Initial deploy FastAPI backend to Hugging Face Space

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+retrieval/docs_no_stop.json filter=lfs diff=lfs merge=lfs -text
+retrieval/ind/*.seg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+.venv/
+venv/
+env/

Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.11
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

@@ -0,0 +1,502 @@
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import List, Dict, Optional
+import pandas as pd
+from whoosh import index, qparser
+from whoosh.qparser import MultifieldParser
+from whoosh.scoring import BM25F
+from whoosh.index import open_dir
+import os
+import nltk
+from nltk import sent_tokenize
+import re
+import unicodedata
+from pyvi import ViTokenizer
+
+nltk.download('punkt_tab')
+nltk.download('stopwords')
+
+app = FastAPI(title="Document Search API")
+
+# CORS configuration
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=[
+        "http://localhost:3000",  # Next.js development
+        "http://localhost:3001",  # Alternative port
+        "https://blue-information-retrieval.vercel.app", # Production frontend URL
+    ],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Configuration
+INDEX_DIR = "./retrieval/ind"
+META_CSV = "./retrieval/final_document_tfidf_pagerank.csv"
+DATA_CLEAN_DIR = "./retrieval/data_clean"
+IMAGE_CSV = "./retrieval/docs_with_images.csv"  # File CSV chứa URL ảnh
+STOPWORDS_PATH = "./retrieval/vietnamese-stopwords-dash.txt"
+
+# Global variables
+ix = None
+meta_df = None
+image_df = None  # DataFrame chứa mapping doc_id -> image_url
+docs_cache = {}
+pagerank_dict = {}
+vi_stopwords = None
+
+
+class SearchRequest(BaseModel):
+    query: str
+    model: str = "bm25"
+    limit: int = 100
+    B: float = 0.75
+    K1: float = 1.2
+    title_boost: float = 1.5
+    content_boost: float = 1.0
+
+
+class SearchResult(BaseModel):
+    doc_id: str
+    title: str
+    url: str
+    snippet: str
+    score: float
+    relevance_percentage: float
+    image_url: Optional[str] = None
+    pagerank_score: Optional[float] = None
+    total_words: Optional[int] = None
+    unique_words: Optional[int] = None
+    top_words: Optional[str] = None
+    top_tfidf: Optional[str] = None
+    avg_tfidf: Optional[float] = None
+    final_score: Optional[float] = None
+
+
+def split_sentences(text):
+    return sent_tokenize(text)
+
+
+def tokenize_vi_sentence_level(text: str) -> list[str]:
+    sentences = sent_tokenize(text)
+    tokens = []
+    for sent in sentences:
+        sent = sent.strip()
+        if not sent:
+            continue
+        sent_tokens = ViTokenizer.tokenize(sent)
+        tokens.extend(sent_tokens.split())
+    return tokens
+
+
+VI_TOKEN_REGEX = re.compile(
+    r"[a-zàáạảãâầấậẩẫăằắặẳẵ"
+    r"èéẹẻẽêềếệểễ"
+    r"ìíịỉĩ"
+    r"òóọỏõôồốộổỗơờớợởỡ"
+    r"ùúụủũưừứựửữ"
+    r"ỳýỵỷỹđ0-9_]+$"
+)
+
+
+def is_valid_vi_token(token: str) -> bool:
+    return bool(VI_TOKEN_REGEX.fullmatch(token))
+
+
+def load_stopwords(path):
+    with open(path, "r", encoding="utf-8") as f:
+        stopwords = set(line.strip().lower() for line in f if line.strip())
+    return stopwords
+
+
+def clean_text(text):
+    if text is None:
+        return ""
+    text = unicodedata.normalize("NFC", text)
+    text = re.sub(r"http\S+|www\S+", "", text)
+    text = re.sub(r"[.,!?]+", " ", text)
+    text = re.sub(r"\s+", " ", text).strip()
+    return text
+
+
+def preprocess_query(query: str, stopwords: set[str] | None = None) -> str:
+    query = clean_text(query)
+    tokens = tokenize_vi_sentence_level(query)
+    processed_tokens = []
+    for tok in tokens:
+        tok = tok.lower()
+        if not is_valid_vi_token(tok):
+            continue
+        if tok.isnumeric():
+            continue
+        if stopwords and tok in stopwords:
+            continue
+        processed_tokens.append(tok)
+    return " ".join(processed_tokens)
+
+
+def load_pagerank(meta_csv: str) -> Dict[str, float]:
+    """Load PageRank scores from CSV"""
+    try:
+        df = pd.read_csv(meta_csv)
+        if 'pagerank' in df.columns and 'id' in df.columns:
