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README.md

@@ -1,12 +1,19 @@
 ---
-title: Sharif Nlp Food Rag
-emoji: 🐠
-colorFrom: green
-colorTo: blue
+title: Food RAG Demo
+emoji: 🍜
+colorFrom: purple
+colorTo: indigo
 sdk: gradio
-sdk_version: 5.45.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This Space demonstrates a simple **text + image retrieval** workflow against your uploaded FAISS indexes, with answers generated via the **Hugging Face Inference API** (set `HF_TOKEN` as a Space secret).
+
+**Environment variables you can set in the Space:**
+- `TEXT_MODEL_REPO` (default: `<your-username>/text-ft-food-rag`)
+- `CLIP_MODEL_REPO` (default: `<your-username>/clip-ft-food-rag`)
+- `DATASET_REPO` (default: `<your-username>/food-rag-index`)
+- `LLM_ID` (default: `mistralai/Mistral-7B-Instruct-v0.3`)
+- `HF_TOKEN` (set this secret in the Space to call the Inference API)

app.py

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+
+# Simple HF Space to test your RAG + image/text search with your Hub models.
+# Move this file (and requirements.txt + README.md) into a new Space.
+import os, json
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+
+import gradio as gr
+import numpy as np
+import faiss
+from PIL import Image
+
+from huggingface_hub import snapshot_download
+from sentence_transformers import SentenceTransformer
+import torch
+from transformers import CLIPModel, CLIPProcessor
+
+# ========== CONFIG (edit to your repos) ==========
+TEXT_MODEL_REPO = os.environ.get("TEXT_MODEL_REPO", "<your-username>/text-ft-food-rag")
+CLIP_MODEL_REPO = os.environ.get("CLIP_MODEL_REPO", "<your-username>/clip-ft-food-rag")
+DATASET_REPO    = os.environ.get("DATASET_REPO",    "<your-username>/food-rag-index")
+# LLM via Inference API (set HF_TOKEN in Space secrets). Change to your preferred instruct model.
+LLM_ID = os.environ.get("LLM_ID", "mistralai/Mistral-7B-Instruct-v0.3")
+# =================================================
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# ---- Download dataset snapshot (FAISS + metas + optionally images/) ----
+DATA_DIR = snapshot_download(repo_id=DATASET_REPO, repo_type="dataset")
+
+# Expected files inside DATA_DIR:
+#   faiss_text.bin, faiss_image.bin, text_meta.jsonl, image_meta.jsonl
+#   images/ (optional) if you want to show pictures next to results
+
+def read_jsonl(path: str):
+    out = []
+    with open(path, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                out.append(json.loads(line))
+    return out
+
+# Load metas & FAISS
+TEXT_META  = read_jsonl(os.path.join(DATA_DIR, "text_meta.jsonl"))
+IMAGE_META = read_jsonl(os.path.join(DATA_DIR, "image_meta.jsonl"))
+T_INDEX = faiss.read_index(os.path.join(DATA_DIR, "faiss_text.bin"))
+I_INDEX = faiss.read_index(os.path.join(DATA_DIR, "faiss_image.bin"))
+
+# Load encoders
+text_enc = SentenceTransformer(TEXT_MODEL_REPO, device=DEVICE)
+clip_model = CLIPModel.from_pretrained(CLIP_MODEL_REPO).to(DEVICE)
+clip_proc  = CLIPProcessor.from_pretrained(CLIP_MODEL_REPO)
+
+# Optional: LLM via HF Inference API (so Spaces don't need to run an LLM locally)
+try:
+    from huggingface_hub import InferenceClient
+    HF_TOKEN = os.environ.get("HF_TOKEN")  # set this in Space -> Settings -> Repository secrets
+    client = InferenceClient(model=LLM_ID, token=HF_TOKEN)
+except Exception as e:
+    client = None
+
+@dataclass
+class Pair:
+    rank: int
+    idx: int
+    doc_id: str
+    title: Optional[str]
+    score: float
+    image_path: Optional[str]
+
+def _pair_from_idx(idx: int, score: float, rank: int) -> Pair:
+    m = TEXT_META[idx]
+    img_path = IMAGE_META[idx].get("image_path")
+    return Pair(rank=rank, idx=idx, doc_id=m.get("id"), title=m.get("title"), score=float(score), image_path=img_path)
