Add app and deps

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/deployment.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PublishConfigData" remoteFilesAllowedToDisappearOnAutoupload="false">
+    <serverData>
+      <paths name="social@95.216.162.70:22 password">
+        <serverdata>
+          <mappings>
+            <mapping local="$PROJECT_DIR$" web="/" />
+          </mappings>
+        </serverdata>
+      </paths>
+      <paths name="social@95.216.162.70:22 password (2)">
+        <serverdata>
+          <mappings>
+            <mapping local="$PROJECT_DIR$" web="/" />
+          </mappings>
+        </serverdata>
+      </paths>
+    </serverData>
+  </component>
+</project>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="DuplicatedCode" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <Languages>
+        <language minSize="122" name="Python" />
+      </Languages>
+    </inspection_tool>
+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="dict.__setitem__" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11" />
+  </component>
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/multimodal-rag-demo.iml" filepath="$PROJECT_DIR$/.idea/multimodal-rag-demo.iml" />
+    </modules>
+  </component>
+</project>

.idea/multimodal-rag-demo.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.11" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

app.py

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+import os, io, gc, json, re, ast
+import numpy as np
+import pandas as pd
+import faiss
+import torch
+import torch.nn.functional as F
+from typing import List, Dict, Any
+from PIL import Image, ImageFilter, ImageOps, ImageEnhance
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# =========================
+# Config (override in Space → Settings → Variables & secrets)
+# =========================
+DATASET_REPO      = os.getenv("DATASET_REPO", "ahm1378/NLP-Project")  # <--- CHANGE to your repo
+CSV_FILE          = os.getenv("CSV_FILE", "final_merged_images.csv")
+E5_INDEX_FILE     = os.getenv("E5_INDEX_FILE", "faiss_e5_rag_v15.ip")
+E5_EMB_FILE       = os.getenv("E5_EMB_FILE", "doc_embeds_e5_rag_v15.npy")
+FUSION_INDEX_FILE = os.getenv("FUSION_INDEX_FILE", "faiss_fusion.ip")
+FUSION_EMB_FILE   = os.getenv("FUSION_EMB_FILE", "fusion_doc_emb.npy")
+FT_HEAD_FILE      = os.getenv("FT_HEAD_FILE", "finetune_clip_fa.pt")  # your finetuned text projection (CLIP space)
+
+HF_TOKEN          = os.getenv("HF_TOKEN", None)  # needed if DATASET_REPO is private
+
+# Models (CPU-friendly defaults; override via env if desired)
+E5_ID       = os.getenv("E5_ID", "intfloat/multilingual-e5-small")
+CLIP_TXT_ID = os.getenv("CLIP_TXT_ID", "sentence-transformers/clip-ViT-B-32-multilingual-v1")
+LLM_ID      = os.getenv("LLM_ID", "Qwen/Qwen2-0.5B-Instruct")   # small enough for free CPU
+
+# Generation defaults (also controllable from UI)
+MAX_NEW_TOKENS_DEFAULT = int(os.getenv("MAX_NEW_TOKENS", "192"))
+TEMPERATURE_DEFAULT    = float(os.getenv("TEMPERATURE", "0.0"))  # deterministic by default on CPU
+TOP_P_DEFAULT          = float(os.getenv("TOP_P", "0.9"))
+TOP_K_DEFAULT          = int(os.getenv("TOP_K", "50"))
+
+# =========================
+# Helpers
+# =========================
+def normalize_digits_months(s: str) -> str:
+    if not isinstance(s, str):
+        s = str(s)
+    trans = str.maketrans("۰۱۲۳۴۵۶۷۸۹٠١٢٣٤٥٦٧٨٩", "01234567890123456789")
+    s = s.translate(trans).replace("\u200c", " ").strip()
+    return s
+
+def _truncate_chars(s: str, limit: int) -> str:
+    return s if (limit is None or len(s) <= limit) else s[:limit] + "…"
+
+def _maybe_hub(file, repo=DATASET_REPO, repo_type="dataset") -> str:
+    # If present locally, use it. Otherwise download from Hub.