+            return dict(zip(df['id'].astype(str), df['pagerank']))
+        return {}
+    except Exception as e:
+        print(f"Warning: Could not load PageRank scores: {e}")
+        return {}
+
+
+def load_images_csv(image_csv: str) -> pd.DataFrame:
+    """Load image URLs from CSV file"""
+    try:
+        if not os.path.exists(image_csv):
+            print(f"⚠️ Image CSV not found: {image_csv}")
+            return pd.DataFrame(columns=['doc_id', 'image_url'])
+        
+        df = pd.read_csv(image_csv)
+        
+        # Đảm bảo có cả 2 cột cần thiết
+        if 'doc_id' not in df.columns or 'image_url' not in df.columns:
+            print("⚠️ Image CSV missing required columns: doc_id, image_url")
+            return pd.DataFrame(columns=['doc_id', 'image_url'])
+        
+        # Convert doc_id to string để dễ mapping
+        df['doc_id'] = df['doc_id'].astype(str)
+        
+        # Loại bỏ các dòng có image_url null/empty
+        df = df[df['image_url'].notna() & (df['image_url'] != '')]
+        
+        print(f"✅ Loaded {len(df)} image URLs from CSV")
+        return df
+        
+    except Exception as e:
+        print(f"❌ Error loading image CSV: {e}")
+        return pd.DataFrame(columns=['doc_id', 'image_url'])
+
+
+def get_image_url(doc_id: str) -> Optional[str]:
+    """Get image URL for a document from CSV"""
+    global image_df
+    
+    if image_df is None or image_df.empty:
+        return None
+    
+    try:
+        # Tìm image_url theo doc_id
+        result = image_df[image_df['doc_id'] == str(doc_id)]
+        
+        if not result.empty:
+            image_url = result.iloc[0]['image_url']
+            # Kiểm tra URL hợp lệ
+            if pd.notna(image_url) and str(image_url).strip() != '':
+                return str(image_url)
+        
+        return None
+        
+    except Exception as e:
+        print(f"Error getting image URL for doc {doc_id}: {e}")
+        return None
+
+
+def load_document_content(doc_id: str) -> str:
+    """Load document content from data_clean directory"""
+    global docs_cache
+    
+    if doc_id in docs_cache:
+        return docs_cache[doc_id]
+    
+    try:
+        file_path = os.path.join(DATA_CLEAN_DIR, f"{doc_id}.txt")
+        
+        if not os.path.exists(file_path):
+            print(f"Warning: File not found: {file_path}")
+            return ""
+        
+        with open(file_path, 'r', encoding='utf-8') as f:
+            content = f.read()
+        
+        docs_cache[doc_id] = content
+        return content
+        
+    except Exception as e:
+        print(f"Error loading document {doc_id}: {e}")
+        return ""
+
+
+def get_snippet(doc_id: str, query_terms: List[str], max_length: int = 200) -> str:
+    """Extract relevant snippet from document based on query terms"""
+    content = load_document_content(doc_id)
+    
+    if not content or content.strip() == "":
+        return "Không có nội dung xem trước."
+    
+    try:
+        content_lower = content.lower()
+        query_lower = [term.lower() for term in query_terms if term.strip()]
+        
+        if not query_lower:
+            words = content.split()
+            snippet_words = words[:30]
+            snippet = ' '.join(snippet_words)
+            if len(snippet) > max_length:
+                snippet = snippet[:max_length] + "..."
+            return snippet
+        
+        best_pos = 0
+        max_matches = 0
+        
+        words = content.split()
+        window_size = min(30, len(words))
+        
+        for i in range(max(1, len(words) - window_size + 1)):
+            window = ' '.join(words[i:i+window_size]).lower()
+            matches = sum(1 for term in query_lower if term in window)
+            if matches > max_matches:
+                max_matches = matches
+                best_pos = i
+        
+        snippet_words = words[best_pos:best_pos+window_size]
+        snippet = ' '.join(snippet_words)
+        
+        if len(snippet) > max_length:
+            snippet = snippet[:max_length] + "..."
