line and line[0].isdigit():
+            step_text = line.split('. ', 1)[-1] if '. ' in line else line
+            steps.append(step_text.strip())
+            continue
+
+        if in_tags and line.startswith('`'):
+            tag_list = [t.strip().strip('`') for t in line.split(',')]
+            tags.extend(tag_list)
+            continue
+
+        if in_reviews and line.startswith('> ') and review_count < max_reviews:
+            reviews.append(line[2:].strip())
+            review_count += 1
+            continue
+
+    html = f'''
+    <div style="border: 1px solid #ddd; border-radius: 8px; padding: 16px; margin: 4px; background: #fff; font-family: system-ui, -apple-system, sans-serif; font-size: 12px; height: 400px; overflow-y: auto; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+        <div style="font-weight: bold; font-size: 14px; color: #333; margin-bottom: 8px;">{title}</div>
+
+        <div style="display: flex; gap: 12px; font-size: 11px; color: #666; margin-bottom: 10px; flex-wrap: wrap;">
+            {f'<span>⏱️ {cook_time}</span>' if cook_time else ''}
+            {f'<span>⭐ {num_ratings} ratings</span>' if num_ratings else ''}
+            {f'<span style="color: #999;">ID: {recipe_id}</span>' if recipe_id else ''}
+        </div>
+
+        <div style="color: #555; margin-bottom: 12px; font-style: italic; line-height: 1.4;">{description[:150]}{"..." if len(description) > 150 else ""}</div>
+
+        <div style="margin-bottom: 12px;">
+            <div style="font-weight: bold; font-size: 11px; color: #333; margin-bottom: 4px;">📝 Ingredients</div>
+            <div style="color: #444; line-height: 1.5;">{", ".join(ingredients[:8])}{"..." if len(ingredients) > 8 else ""}</div>
+        </div>
+
+        <div style="margin-bottom: 12px;">
+            <div style="font-weight: bold; font-size: 11px; color: #333; margin-bottom: 4px;">👨‍🍳 Steps ({len(steps)} total)</div>
+            <ol style="margin: 0; padding-left: 20px; color: #444; line-height: 1.5;">
+                {"".join(f'<li style="margin-bottom: 4px;">{step[:80]}{"..." if len(step) > 80 else ""}</li>' for step in steps[:4])}
+                {f'<li style="color: #999;">...and {len(steps) - 4} more steps</li>' if len(steps) > 4 else ''}
+            </ol>
+        </div>
+    '''
+
+    if tags:
+        display_tags = tags[:5]
+        html += f'''
+        <div style="margin-bottom: 12px;">
+            <div style="font-weight: bold; font-size: 11px; color: #333; margin-bottom: 4px;">🏷️ Tags</div>
+            <div style="display: flex; flex-wrap: wrap; gap: 4px;">
+                {"".join(f'<span style="background: #f0f0f0; padding: 2px 6px; border-radius: 4px; font-size: 10px;">{tag}</span>' for tag in display_tags)}
+                {f'<span style="color: #999; font-size: 10px;">+{len(tags) - 5} more</span>' if len(tags) > 5 else ''}
+            </div>
+        </div>
+        '''
+
+    if reviews:
+        html += f'''
+        <div style="border-top: 1px solid #eee; padding-top: 10px; margin-top: 10px;">
+            <div style="font-weight: bold; font-size: 11px; color: #333; margin-bottom: 4px;">💬 Review</div>
+            <div style="color: #555; font-size: 11px; line-height: 1.4; background: #f9f9f9; padding: 8px; border-radius: 4px; font-style: italic;">"{reviews[0][:200]}{"..." if len(reviews[0]) > 200 else ""}"</div>
+        </div>
+        '''
+
+    html += '</div>'
+    return html
+
+
+def create_recipe_cards_html(
+    scores_and_samples: list[dict],
+    num_results: int = 3,
+    text_key: str = "text",
+    max_reviews: int = 1
+) -> str:
+    """Generate combined HTML cards from scored recipe samples."""