+    if os.path.isfile(file):
+        return file
+    return hf_hub_download(repo_id=repo, filename=file, repo_type=repo_type, token=HF_TOKEN)
+
+# =========================
+# Fetch artifacts
+# =========================
+CSV_PATH          = _maybe_hub(CSV_FILE)
+E5_INDEX_PATH     = _maybe_hub(E5_INDEX_FILE)
+# (E5_EMB_PATH not strictly needed at runtime)
+FUSION_INDEX_PATH = _maybe_hub(FUSION_INDEX_FILE) if FUSION_INDEX_FILE else None
+FT_HEAD_PATH      = _maybe_hub(FT_HEAD_FILE) if FT_HEAD_FILE else None
+
+# =========================
+# Load dataframe
+# =========================
+if not os.path.isfile(CSV_PATH):
+    raise FileNotFoundError(f"CSV missing: {CSV_PATH}")
+
+df = pd.read_csv(CSV_PATH)
+
+# Expect columns: 'id', 'bio', 'image_paths_abs' (list or stringified list)
+def first_image(x):
+    if isinstance(x, list) and x:
+        return x[0]
+    if isinstance(x, str) and x.strip():
+        # try JSON list
+        try:
+            lst = json.loads(x)
+            if isinstance(lst, list) and lst:
+                return lst[0]
+        except Exception:
+            # try Python literal list (handles single quotes)
+            try:
+                lst = ast.literal_eval(x)
+                if isinstance(lst, list) and lst:
+                    return lst[0]
+            except Exception:
+                return x  # treat as single path
+    return ""
+
+if "image_paths_abs" in df.columns:
+    df["first_image"] = df["image_paths_abs"].apply(first_image)
+else:
+    df["first_image"] = ""
+
+if "bio" not in df.columns:
+    raise KeyError("Expected 'bio' column in CSV.")
+df["bio"] = df["bio"].astype(str)
+
+# =========================
+# Indices
+# =========================
+if not os.path.isfile(E5_INDEX_PATH):
+    raise FileNotFoundError(f"E5 index not found: {E5_INDEX_PATH}")
+index_e5 = faiss.read_index(E5_INDEX_PATH)
+
+index_fusion = None
+if FUSION_INDEX_PATH and os.path.isfile(FUSION_INDEX_PATH):
+    index_fusion = faiss.read_index(FUSION_INDEX_PATH)
+
+# =========================
+# Models (CPU-only)
+# =========================
+device = "cpu"
+dtype  = torch.float32
+
+# Text retrieval encoder (E5)
+st_e5 = SentenceTransformer(E5_ID, device=device)
+
+# CLIP text encoder (fallback when no FT head)
+st_clip_txt = SentenceTransformer(CLIP_TXT_ID, device=device).eval()
+
+# Optional: finetuned CLIP text projection head (512->512, bias=False)
+mclip = SentenceTransformer(CLIP_TXT_ID, device=device).eval()
+proj_txt = None
+if FT_HEAD_PATH and os.path.isfile(FT_HEAD_PATH):
+    try:
+        proj_txt = torch.nn.Linear(512, 512, bias=False)
+        ckpt = torch.load(FT_HEAD_PATH, map_location="cpu")
+        if "proj_txt" in ckpt:
+            proj_txt.load_state_dict(ckpt["proj_txt"])
+        elif "state_dict" in ckpt:
+            proj_txt.load_state_dict(ckpt["state_dict"])
+        else:
+            raise KeyError("No 'proj_txt' or 'state_dict' key in FT checkpoint.")
