Update app.py

app.py

@@ -1,381 +1,381 @@
-import gradio as gr
-import torch
-import unicodedata
-import re
-import numpy as np
-from pathlib import Path
-from transformers import AutoTokenizer, AutoModel
-from sklearn.feature_extraction.text import HashingVectorizer
-from sklearn.preprocessing import normalize as sk_normalize
-import chromadb
-import joblib
-import pickle
-import scipy.sparse
-import textwrap
-import os
-import json # Για το διάβασμα του JSON κατά το setup
-import tqdm.auto as tq # Για progress bars κατά το setup
-
-# --------------------------- CONFIG για ChatbotVol107 -----------------------------------
-# --- Ρυθμίσεις Μοντέλου και Βάσης Δεδομένων ---
-MODEL_NAME        = "nlpaueb/bert-base-greek-uncased-v1"
-PERSISTENT_STORAGE_ROOT = Path("/data") # Για Hugging Face Spaces Persistent Storage
-DB_DIR_APP        = PERSISTENT_STORAGE_ROOT / "chroma_db_ChatbotVol107"
-COL_NAME          = "collection_chatbotvol107"
-ASSETS_DIR_APP    = PERSISTENT_STORAGE_ROOT / "assets_ChatbotVol107"
-DATA_PATH_FOR_SETUP = "./dataset14.json"
-
-# --- Ρυθμίσεις για Google Cloud Storage για τα PDF links ---
-GCS_BUCKET_NAME = "chatbotthesisihu" # Το δικό σας GCS Bucket Name
-GCS_PUBLIC_URL_PREFIX = f"https://storage.googleapis.com/{GCS_BUCKET_NAME}/"
-# -------------------------------------------------------------
-
-# --- Παράμετροι Αναζήτησης και Μοντέλου ---
-CHUNK_SIZE        = 512 
-CHUNK_OVERLAP     = 40
-BATCH_EMB         = 32  # Για τη δημιουργία των embeddings κατά το setup
-ALPHA_BASE        = 0.2 # Βέλτιστη τιμή alpha που βρήκατε
-ALPHA_LONGQ       = 0.35# Βέλτιστη τιμή alpha για μεγάλα queries που βρήκατε
-DEVICE            = "cuda" if torch.cuda.is_available() else "cpu"
-
-print(f"Running ChatbotVol107 on device: {DEVICE}")
-print(f"Using model: {MODEL_NAME}")
-
-# === ΛΟΓΙΚΗ ΔΗΜΙΟΥΡΓΙΑΣ ΒΑΣΗΣ ΚΑΙ ASSETS (Αν δεν υπάρχουν) ===
-def setup_database_and_assets():
-    print("Checking if database and assets need to be created...")
-    # Έλεγχος ύπαρξης βασικών αρχείων για να αποφασιστεί αν το setup χρειάζεται
-    # Ο έλεγχος col.count()
-    run_setup = True
-    if DB_DIR_APP.exists() and ASSETS_DIR_APP.exists() and (ASSETS_DIR_APP / "ids.pkl").exists():
-        try:
-            client_check = chromadb.PersistentClient(path=str(DB_DIR_APP.resolve()))
-            collection_check = client_check.get_collection(name=COL_NAME)
-            if collection_check.count() > 0:
-                print("✓ Database and assets appear to exist and collection is populated. Skipping setup.")
-                run_setup = False
-            else:
-                print("Collection exists but is empty. Proceeding with setup.")
-                if DB_DIR_APP.exists(): # Καθαρισμός αν η βάση υπάρχει αλλά είναι ελλιπής/άδεια
-                    import shutil
-                    print(f"Attempting to clean up existing empty/corrupt DB directory: {DB_DIR_APP}")
-                    shutil.rmtree(DB_DIR_APP)
-        except Exception as e_check: # Π.χ. η συλλογή δεν υπάρχει
-            print(f"Database or collection check failed (Error: {e_check}). Proceeding with setup.")