+        
+        if best_pos > 0:
+            snippet = "..." + snippet
+        
+        return snippet
+        
+    except Exception as e:
+        print(f"Error generating snippet for doc {doc_id}: {e}")
+        return "Lỗi khi tạo đoạn trích."
+
+
+def bm25_search(ix, query_str: str, vi_stopwords: set[str] | None = None, top_k: int = 100, 
+                B: float = 0.75, K1: float = 1.2,
+                title_boost: float = 1.5, content_boost: float = 1.0) -> Dict[str, float]:
+    """BM25 search with title and content fields"""
+    query_str = preprocess_query(query_str, stopwords=vi_stopwords)
+    results = {}
+    weighting = BM25F(B=B, K1=K1)
+    
+    with ix.searcher(weighting=weighting) as searcher:
+        field_boosts = {
+            "title": title_boost,
+            "content": content_boost
+        }
+        
+        parser = MultifieldParser(
+            ["title", "content"],
+            schema=ix.schema,
+            fieldboosts=field_boosts,
+            group=qparser.OrGroup
+        )
+        
+        q = parser.parse(query_str)
+        hits = searcher.search(q, limit=top_k)
+        
+        for hit in hits:
+            results[str(hit["docid"])] = float(hit.score)
+    
+    return results
+
+
+def normalize_scores(scores: Dict[str, float]) -> Dict[str, float]:
+    """Normalize scores to percentage (0-100)"""
+    if not scores:
+        return {}
+    max_score = max(scores.values())
+    if max_score == 0:
+        return {k: 0.0 for k in scores}
+    return {k: (v / max_score) * 100 for k, v in scores.items()}
+
+
+@app.on_event("startup")
+async def startup_event():
+    """Initialize index and load data on startup"""
+    global ix, meta_df, image_df, pagerank_dict, vi_stopwords
+    
+    try:
+        # Load Whoosh index
+        if os.path.exists(INDEX_DIR):
+            ix = open_dir(INDEX_DIR)
+            print("✅ Loaded Whoosh index")
+        else:
+            print("❌ Index directory not found:", INDEX_DIR)
+        
+        # Load metadata
+        if os.path.exists(META_CSV):
+            meta_df = pd.read_csv(META_CSV)
+            print(f"✅ Loaded {len(meta_df)} documents metadata")
+        else:
+            print("❌ Metadata CSV not found:", META_CSV)
+        
+        # Load image CSV
+        image_df = load_images_csv(IMAGE_CSV)
+        
+        # Check data_clean directory
+        if os.path.exists(DATA_CLEAN_DIR):
+            num_files = len([f for f in os.listdir(DATA_CLEAN_DIR) if f.endswith('.txt')])
+            print(f"✅ Found {num_files} text files in {DATA_CLEAN_DIR}")
+        else:
+            print("❌ Data clean directory not found:", DATA_CLEAN_DIR)
+        
+        # Load PageRank scores
+        pagerank_dict = load_pagerank(META_CSV)
+        print(f"✅ Loaded PageRank scores for {len(pagerank_dict)} documents")
+        
+        # Load StopWords
+        if os.path.exists(STOPWORDS_PATH):
+            vi_stopwords = load_stopwords(STOPWORDS_PATH)
+            print(f"✅ Loaded {len(vi_stopwords)} Vietnamese stopwords")
+        else:
+            print("⚠️ Stopwords file not found, continuing without stopwords")
+            vi_stopwords = set()
+        
+    except Exception as e:
+        print(f"❌ Error during startup: {e}")
+        raise
+
+
+@app.get("/")
+async def root():
+    """Health check endpoint"""
+    return {
+        "status": "ok",
+        "message": "Document Search API is running",
+        "total_documents": len(meta_df) if meta_df is not None else 0,
+        "total_images": len(image_df) if image_df is not None else 0,
+        "data_clean_dir": DATA_CLEAN_DIR,
+        "image_csv": IMAGE_CSV,
+        "index_dir": INDEX_DIR