+        proj_txt.eval()
+        print("[OK] loaded finetuned projection head:", FT_HEAD_PATH)
+    except Exception as e:
+        print("[WARN] failed to load finetuned head:", e)
+        proj_txt = None
+
+# Lazy CLIP image encoder (only load if user actually does fusion)
+clip_model = None
+clip_preprocess = None
+def _ensure_clip_loaded():
+    global clip_model, clip_preprocess
+    if clip_model is None:
+        import open_clip  # lazy import
+        model, _, preprocess_val = open_clip.create_model_and_transforms(
+            "ViT-B-32", pretrained="laion2b_s34b_b79k", device="cpu"
+        )
+        clip_model = model.eval()
+        clip_preprocess = preprocess_val
+        print("[OK] CLIP ViT-B/32 loaded on CPU")
+
+# LLM (small; CPU-friendly)
+tokenizer = AutoTokenizer.from_pretrained(LLM_ID, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    LLM_ID,
+    torch_dtype=dtype,
+).to("cpu").eval()
+
+# =========================
+# Retrieval helpers
+# =========================
+@torch.no_grad()
+def _encode_query_e5(q: str) -> np.ndarray:
+    qn = "query: " + normalize_digits_months(q)
+    v = st_e5.encode([qn], batch_size=1, convert_to_numpy=True, normalize_embeddings=True)[0]
+    return v.astype("float32")
+
+def _faiss_search(index, q_vec: np.ndarray, k: int):
+    if q_vec.ndim == 1:
+        q_vec = q_vec[None, :]
+    s, I = index.search(q_vec.astype("float32"), k)
+    return list(zip(I[0].tolist(), s[0].tolist()))
+
+def search_text_rag(query_text: str, k: int = 5):
+    q = _encode_query_e5(query_text)
+    return _faiss_search(index_e5, q, k)
+
+# ---- Fusion (CLIP space) ----
+def _jpeg(img, quality=40):
+    buf = io.BytesIO(); img.save(buf, format="JPEG", quality=quality, optimize=False)
+    buf.seek(0); return Image.open(buf).convert("RGB")
+
+def _rand_resized_crop(img, scale=(0.7, 0.9)):
+    w,h = img.size; s = np.random.uniform(*scale)
+    nw,nh = max(1,int(w*s)), max(1,int(h*s))
+    left  = np.random.randint(0, max(1, w-nw))
+    top   = np.random.randint(0, max(1, h-nh))
+    return img.crop((left, top, left+nw, top+nh)).resize((w, h), Image.BICUBIC)
+
+def _color_jitter(img, b=(0.9,1.1), c=(0.9,1.1)):
+    img = ImageOps.autocontrast(img)
+    img = ImageEnhance.Brightness(img).enhance(np.random.uniform(*b))
+    img = ImageEnhance.Contrast(img).enhance(np.random.uniform(*c))
+    return img
+
+def augment_once(img: Image.Image, level="medium"):
+    if level == "mild":
+        img = _rand_resized_crop(img, (0.85, 0.95)); img = _jpeg(img, 60)
+    elif level == "medium":
+        img = _rand_resized_crop(img, (0.7, 0.9))
+        img = img.filter(ImageFilter.GaussianBlur(1.0))
+        img = _color_jitter(img, (0.9,1.1), (0.9,1.1)); img = _jpeg(img, 40)
+    else:
+        img = _rand_resized_crop(img, (0.6, 0.8))
+        img = img.filter(ImageFilter.GaussianBlur(1.2)); img = _jpeg(img, 30)
+    return img
+
+@torch.no_grad()
+def _encode_pil_clip(img: Image.Image) -> np.ndarray:
+    _ensure_clip_loaded()
+    t = clip_preprocess(img).unsqueeze(0)
+    feat = clip_model.encode_image(t)
+    feat = F.normalize(feat.float(), dim=-1)
+    return feat.cpu().numpy().astype("float32")  # (1,512)
+
+@torch.no_grad()
+def _encode_query_text_clipspace(q: str) -> np.ndarray:
+    qn = normalize_digits_months(q)
+    if proj_txt is not None:
+        # mclip raw → proj → normalize
+        t = torch.tensor(
+            mclip.encode([qn], convert_to_numpy=True, normalize_embeddings=False),
+            dtype=torch.float32
+        )
+        t = proj_txt(t)
+        t = F.normalize(t, dim=-1).cpu().numpy().astype("float32")
+        return t
+    else:
+        # fallback: CLIP multilingual text encoder (already normalized)
+        t = st_clip_txt.encode([qn], batch_size=1, convert_to_numpy=True, normalize_embeddings=True)
+        return t.astype("float32")
+
+@torch.no_grad()
+def make_query_embed(query_text: str,
+                     image: Image.Image = None,
+                     alpha_q: float = 0.7,
+                     use_aug: bool = True,
+                     n_aug: int = 3) -> np.ndarray:
+    qt = _encode_query_text_clipspace(query_text)  # (1,512)
+    qi = None
+    if image is not None:
+        if clip_model is None:  # ensure loaded only if needed
+            _ensure_clip_loaded()
+        if use_aug:
+            feats = [ _encode_pil_clip(augment_once(image, "medium")) for _ in range(max(1,int(n_aug))) ]
+            qi = np.mean(np.vstack(feats), axis=0, keepdims=True).astype("float32")
+        else:
+            qi = _encode_pil_clip(image)
+    if qi is not None:
+        qv = torch.from_numpy(alpha_q*qt + (1.0-alpha_q)*qi)
+        qv = F.normalize(qv, dim=-1).cpu().numpy().astype("float32")
+        return qv
+    return qt
+
+def search_fusion(query_text: str, image: Image.Image, k: int = 5, alpha_q: float = 0.7):
+    if index_fusion is None:
+        raise RuntimeError("Fusion index not available (upload FUSION_INDEX_FILE to dataset repo).")