-            if DB_DIR_APP.exists(): # Καθαρισμός αν η βάση φαίνεται κατεστραμμένη
-                import shutil
-                print(f"Attempting to clean up existing corrupt DB directory: {DB_DIR_APP}")
-                shutil.rmtree(DB_DIR_APP)
-    
-    if not run_setup:
-        return True # Το setup δεν χρειάζεται
-
-    print(f"!Database/Assets not found or incomplete. Starting setup process.")
-    print(f"This will take a very long time, especially on the first run !")
-
-    ASSETS_DIR_APP.mkdir(parents=True, exist_ok=True)
-    DB_DIR_APP.mkdir(parents=True, exist_ok=True)
-
-    # --- Helper συναρτήσεις για το setup (τοπικές σε αυτή τη συνάρτηση) ---
-    def _strip_acc_setup(s:str)->str: return ''.join(ch for ch in unicodedata.normalize('NFD', s) if not unicodedata.combining(ch))
-    _STOP_SETUP = {"σχετικο","σχετικά","με","και"}
-    def _preprocess_setup(txt:str)->str:
-        txt = _strip_acc_setup(txt.lower())
-        txt = re.sub(r"[^a-zα-ω0-9 ]", " ", txt)
-        txt = re.sub(r"\s+", " ", txt).strip()
-        return " ".join(w for w in txt.split() if w not in _STOP_SETUP)
-
-    def _chunk_text_setup(text, tokenizer_setup):
-        token_ids = tokenizer_setup.encode(text, add_special_tokens=False)
-        if len(token_ids) <= (CHUNK_SIZE - 2): return [text]
-        ids_with_special_tokens = tokenizer_setup(text, truncation=False, padding=False)["input_ids"]
-        effective_chunk_size = CHUNK_SIZE
-        step = effective_chunk_size - CHUNK_OVERLAP
-        chunks = []
-        for i in range(0, len(ids_with_special_tokens), step):
-            current_chunk_ids = ids_with_special_tokens[i:i+effective_chunk_size]
-            if not current_chunk_ids: break
-            if len(chunks) > 0 and len(current_chunk_ids) < CHUNK_OVERLAP:
-                 if len(ids_with_special_tokens) - i < effective_chunk_size: pass
-                 else: break
-            decoded_chunk = tokenizer_setup.decode(current_chunk_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True).strip()
-            if decoded_chunk: chunks.append(decoded_chunk)
-        return chunks if chunks else [text]
-
-    def _cls_embed_setup(texts, tokenizer_setup, model_setup, bs=BATCH_EMB):
-        out_embeddings = []
-        for i in tq.tqdm(range(0, len(texts), bs), desc="Embedding texts for DB setup"):
-            enc = tokenizer_setup(texts[i:i+bs], padding=True, truncation=True, max_length=CHUNK_SIZE, return_tensors="pt").to(DEVICE)
-            with torch.no_grad():
-                model_output = model_setup(**enc)
-                last_hidden_state = model_output.last_hidden_state
-                cls_embedding = last_hidden_state[:, 0, :]
-                cls_normalized = torch.nn.functional.normalize(cls_embedding, p=2, dim=1)
-                out_embeddings.append(cls_normalized.cpu())
-        return torch.cat(out_embeddings).numpy()
-
-    # --- Κύρια Λογική του Setup ---
-    print(f"⏳ (Setup) Loading Model ({MODEL_NAME}) and Tokenizer...")
-    tokenizer_setup = AutoTokenizer.from_pretrained(MODEL_NAME)
-    model_setup = AutoModel.from_pretrained(MODEL_NAME).to(DEVICE).eval()
-    print("✓ (Setup) Model and Tokenizer loaded.")
-
-    print(f"⏳ (Setup) Reading & chunking JSON data from {DATA_PATH_FOR_SETUP}...")
-    if not Path(DATA_PATH_FOR_SETUP).exists():
-        print(f"!!! CRITICAL SETUP ERROR: Dataset file {DATA_PATH_FOR_SETUP} not found in the Space repo! Please upload it.")