+    }
+
+
+@app.post("/search", response_model=List[SearchResult])
+async def search(request: SearchRequest):
+    """Search documents using BM25 algorithm"""
+    if not request.query.strip():
+        raise HTTPException(status_code=400, detail="Query cannot be empty")
+    
+    if ix is None or meta_df is None:
+        raise HTTPException(status_code=503, detail="Search index not initialized")
+    
+    try:
+        raw_scores = bm25_search(
+            ix, 
+            request.query,
+            vi_stopwords=vi_stopwords,
+            top_k=request.limit,
+            B=request.B,
+            K1=request.K1,
+            title_boost=request.title_boost,
+            content_boost=request.content_boost
+        )
+        
+        if not raw_scores:
+            return []
+        
+        normalized_scores = normalize_scores(raw_scores)
+        query_terms = request.query.split()
+        
+        results = []
+        for doc_id, score in raw_scores.items():
+            doc_row = meta_df[meta_df['id'].astype(str) == doc_id]
+            
+            if doc_row.empty:
+                continue
+            
+            doc_info = doc_row.iloc[0]
+            snippet = get_snippet(doc_id, query_terms, max_length=300)
+            pr_score = pagerank_dict.get(doc_id)
+            
+            # Lấy image_url từ CSV
+            image_url = get_image_url(doc_id)
+            
+            result = SearchResult(
+                doc_id=doc_id,
+                title=str(doc_info.get('title', 'Untitled')),
+                url=str(doc_info.get('url', '')),
+                snippet=snippet,
+                score=round(score, 4),
+                relevance_percentage=round(normalized_scores[doc_id], 2),
+                image_url=image_url,
+                pagerank_score=round(pr_score, 6) if pr_score else None,
+                total_words=int(doc_info.get('total_words', 0)) if pd.notna(doc_info.get('total_words')) else None,
+                unique_words=int(doc_info.get('unique_words', 0)) if pd.notna(doc_info.get('unique_words')) else None,
+                top_words=str(doc_info.get('top_words', '')) if pd.notna(doc_info.get('top_words')) else None,
+                top_tfidf=str(doc_info.get('top_tfidf', '')) if pd.notna(doc_info.get('top_tfidf')) else None,
+                avg_tfidf=round(float(doc_info.get('avg_tfidf', 0)), 6) if pd.notna(doc_info.get('avg_tfidf')) else None,
+                final_score=round(float(doc_info.get('final_score', 0)), 6) if pd.notna(doc_info.get('final_score')) else None
+            )
+            
+            results.append(result)
+        
+        results.sort(key=lambda x: x.score, reverse=True)
+        return results[:request.limit]
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Search error: {str(e)}")
+
+
+@app.get("/stats")
+async def get_stats():
+    """Get statistics about the search index"""
+    if meta_df is None:
+        raise HTTPException(status_code=503, detail="Index not initialized")
+    
+    num_cached_docs = len(docs_cache)
+    num_txt_files = 0
+    
+    if os.path.exists(DATA_CLEAN_DIR):
+        num_txt_files = len([f for f in os.listdir(DATA_CLEAN_DIR) if f.endswith('.txt')])
+    
+    return {
+        "total_documents": len(meta_df),
+        "total_images": len(image_df) if image_df is not None else 0,
+        "text_files_available": num_txt_files,
+        "cached_documents": num_cached_docs,
+        "pagerank_scores": len(pagerank_dict),
+        "index_directory": INDEX_DIR,
+        "data_clean_directory": DATA_CLEAN_DIR,
+        "image_csv": IMAGE_CSV
+    }
+
+
+@app.get("/document/{doc_id}")
+async def get_document(doc_id: str):