+    qv = make_query_embed(query_text, image=image, alpha_q=alpha_q, use_aug=True, n_aug=3)
+    return _faiss_search(index_fusion, qv, k)
+
+# =========================
+# RAG + LLM
+# =========================
+def retrieve_context_auto(question: str, k: int = 5, image: Image.Image = None) -> Dict[str, Any]:
+    q = normalize_digits_months(question)
+    if (image is not None):
+        route = "fusion"
+        try:
+            hits = search_fusion(q, image=image, k=k)
+        except Exception as e:
+            route = "text_e5"  # graceful fallback
+            hits = search_text_rag(q, k=k)
+    else:
+        route = "text_e5"
+        hits = search_text_rag(q, k=k)
+
+    ctxs = []
+    for idx, score in hits:
+        if 0 <= idx < len(df):
+            row = df.iloc[idx]
+            ctxs.append({"index": int(idx), "id": row.get("id", idx), "score": float(score), "bio": str(row["bio"])})
+    return {"route": route, "contexts": ctxs}
+
+def build_prompt(question: str, contexts: List[Dict[str, Any]], lang="fa", max_chars=5000) -> str:
+    sys_fa = "تو یک دستیار پاسخ‌گو هستی که فقط بر اساس متن‌های داده‌شده پاسخ می‌دهی. اگر پاسخی در متن‌ها نبود، صادقانه بگو «در متن‌های بازیابی‌شده پاسخی پیدا نشد.»"
+    sys_en = "You are a helpful assistant. Answer only using retrieved passages. If not found, say 'No answer found in retrieved passages.'"
+    system_text = sys_fa if lang == "fa" else sys_en
+
+    parts = []
+    for i, c in enumerate(contexts, 1):
+        bi = c["bio"].strip()
+        if bi:
+            parts.append(f"[{i}] {bi}")
+    joined = _truncate_chars("\n\n".join(parts), max_chars)
+
+    user = (f"سؤال: {question}\n\nمتون بازیابی‌شده:\n{joined}\n\n"
+            f"فقط با اتکا به متون بالا پاسخ بده و منابع را با [1], [2], ... ارجاع بده."
+           ) if lang == "fa" else (
+            f"Question: {question}\n\nRetrieved passages:\n{joined}\n\n"
+            f"Answer only using the passages, cite sources as [1], [2], ..."
+           )
+    msgs = [{"role": "system", "content": system_text},
+            {"role": "user", "content": user}]
+    return tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+
+@torch.inference_mode()
+def llm_generate(prompt: str,
+                 max_new_tokens=MAX_NEW_TOKENS_DEFAULT,
+                 temperature=TEMPERATURE_DEFAULT,
+                 top_p=TOP_P_DEFAULT,
+                 top_k=TOP_K_DEFAULT,
+                 do_sample=False) -> str:
+    inputs = tokenizer(prompt, return_tensors="pt")
+    out = model.generate(
+        **inputs,
+        max_new_tokens=int(max_new_tokens),
+        do_sample=bool(do_sample),
+        temperature=float(temperature),
+        top_p=float(top_p),
+        top_k=int(top_k),
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    text = tokenizer.decode(out[0], skip_special_tokens=True)
+    if text.startswith(prompt):
+        text = text[len(prompt):]
+    return text.strip()
+
+# ---- MCQ helpers ----
+def build_mcq_prompt(question: str, options: List[str], contexts: List[Dict[str, Any]], lang="fa", max_chars=5000) -> str:
+    sys_fa = "تو یک دستیار پاسخ‌گو هستی که فقط بر اساس متن‌های داده‌شده پاسخ می‌دهی."