+    recipe_cards_html = []
+
+    for item in scores_and_samples[:num_results]:
+        sample = item["sample"]
+        markdown_text = sample.get(text_key, "") or sample.get("markdown", "")
+        card_html = _markdown_to_simple_html(markdown_text, max_reviews=max_reviews)
+        recipe_cards_html.append(f'<div style="flex: 1; min-width: 0;">{card_html}</div>')
+
+    combined_html = f'''
+    <div style="margin-top: 16px;">
+        <h3 style="font-family: system-ui, -apple-system, sans-serif; font-size: 16px; font-weight: 600; color: #333; margin-bottom: 12px;">Retrieved Texts</h3>
+        <div style="display: flex; flex-direction: row; gap: 12px; width: 100%;">
+            {"".join(recipe_cards_html)}
+        </div>
+    </div>
+    '''
+
+    return combined_html
+
+
+# ============================================================================
+# Main Retrieve Function
+# ============================================================================
+
+@spaces.GPU
+def retrieve(
+    query_text: str | None,
+    query_image: Image.Image | None,
+    rerank_option: str,
+    generate_summary_option: str
+):
+    """
+    Main retrieval function for the Gradio interface.
+
+    Args:
+        query_text: Text query input
+        query_image: Image query input (PIL Image)
+        rerank_option: "True" or "False" to enable reranking
+        generate_summary_option: "True" or "False" to enable summary generation
+
+    Returns:
+        Tuple of (image_gallery, recipe_cards_html, summary, timing_dict)
+    """
+
+    embeddings_on_gpu = image_text_embeddings.to("cuda")
+
+    # Determine input query (prefer text over image)
+    if query_text and query_text.strip():
+        input_query = query_text
+    elif query_image is not None:
+        input_query = query_image
+    else:
+        raise gr.Error("Please provide either a text query or an image query.")
+
+    # === Retrieval ===
+    start_time_query_to_embed_match = time.time()
+    result_sorted_scores, result_sorted_indices = match_query_to_embeddings(
+        query=input_query,
+        target_embeddings_to_match=embeddings_on_gpu,
+        top_k=20
+    )
+    end_time_query_to_embed_match = time.time()
+
+    top_dataset_results_to_show = dataset["train"].select(result_sorted_indices)
+
+    scores_and_samples = [
+        {"score": round(score.item(), 4), "sample": sample}
+        for score, sample in zip(result_sorted_scores, top_dataset_results_to_show)
+    ]
+
+    output_image_gallery = [
+        (item["sample"]["image"], f'Score: {item["score"]}')
+        for item in scores_and_samples[:3]
+    ]
+
+    output_recipe_cards_html = create_recipe_cards_html(
+        scores_and_samples=scores_and_samples,
+        num_results=3,
+        text_key="recipe_markdown",
+        max_reviews=1
+    )
+
+    # === Reranking (optional) ===
+    if rerank_option == "True":
+        start_time_reranking = time.time()
+        dataset_samples_to_rerank, rerank_sorted_indicies = rerank_samples(
+            sorted_indices=result_sorted_indices,
+            dataset=dataset,
+            dataset_split="train",
+            query_text=input_query,
+            num_samples_to_rerank=20,
+            rerank_model=rerank_model,
+            rerank_processor=rerank_processor
+        )
+        end_time_reranking = time.time()
+        rerank_time = round(end_time_reranking - start_time_reranking, 4)
+
+        top_dataset_results_to_show = dataset_samples_to_rerank.select(rerank_sorted_indicies)
+        samples_and_rerank_changes = []
+        for new_rank, (sample, original_rank) in enumerate(zip(top_dataset_results_to_show, rerank_sorted_indicies)):
+            movement = new_rank - original_rank
+            if movement == 0:
+                movement_string = f"{movement}"
+            else:
+                movement_string = f"+{abs(movement)}" if movement < 0 else f"-{movement}"
+            rerank_string = f"Original rank: {original_rank} | New rank: {new_rank} | Movement: {movement_string}"
+            samples_and_rerank_changes.append({"sample": sample, "rerank_string": rerank_string})
+
+        output_image_gallery = [
+            (item["sample"]["image"], item["rerank_string"])
+            for item in samples_and_rerank_changes[:3]
+        ]
+        output_recipe_cards_html = create_recipe_cards_html(
+            scores_and_samples=samples_and_rerank_changes,
+            num_results=3,
+            text_key="recipe_markdown",
+            max_reviews=1
+        )
+    else:
+        rerank_time = "Reranking turned off"
+
+    # === Generation (optional) ===
+    if generate_summary_option == "True":
+        start_time_generation_output = time.time()
+        if rerank_option == "True":
+            recipe_texts = [item["sample"]["recipe_markdown"] for item in samples_and_rerank_changes[:3]]
+        else:
+            recipe_texts = [item["sample"]["recipe_markdown"] for item in scores_and_samples[:3]]
+
+        summary = generate_recipe_summary(recipe_texts)
+        summary = summary.replace("```markdown", "").replace("```", "")
+        end_time_generation_output = time.time()
+        generation_time = round(end_time_generation_output - start_time_generation_output, 4)
+    else:
+        generation_time = "Generation turned off"
+        summary = "Generation turned off, no summary created"
+
+    timing_dict = {
+        "query_embed_and_match_time": round(end_time_query_to_embed_match - start_time_query_to_embed_match, 4),
+        "rerank_time": rerank_time,
+        "generation_time": generation_time
+    }
+
+    return output_image_gallery, output_recipe_cards_html, summary, timing_dict
+
+
+# ============================================================================
+# Gradio Interface
+# ============================================================================
+
+with gr.Blocks(title="Multimodal RAG Demo") as demo:
+    gr.Markdown("""# 👁️📑 Multimodal RAG Demo with Nemotron Embed VL and Rerank VL
+
+Input an image or text about food and get recipe images/text back.