+    """Get full document content and metadata"""
+    if meta_df is None:
+        raise HTTPException(status_code=503, detail="Index not initialized")
+    
+    doc_row = meta_df[meta_df['id'].astype(str) == doc_id]
+    
+    if doc_row.empty:
+        raise HTTPException(status_code=404, detail="Document not found")
+    
+    doc_info = doc_row.iloc[0]
+    content = load_document_content(doc_id)
+    image_url = get_image_url(doc_id)
+    
+    return {
+        "doc_id": doc_id,
+        "title": str(doc_info.get('title', 'Untitled')),
+        "url": str(doc_info.get('url', '')),
+        "content": content,
+        "image_url": image_url,
+        "pagerank": float(doc_info.get('pagerank', 0)) if pd.notna(doc_info.get('pagerank')) else None,
+        "total_words": int(doc_info.get('total_words', 0)) if pd.notna(doc_info.get('total_words')) else None,
+        "unique_words": int(doc_info.get('unique_words', 0)) if pd.notna(doc_info.get('unique_words')) else None,
+        "top_words": str(doc_info.get('top_words', '')) if pd.notna(doc_info.get('top_words')) else None,
+        "top_tfidf": str(doc_info.get('top_tfidf', '')) if pd.notna(doc_info.get('top_tfidf')) else None,
+        "avg_tfidf": float(doc_info.get('avg_tfidf', 0)) if pd.notna(doc_info.get('avg_tfidf')) else None,
+        "final_score": float(doc_info.get('final_score', 0)) if pd.notna(doc_info.get('final_score')) else None
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

@@ -0,0 +1,123 @@
+alembic==1.17.2
+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.12.0
+asttokens==3.0.0
+attrs==25.4.0
+bcrypt==5.0.0
+beautifulsoup4==4.14.2
+certifi==2025.10.5
+cffi==2.0.0
+charset-normalizer==3.4.4
+click==8.3.0
+colorama==0.4.6
+colorlog==6.10.1
+comm==0.2.3
+contourpy==1.3.3
+cryptography==46.0.3
+cycler==0.12.1
+datasketch==1.6.5
+debugpy==1.8.17
+decorator==5.2.1
+ecdsa==0.19.1
+emoji==2.15.0
+executing==2.2.1
+fastapi==0.128.0
+filelock==3.20.0
+fonttools==4.61.1
+fsspec==2025.9.0
+gensim==4.4.0
+greenlet==3.3.0
+h11==0.16.0
+huggingface-hub==0.36.0
+idna==3.11
+ipykernel==7.0.1
+ipython==9.6.0
+ipython_pygments_lexers==1.1.1
+jedi==0.19.2
+Jinja2==3.1.6
+joblib==1.5.2
+jupyter_client==8.6.3
+jupyter_core==5.9.1
+kiwisolver==1.4.9
+Mako==1.3.10
+MarkupSafe==3.0.3
+matplotlib==3.10.8
+matplotlib-inline==0.1.7
+mpmath==1.3.0
+nest-asyncio==1.6.0
+networkx==3.5
+nltk==3.9.2
+numpy==2.3.4
+optuna==4.6.0
+outcome==1.3.0.post0
+packaging==25.0
+pandas==2.3.3
+parso==0.8.5
+passlib==1.7.4
+pillow==12.0.0
+platformdirs==4.5.0
+prompt_toolkit==3.0.52
+protobuf==6.33.0
+psutil==7.1.1
+psycopg2-binary==2.9.11
+pure_eval==0.2.3
+py_vncorenlp==0.1.4
+pyasn1==0.6.1
+pycparser==2.23
+pydantic==2.12.5
+pydantic_core==2.41.5
+Pygments==2.19.2
+pyjnius==1.7.0
+pyparsing==3.2.5
+PySocks==1.7.1
+python-crfsuite==0.9.11
+python-dateutil==2.9.0.post0
+python-dotenv==1.2.1
+python-jose==3.5.0
+pytrec_eval-terrier==0.5.10
+pytz==2025.2
+pyvi==0.1.1
+PyYAML==6.0.3
+pyzmq==27.1.0
+regex==2025.10.23
+requests==2.32.5
+rsa==4.9.1
+safetensors==0.7.0
+scikit-learn==1.7.2
+scipy==1.16.2
+selenium==4.37.0
+sentence-transformers==5.2.0
+setuptools==80.9.0
+six==1.17.0
+sklearn-crfsuite==0.5.0
+smart_open==7.5.0
+sniffio==1.3.1
+sortedcontainers==2.4.0
+soupsieve==2.8
+SQLAlchemy==2.0.45
+stack-data==0.6.3
+stanza==1.11.0
+starlette==0.50.0
+sympy==1.14.0
+tabulate==0.9.0
+threadpoolctl==3.6.0
+tokenizers==0.22.1
+torch==2.9.0
+tornado==6.5.2
+tqdm==4.67.1
+traitlets==5.14.3
+transformers==4.57.3
+trio==0.31.0
+trio-websocket==0.12.2
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+tzdata==2025.2
+urllib3==2.5.0
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