+    sys_en = "You are a helpful assistant. Answer only using the retrieved passages."
+    system_text = sys_fa if lang == "fa" else sys_en
+
+    parts = []
+    for i, c in enumerate(contexts, 1):
+        bi = c["bio"].strip()
+        if bi:
+            parts.append(f"[{i}] {bi}")
+    joined = _truncate_chars("\n\n".join(parts), max_chars)
+
+    labels = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+    opts_str = "\n".join([f"{labels[i]}) {o}" for i, o in enumerate(options)])
+
+    if lang == "fa":
+        user = (
+            f"سؤال: {question}\n\nگزینه‌ها:\n{opts_str}\n\nمتون بازیابی‌شده:\n{joined}\n\n"
+            'فقط براساس متون بالا پاسخ بده. دقیقاً در این قالب برگردان:\n{"answer_index": X, "reason": "…"}'
+        )
+    else:
+        user = (
+            f"Question: {question}\n\nOptions:\n{opts_str}\n\nRetrieved:\n{joined}\n\n"
+            'Answer strictly based on passages. Return exactly:\n{"answer_index": X, "reason": "..."}'
+        )
+    msgs = [{"role": "system", "content": system_text},
+            {"role": "user", "content": user}]
+    return tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+
+def parse_mcq_output(text: str, n: int) -> Dict[str, Any]:
+    m = re.search(r'{"\s*answer_index"\s*:\s*([0-9]+)\s*,\s*"reason"\s*:\s*"(.*?)"}', text, re.S)
+    if m:
+        idx = int(m.group(1)); reason = m.group(2).strip()
+        if 0 <= idx < n:
+            return {"answer_index": idx, "reason": reason}
+    letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+    m2 = re.search(r'\b([A-D])\b', text, re.I)
+    if m2:
+        idx = letters.index(m2.group(1).upper())
+        if idx < n:
+            return {"answer_index": idx, "reason": text.strip()}
+    m3 = re.search(r'\b([1-9])\b', text)
+    if m3:
+        idx = int(m3.group(1)) - 1
+        if 0 <= idx < n:
+            return {"answer_index": idx, "reason": text.strip()}
+    return {"answer_index": None, "reason": text.strip()}
+
+# =========================
+# Gradio UI
+# =========================
+def ui_answer(question, image, topk, max_tokens, temperature, top_p, top_k):
+    if not question or not question.strip():
+        return "Please enter a question.", [], ""
+    # Retrieve
+    ret = retrieve_context_auto(question, k=int(topk), image=image)
+    prompt = build_prompt(question, ret["contexts"], lang="fa", max_chars=5000)
+    ans = llm_generate(prompt, max_new_tokens=int(max_tokens),
+                       temperature=float(temperature), top_p=float(top_p),
+                       top_k=int(top_k), do_sample=False)
+    # Sources
+    rows = []
+    for i, c in enumerate(ret["contexts"], 1):
+        snip = c["bio"][:180] + ("…" if len(c["bio"]) > 180 else "")
+        rows.append([i, c["id"], round(c["score"], 4), snip])
+    return ans, rows, ret["route"]
+
+def ui_mcq(question, options_txt, image, topk, max_tokens, temperature, top_p, top_k):
+    opts = [o.strip() for o in (options_txt or "").splitlines() if o.strip()]
+    if not question or len(opts) < 2:
+        return "Provide a question and at least 2 options.", "", [], ""
+    ret = retrieve_context_auto(question, k=int(topk), image=image)
+    prompt = build_mcq_prompt(question, opts, ret["contexts"], lang="fa", max_chars=5000)
+    out = llm_generate(prompt, max_new_tokens=int(max_tokens),