+
+This is a scalable workflow that can lend itself to many use cases such as business document retrieval, technical manual look ups and more.
+
+By default it returns the top 3 results from a database of 10,000+ recipes. We've limited it to 3 for the demo but in practice you could return as many as you like.
+
+* **Dataset used:** https://huggingface.co/datasets/mrdbourke/recipe-synthetic-images-10k
+* **Embedding model used:** https://huggingface.co/nvidia/llama-nemotron-embed-vl-1b-v2
+    * **Note:** By default we use the image + text embeddings as we have access to image and text pairs in our dataset, and according to the [launch blog post](https://huggingface.co/blog/nvidia/llama-nemotron-vl-1b), these work the best.
+* **Rerank model used:** https://huggingface.co/nvidia/llama-nemotron-rerank-vl-1b-v2
+* **Generation model used:** https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct (note: you could use a larger model such as [Nemotron v3](https://huggingface.co/collections/nvidia/nvidia-nemotron-v3), however, this will require more compute resources)
+""")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### Query Input")
+
+            query_text = gr.Textbox(
+                label="Text Query",
+                placeholder="e.g. 'dinner recipes with tomatoes'",
+                lines=2
+            )
+
+            query_image = gr.Image(
+                label="Image Query (optional)",
+                type="pil",
+                height=200
+            )
+
+            generate_summary_option = gr.Radio(
+                choices=["True", "False"],
+                value="False",
+                label="Generate recipe summary"
+            )
+
+            rerank_option = gr.Radio(
+                choices=["True", "False"],
+                value="False",
+                label="Rerank initial results? (note: reranking is for text queries only)"
+            )
+
+            search_btn = gr.Button("Search", variant="primary", size="lg")
+
+        with gr.Column(scale=2):
+            gr.Markdown("### Retrieved Results")
+
+            gallery_output = gr.Gallery(
+                label="Retrieved Recipe Images",
+                columns=3,
+                height="auto",
+                object_fit="cover",
+                show_label=True
+            )
+
+            recipes_html = gr.HTML(label="Retrieved Recipe Texts")
+
+            summary_generation = gr.Markdown(
+                label="Retrieved Recipe Summary (generated from top results)"
+            )
+
+            timing_output = gr.JSON(label="Timings")
+
+    gr.Markdown("### Example Queries")
+
+    gr.Examples(
+        examples=[
+            ["best omelette recipes", None, "False", "False"],
+            ["best omelette recipes", None, "False", "True"],
+            ["best omelette recipes", None, "True", "True"],
+            ["eggplant dip", None, "True", "True"],
+            [None, "kitchen_bench.png", "False", "True"]
+        ],
+        inputs=[query_text, query_image, rerank_option, generate_summary_option],
+        label="Example Queries"
+    )
+
+    search_btn.click(
+        fn=retrieve,
+        inputs=[query_text, query_image, rerank_option, generate_summary_option],
+        outputs=[gallery_output, recipes_html, summary_generation, timing_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,10 @@
+torch==2.9.1
+torchvision==0.24.1
+transformers==4.57.3
+gradio==6.2.0
+datasets==4.4.2
+safetensors==0.7.0
+Pillow==12.0.0
+accelerate==1.12.0
+qwen-vl-utils==0.0.14
+spaces