+
+def search_text(q: str, topk: int = 10) -> List[Pair]:
+    qv = text_enc.encode([q], convert_to_numpy=True, normalize_embeddings=True).astype("float32")
+    D, I = T_INDEX.search(qv, topk)
+    out = []
+    for r, (i, s) in enumerate(zip(I[0].tolist(), D[0].tolist()), start=1):
+        if i < 0: continue
+        out.append(_pair_from_idx(i, s, r))
+    return out
+
+def search_image(img: Image.Image, topk: int = 10) -> List[Pair]:
+    inputs = clip_proc(images=[img.convert("RGB")], return_tensors="pt").to(DEVICE)
+    with torch.no_grad():
+        qv = clip_model.get_image_features(**inputs)
+        qv = torch.nn.functional.normalize(qv, dim=1).float().cpu().numpy().astype(np.float32)
+    D, I = I_INDEX.search(qv, topk)
+    out = []
+    for r, (i, s) in enumerate(zip(I[0].tolist(), D[0].tolist()), start=1):
+        if i < 0: continue
+        out.append(_pair_from_idx(i, s, r))
+    return out
+
+def build_prompt(question: str, ctx: List[Pair]) -> str:
+    lines = ["You are a helpful assistant. Answer the user's question using the given context.",
+             "If the answer is not contained in the context, say you don't know.\n",
+             "Context:"]
+    for p in ctx:
+        lines.append(f"- {p.title or ''} (id={p.doc_id}) [score={p.score:.3f}]")
+    lines.append(f"\nQuestion: {question}\nAnswer:")
+    return "\n".join(lines)
+
+def call_llm(prompt: str) -> str:
+    if client is None:
+        return "(LLM not configured)\n\n" + prompt
+    try:
+        out = client.text_generation(prompt=prompt, max_new_tokens=256, temperature=0.2, do_sample=True)
+        return out.strip()
+    except Exception as e:
+        return f"(LLM error: {e})\n\n" + prompt
+
+def display_gallery(pairs: List[Pair]) -> List[Tuple[str, str]]:
+    # Return [(image_path, caption), ...] for Gradio Gallery. Works if images/ folder is included.
+    items = []
+    for p in pairs:
+        if p.image_path:
+            local_path = os.path.join(DATA_DIR, p.image_path) if not os.path.isabs(p.image_path) else p.image_path
+            if os.path.exists(local_path):
+                caption = f"#{p.rank} — {p.title or ''}\nscore={p.score:.3f}"
+                items.append((local_path, caption))
+    return items
+
+def answer(question: str, image: Optional[Image.Image], topk: int, k_ctx: int, use_image: bool):
+    if use_image and image is not None:
+        top = search_image(image, topk=topk)
+    else:
+        top = search_text(question, topk=topk)
+    ctx = top[:max(1, k_ctx)]
+    prompt = build_prompt(question, ctx)
+    gen = call_llm(prompt)
+    gal = display_gallery(top)
+    return gen, [[p.rank, p.title or "", f"{p.score:.3f}", p.doc_id] for p in top], gal
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🍜 Food RAG Demo (text+image search)")
+    with gr.Row():
+        q = gr.Textbox(label="Question", placeholder="Ask something about a dish, ingredient, etc.")
+        img = gr.Image(label="Optional image", type="pil")
+    with gr.Row():
+        topk = gr.Slider(1, 20, value=10, step=1, label="Top-K search")
+        kctx = gr.Slider(1, 10, value=4, step=1, label="K context to LLM")
+        use_img = gr.Checkbox(label="Use image for search", value=False)
+    btn = gr.Button("Run")
+    out_text = gr.Textbox(label="Answer")
+    out_table = gr.Dataframe(headers=["Rank", "Title", "Score", "Doc ID"], label="Top-K retrieval")
+    out_gallery = gr.Gallery(label="Matches (if images available)", columns=5, height=200)
+    btn.click(answer, inputs=[q, img, topk, kctx, use_img], outputs=[out_text, out_table, out_gallery])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+
+gradio>=4.0.0
+huggingface_hub>=0.24.0
+transformers>=4.43.0
+sentence-transformers>=3.0.0
+torch
+torchvision
+pillow
+faiss-cpu
+numpy