Update app.py

app.py

@@ -1,4 +1,3 @@
-# app.py
 import io
 import os
 import faiss
@@ -12,16 +11,14 @@ from transformers import BlipProcessor, BlipForConditionalGeneration
 import torch
 
 APP_TITLE = "Image → Hadith Similarity (FAISS)"
-INDEX_PATH = "faiss.index"              # put your file here (git-lfs likely)
-META_PATH  = "hadith_meta.parquet"      # hadithID, text_ar, text_en, etc.
+INDEX_PATH = "hadith_semantic.faiss"
+META_PATH  = "hadith_meta.parquet"
 
-# Models
 SBERT_NAME = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
 BLIP_NAME  = "Salesforce/blip-image-captioning-base"
 
 app = FastAPI(title=APP_TITLE)
 
-# -------- Load once --------
 index = None
 meta = None
 sbert = None
@@ -29,32 +26,49 @@ blip_processor = None
 blip_model = None
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+
 @app.on_event("startup")
 def load_all():
     global index, meta, sbert, blip_processor, blip_model
 
-    assert os.path.exists(INDEX_PATH), f"Missing {INDEX_PATH}"
-    assert os.path.exists(META_PATH), f"Missing {META_PATH}"
+    if not os.path.exists(INDEX_PATH):
+        raise RuntimeError(f"Missing FAISS index: {INDEX_PATH}")
+    if not os.path.exists(META_PATH):
+        raise RuntimeError(f"Missing meta file: {META_PATH}")
 
     index = faiss.read_index(INDEX_PATH)
     meta = pd.read_parquet(META_PATH)
 
+    # Basic sanity check
+    if len(meta) != index.ntotal:
+        # Not always fatal, but usually means mismatch between index build order and meta rows.
+        print(f"[WARN] meta rows ({len(meta)}) != index.ntotal ({index.ntotal}). "
+              f"Results will use row positions; ensure they align.")
+
     sbert = SentenceTransformer(SBERT_NAME)
 
     blip_processor = BlipProcessor.from_pretrained(BLIP_NAME)
     blip_model = BlipForConditionalGeneration.from_pretrained(BLIP_NAME).to(device)
     blip_model.eval()
 
+
 @app.get("/health")
 def health():
+    # Try infer dim from index when possible
+    dim = getattr(index, "d", None)
     return {
         "ok": True,
+        "index_file": INDEX_PATH,
+        "meta_file": META_PATH,
         "index_ntotal": int(index.ntotal),
-        "model": SBERT_NAME,
+        "meta_rows": int(len(meta)),
+        "dim": int(dim) if dim is not None else None,
+        "text_model": SBERT_NAME,
         "caption_model": BLIP_NAME,
-        "dim": 384
+        "device": device
     }
 
+
 def caption_image(pil_img: Image.Image) -> str:
     inputs = blip_processor(images=pil_img, return_tensors="pt").to(device)
     with torch.no_grad():
@@ -62,15 +76,25 @@ def caption_image(pil_img: Image.Image) -> str:
     cap = blip_processor.decode(out[0], skip_special_tokens=True)
     return cap.strip()
 
+
 def embed_text(text: str) -> np.ndarray:
+    # normalize_embeddings => cosine via inner-product
     v = sbert.encode([text], normalize_embeddings=True)
     return v.astype("float32")
 
+
+def pick_col(row, candidates, default=""):
+    for c in candidates:
+        if c in row and pd.notna(row[c]):
+            return row[c]
+    return default
+
+
 @app.post("/search_image")
 async def search_image(
     file: UploadFile = File(...),
     k: int = Query(10, ge=1, le=50),
-    format: str = Query("json")
+    format: str = Query("json"),
 ):
     data = await file.read()
     pil = Image.open(io.BytesIO(data)).convert("RGB")
@@ -78,30 +102,50 @@ async def search_image(
     cap = caption_image(pil)
     qvec = embed_text(cap)
 
-    # cosine via normalized + inner product
     scores, idxs = index.search(qvec, k)
 
     results = []
     for rank, (i, s) in enumerate(zip(idxs[0].tolist(), scores[0].tolist()), start=1):
-        row = meta.iloc[i]
+        if i < 0 or i >= len(meta):
+            continue
+
+        row = meta.iloc[i].to_dict()
+
+        hadith_id = pick_col(row, ["hadithID", "hadith_id", "id", "doc_id"], default=i)
+        text_ar   = pick_col(row, ["text_ar", "arabic", "ar", "text"], default="")
+        text_en   = pick_col(row, ["text_en", "english", "en"], default="")
+        source    = pick_col(row, ["source", "book", "collection"], default="")
+
         results.append({
             "rank": rank,
             "score": float(s),
-            "hadithID": int(row.get("hadithID", i)),
-            "text_ar": row.get("text_ar", ""),
-            "text_en": row.get("text_en", ""),
-            "source": row.get("source", "")
+            "hadithID": int(hadith_id) if str(hadith_id).isdigit() else str(hadith_id),
+            "text_ar": str(text_ar),
+            "text_en": str(text_en),
+            "source": str(source),
         })
 
     payload = {"caption": cap, "k": k, "results": results}
 
     if format == "html":
-        # minimal HTML (you can beautify)
         items = "\n".join([
-            f"<li><b>{r['rank']}</b> score={r['score']:.3f} — hadithID={r['hadithID']}<br>{r['text_ar']}</li>"
+            f"<li><b>#{r['rank']}</b> score={r['score']:.3f} — hadithID={r['hadithID']}<br>"
+            f"<div style='font-family: system-ui; direction: rtl; font-size: 18px'>{r['text_ar']}</div>"
+            f"<div style='color:#666; margin-top:6px'>{r['text_en']}</div>"
+            f"<div style='color:#999; margin-top:6px'>source: {r['source']}</div>"
+            f"</li>"
             for r in results
         ])
-        html = f"<h3>Caption</h3><p>{cap}</p><h3>Results</h3><ol>{items}</ol>"
+        html = f"""
+        <html>
+        <body style="margin:18px; font-family: system-ui">
+          <h3>Caption</h3>
+          <p>{cap}</p>
+          <h3>Top Results</h3>
+          <ol>{items}</ol>
+        </body>
+        </html>
+        """
         return HTMLResponse(html)
 
     return JSONResponse(payload)