-        return False 
-
-    with open(DATA_PATH_FOR_SETUP, encoding="utf-8") as f: docs_json = json.load(f)
-    
-    raw_chunks_setup, pre_chunks_setup, metas_setup, ids_list_setup = [], [], [], []
-    for d_setup in tq.tqdm(docs_json, desc="(Setup) Processing documents"):
-        doc_text = d_setup.get("text")
-        if not doc_text: continue
-        chunked_doc_texts = _chunk_text_setup(doc_text, tokenizer_setup)
-        if not chunked_doc_texts: continue
-        for idx, chunk in enumerate(chunked_doc_texts):
-            if not chunk.strip(): continue
-            raw_chunks_setup.append(chunk)
-            pre_chunks_setup.append(_preprocess_setup(chunk))
-            metas_setup.append({"id": d_setup["id"], "title": d_setup["title"], "url": d_setup["url"], "chunk_num": idx+1, "total_chunks": len(chunked_doc_texts)})
-            ids_list_setup.append(f'{d_setup["id"]}_c{idx+1}')
-    
-    print(f"  → (Setup) Total chunks created: {len(raw_chunks_setup):,}")
-    if not raw_chunks_setup:
-        print("!!! CRITICAL SETUP ERROR: No chunks were created from the dataset.")
-        return False 
-
-    print("⏳ (Setup) Building lexical matrices (TF-IDF)...")
-    char_vec_setup = HashingVectorizer(analyzer="char_wb", ngram_range=(2,5), n_features=2**20, norm=None, alternate_sign=False, binary=True)
-    word_vec_setup = HashingVectorizer(analyzer="word", ngram_range=(1,2), n_features=2**19, norm=None, alternate_sign=False, binary=True)
-    X_char_setup = sk_normalize(char_vec_setup.fit_transform(pre_chunks_setup))
-    X_word_setup = sk_normalize(word_vec_setup.fit_transform(pre_chunks_setup))
-    print("✓ (Setup) Lexical matrices built.")
-
-    print(f"⏳ (Setup) Setting up ChromaDB client at {DB_DIR_APP}...")
-    client_setup = chromadb.PersistentClient(path=str(DB_DIR_APP.resolve()))
-    print(f"  → (Setup) Creating collection: {COL_NAME}")
-    try: # Προσπάθεια διαγραφής αν υπάρχει για σίγουρη νέα δημιουργία
-        client_setup.delete_collection(COL_NAME)
-    except: pass
-    col_setup = client_setup.get_or_create_collection(COL_NAME, metadata={"hnsw:space":"cosine"})
-    
-    print("⏳ (Setup) Encoding chunks and streaming to ChromaDB...")
-    for start_idx in tq.tqdm(range(0, len(pre_chunks_setup), BATCH_EMB), desc="(Setup) Adding to ChromaDB"):
-        end_idx = min(start_idx + BATCH_EMB, len(pre_chunks_setup))
-        batch_pre_chunks  = pre_chunks_setup[start_idx:end_idx]
-        batch_ids         = ids_list_setup[start_idx:end_idx]
-        batch_metadatas   = metas_setup[start_idx:end_idx]
-        if not batch_pre_chunks: continue
-        batch_embeddings = _cls_embed_setup(batch_pre_chunks, tokenizer_setup, model_setup, bs=BATCH_EMB)
-        col_setup.add(embeddings=batch_embeddings.tolist(), documents=batch_pre_chunks, metadatas=batch_metadatas, ids=batch_ids)
-    
-    final_count = col_setup.count()
-    print(f"✓ (Setup) Index built and stored in ChromaDB. Final count: {final_count}")
-    if final_count != len(ids_list_setup):
-        print(f"!!! WARNING (Setup): Mismatch after setup! Expected {len(ids_list_setup)} items, got {final_count}")
-        # return False # Αποφασίζουμε αν αυτό είναι κρίσιμο σφάλμα ή απλή προειδοποίηση
-
-    print(f"💾 (Setup) Saving assets to {ASSETS_DIR_APP}...")