+                       temperature=float(temperature), top_p=float(top_p),
+                       top_k=int(top_k), do_sample=False)
+    parsed = parse_mcq_output(out, len(opts))
+    pred = parsed["answer_index"]
+    pred_text = (opts[pred] if (pred is not None and 0 <= pred < len(opts)) else "N/A")
+    rows = []
+    for i, c in enumerate(ret["contexts"], 1):
+        snip = c["bio"][:180] + ("…" if len(c["bio"]) > 180 else "")
+        rows.append([i, c["id"], round(c["score"], 4), snip])
+    result = f"Pred: index={pred}  text={pred_text}\nReason: {parsed['reason']}"
+    return result, out, rows, ret["route"]
+
+with gr.Blocks(title="Multimodal RAG (CPU) • E5 + CLIP Fusion + Qwen 0.5B") as demo:
+    gr.Markdown("### Free-tier CPU demo: text RAG (E5) + optional fusion (CLIP) → Qwen 0.5B")
+    with gr.Tab("Ask"):
+        with gr.Row():
+            q = gr.Textbox(label="Question", placeholder="سؤال خود را بنویسید…", lines=3)
+            img = gr.Image(type="pil", label="Optional image (fusion if provided)")
+        with gr.Row():
+            topk = gr.Slider(1, 20, value=5, step=1, label="Top-K retrieve")
+            max_tokens = gr.Slider(32, 1024, value=MAX_NEW_TOKENS_DEFAULT, step=16, label="Max new tokens")
+        with gr.Row():
+            temperature = gr.Slider(0.0, 1.5, value=TEMPERATURE_DEFAULT, step=0.1, label="Temperature")
+            top_p = gr.Slider(0.1, 1.0, value=TOP_P_DEFAULT, step=0.05, label="Top-p")
+            top_k = gr.Slider(1, 100, value=TOP_K_DEFAULT, step=1, label="Top-k")
+        btn = gr.Button("Answer")
+        ans = gr.Textbox(label="Answer", lines=8)
+        route = gr.Textbox(label="Route used (text_e5 or fusion)")
+        table = gr.Dataframe(headers=["#", "id", "score", "snippet"], interactive=False)
+        btn.click(ui_answer, [q, img, topk, max_tokens, temperature, top_p, top_k], [ans, table, route])
+
+    with gr.Tab("MCQ"):
+        with gr.Row():
+            q_mcq = gr.Textbox(label="Question", lines=3)
+            opts_mcq = gr.Textbox(label="Options (one per line)", lines=6)
+        img_mcq = gr.Image(type="pil", label="Optional image (fusion if provided)")
+        with gr.Row():
+            topk2 = gr.Slider(1, 20, value=5, step=1, label="Top-K retrieve")
+            max_tokens2 = gr.Slider(32, 1024, value=MAX_NEW_TOKENS_DEFAULT, step=16, label="Max new tokens")
+        with gr.Row():
+            temperature2 = gr.Slider(0.0, 1.5, value=TEMPERATURE_DEFAULT, step=0.1, label="Temperature")
+            top_p2 = gr.Slider(0.1, 1.0, value=TOP_P_DEFAULT, step=0.05, label="Top-p")
+            top_k2 = gr.Slider(1, 100, value=TOP_K_DEFAULT, step=1, label="Top-k")
+        btn2 = gr.Button("Answer MCQ")
+        result = gr.Textbox(label="Prediction")
+        raw = gr.Textbox(label="Raw LLM output", lines=6)
+        route2 = gr.Textbox(label="Route used")
+        table2 = gr.Dataframe(headers=["#", "id", "score", "snippet"], interactive=False)
+        btn2.click(ui_mcq, [q_mcq, opts_mcq, img_mcq, topk2, max_tokens2, temperature2, top_p2, top_k2],
+                   [result, raw, table2, route2])
+
+demo.launch()

requirements.txt

@@ -0,0 +1,11 @@
+gradio>=4.29.0
+huggingface_hub>=0.23.0
+pandas
+numpy
+pillow
+faiss-cpu
+sentence-transformers>=2.5.0
+transformers>=4.41.0
+open_clip_torch>=2.24.0
+tqdm
+torch