-    joblib.dump(char_vec_setup, ASSETS_DIR_APP / "char_vectorizer.joblib")
-    joblib.dump(word_vec_setup, ASSETS_DIR_APP / "word_vectorizer.joblib")
-    scipy.sparse.save_npz(ASSETS_DIR_APP / "X_char_sparse.npz", X_char_setup)
-    scipy.sparse.save_npz(ASSETS_DIR_APP / "X_word_sparse.npz", X_word_setup)
-    with open(ASSETS_DIR_APP / "pre_chunks.pkl", "wb") as f: pickle.dump(pre_chunks_setup, f)
-    with open(ASSETS_DIR_APP / "raw_chunks.pkl", "wb") as f: pickle.dump(raw_chunks_setup, f)
-    with open(ASSETS_DIR_APP / "ids.pkl", "wb") as f: pickle.dump(ids_list_setup, f)
-    with open(ASSETS_DIR_APP / "metas.pkl", "wb") as f: pickle.dump(metas_setup, f)
-    print("✓ (Setup) Assets saved.")
-    
-    del tokenizer_setup, model_setup, docs_json, raw_chunks_setup, pre_chunks_setup, metas_setup, ids_list_setup
-    del char_vec_setup, word_vec_setup, X_char_setup, X_word_setup, client_setup, col_setup
-    if DEVICE == "cuda":
-        torch.cuda.empty_cache()
-    print("🎉 (Setup) Database and assets creation process complete!")
-    return True
-# ==================================================================
-
-setup_successful = setup_database_and_assets()
-
-# ----------------------- PRE-/POST HELPERS (για την εφαρμογή Gradio) ----------------------------
-def strip_acc(s: str) -> str:
-    return ''.join(ch for ch in unicodedata.normalize('NFD', s)
-                   if not unicodedata.combining(ch))
-
-STOP = {"σχετικο", "σχετικα", "με", "και"}
-
-def preprocess(txt: str) -> str:
-    txt = strip_acc(txt.lower())
-    txt = re.sub(r"[^a-zα-ω0-9 ]", " ", txt)
-    txt = re.sub(r"\s+", " ", txt).strip()
-    return " ".join(w for w in txt.split() if w not in STOP)
-
-# cls_embed για την εφαρμογή Gradio (ένα query κάθε φορά)
-def cls_embed(texts, tokenizer_app, model_app): 
-    out = []
-    enc = tokenizer_app(texts, padding=True, truncation=True,
-              max_length=CHUNK_SIZE, return_tensors="pt").to(DEVICE)
-    with torch.no_grad():
-        model_output = model_app(**enc)
-        last_hidden_state = model_output.last_hidden_state
-        cls_embedding = last_hidden_state[:, 0, :] 
-        cls_normalized = torch.nn.functional.normalize(cls_embedding, p=2, dim=1)
-        out.append(cls_normalized.cpu())
-    return torch.cat(out).numpy()
-
-# ---------------------- LOAD MODELS & DATA (Για την εφαρμογή Gradio) --------------------
-tok = None
-model = None
-char_vec = None
-word_vec = None
-X_char = None
-X_word = None
-pre_chunks = None
-raw_chunks = None
-ids = None
-metas = None
-col = None
-
-if setup_successful:
-    print(f"⏳ Loading Model ({MODEL_NAME}) and Tokenizer for Gradio App...")
-    try:
-        tok = AutoTokenizer.from_pretrained(MODEL_NAME)
-        model = AutoModel.from_pretrained(MODEL_NAME).to(DEVICE).eval()
-        print("✓ Model and tokenizer loaded for Gradio App.")
-    except Exception as e:
-        print(f"CRITICAL ERROR loading model/tokenizer for Gradio App: {e}")
-        setup_successful = False 
-
-    if setup_successful:
-        print(f"⏳ Loading TF-IDF/Assets from {ASSETS_DIR_APP} for Gradio App...")
-        try:
-            char_vec = joblib.load(ASSETS_DIR_APP / "char_vectorizer.joblib")
-            word_vec = joblib.load(ASSETS_DIR_APP / "word_vectorizer.joblib")
-            X_char = scipy.sparse.load_npz(ASSETS_DIR_APP / "X_char_sparse.npz")
-            X_word = scipy.sparse.load_npz(ASSETS_DIR_APP / "X_word_sparse.npz")
-            with open(ASSETS_DIR_APP / "pre_chunks.pkl", "rb") as f: pre_chunks = pickle.load(f)
-            with open(ASSETS_DIR_APP / "raw_chunks.pkl", "rb") as f: raw_chunks = pickle.load(f)
-            with open(ASSETS_DIR_APP / "ids.pkl", "rb") as f: ids = pickle.load(f) 
-            with open(ASSETS_DIR_APP / "metas.pkl", "rb") as f: metas = pickle.load(f)
-            print("✓ TF-IDF/Assets loaded for Gradio App.")
-        except Exception as e:
-            print(f"CRITICAL ERROR loading TF-IDF/Assets for Gradio App: {e}")
-            setup_successful = False
-
-    if setup_successful:
-        print(f"⏳ Connecting to ChromaDB at {DB_DIR_APP} for Gradio App...")
-        try:
-            client = chromadb.PersistentClient(path=str(DB_DIR_APP.resolve()))
-            col = client.get_collection(COL_NAME) # Αν δεν υπάρχει μετά το setup, εδώ θα γίνει σφάλμα.
-            print(f"✓ Connected to ChromaDB. Collection '{COL_NAME}' count: {col.count()}")
-            if col.count() == 0 and len(ids) > 0: # Αν υπάρχουν ids αλλά η βάση είναι άδεια
-                print(f"!!! CRITICAL WARNING: ChromaDB collection '{COL_NAME}' is EMPTY at {DB_DIR_APP} but assets were loaded. Setup might have failed to populate DB correctly.")
-                setup_successful = False 
-        except Exception as e:
-            print(f"CRITICAL ERROR connecting to ChromaDB or getting collection for Gradio App: {e}")
-            setup_successful = False
-else:
-    print("!!! Setup process failed or was skipped. Gradio app will not function correctly. !!!")
-
-# ---------------------- HYBRID SEARCH (Κύρια Λογική) ---
-def hybrid_search_gradio(query, k=5):
-    if not setup_successful or not ids or not col or not model or not tok: 
-        return "Σφάλμα: Η εφαρμογή δεν αρχικοποιήθηκε σωστά. Τα δεδομένα ή το μοντέλο δεν φορτώθηκαν. Ελέγξτε τα logs εκκίνησης."
-    if not query.strip():
-        return "Παρακαλώ εισάγετε μια ερώτηση."
-    
-    q_pre = preprocess(query)
-    words = q_pre.split()
-    alpha = ALPHA_LONGQ if len(words) > 30 else ALPHA_BASE
-    exact_ids_set = {ids[i] for i, t in enumerate(pre_chunks) if q_pre in t}
-    q_emb_np = cls_embed([q_pre], tok, model) 
-    q_emb_list = q_emb_np.tolist()
-
-    try:
-        sem_results = col.query(query_embeddings=q_emb_list, n_results=min(k * 30, len(ids)), include=["distances"])
-    except Exception as e:
-        # Εκτύπωση του σφάλματος στα logs του server για διάγνωση
-        print(f"ERROR during ChromaDB query in hybrid_search_gradio: {type(e).__name__}: {e}")
-        return "Σφάλμα κατά την σημασιολογική αναζήτηση. Επικοινωνήστε με τον διαχειριστή."
-
-    sem_sims = {doc_id: 1 - dist for doc_id, dist in zip(sem_results["ids"][0], sem_results["distances"][0])}
-    q_char_sparse = char_vec.transform([q_pre])
-    q_char_normalized = sk_normalize(q_char_sparse)
-    char_sim_scores = (q_char_normalized @ X_char.T).toarray().flatten()
-    q_word_sparse = word_vec.transform([q_pre])
-    q_word_normalized = sk_normalize(q_word_sparse)
-    word_sim_scores = (q_word_normalized @ X_word.T).toarray().flatten()
-    lex_sims = {}
-    for idx, (c_score, w_score) in enumerate(zip(char_sim_scores, word_sim_scores)):
-        if c_score > 0 or w_score > 0:
-            if idx < len(ids): lex_sims[ids[idx]] = 0.85 * c_score + 0.15 * w_score
-            else: print(f"Warning (hybrid_search): Lexical score index {idx} out of bounds for ids list (len: {len(ids)}).")
-
-    all_chunk_ids_set = set(sem_sims.keys()) | set(lex_sims.keys()) | exact_ids_set
-    scored = []
-    for chunk_id_key in all_chunk_ids_set:
-        s = alpha * sem_sims.get(chunk_id_key, 0.0) + (1 - alpha) * lex_sims.get(chunk_id_key, 0.0)
-        if chunk_id_key in exact_ids_set: s = 1.0
-        scored.append((chunk_id_key, s))
-    scored.sort(key=lambda x: x[1], reverse=True)
-    hits_output = []
-    seen_doc_main_ids = set()
-    for chunk_id_val, score_val in scored:
-        try: idx_in_lists = ids.index(chunk_id_val) 
-        except ValueError: print(f"Warning (hybrid_search): chunk_id '{chunk_id_val}' not found in loaded ids. Skipping."); continue
-        doc_meta = metas[idx_in_lists]
-        doc_main_id = doc_meta['id']
-        if doc_main_id in seen_doc_main_ids: continue
-        original_url_from_meta = doc_meta.get('url', '#')
-        pdf_gcs_url = "#" 
-        pdf_filename_display = "N/A"
-        if original_url_from_meta and original_url_from_meta != '#':
-            pdf_filename_extracted = os.path.basename(original_url_from_meta)
-            if pdf_filename_extracted and pdf_filename_extracted.lower().endswith(".pdf"):
-                pdf_gcs_url = f"{GCS_PUBLIC_URL_PREFIX}{pdf_filename_extracted}"
-                pdf_filename_display = pdf_filename_extracted 
-            elif pdf_filename_extracted: pdf_filename_display = "Source is not a PDF"
-            # else: pdf_filename_display = "No source URL" # This case is covered by initialization
-        # else: pdf_filename_display = "No source URL" # This case is covered by initialization
-
-        hits_output.append({
-            "score": score_val, "title": doc_meta.get('title', 'N/A'),
-            "snippet": raw_chunks[idx_in_lists][:500] + " ...",
-            "original_url_meta": original_url_from_meta, "pdf_serve_url": pdf_gcs_url, 
-            "pdf_filename_display": pdf_filename_display 
-        })
-        seen_doc_main_ids.add(doc_main_id)
-        if len(hits_output) >= k: break
-    if not hits_output: return "Δεν βρέθηκαν σχετικά αποτελέσματα."
-    output_md = f"Βρέθηκαν **{len(hits_output)}** σχετικά αποτελέσματα:\n\n"
-    for hit in hits_output:
-        output_md += f"### {hit['title']} (Score: {hit['score']:.3f})\n"
-        snippet_wrapped = textwrap.fill(hit['snippet'].replace("\n", " "), width=100)
-        output_md += f"**Απόσπασμα:** {snippet_wrapped}\n"
-        if hit['pdf_serve_url'] and hit['pdf_serve_url'] != '#':
-            output_md += f"**Πηγή (PDF):** <a href='{hit['pdf_serve_url']}' target='_blank'>{hit['pdf_filename_display']}</a>\n"
-        elif hit['original_url_meta'] and hit['original_url_meta'] != '#':
-            output_md += f"**Πηγή (αρχικό από metadata):** [{hit['original_url_meta']}]({hit['original_url_meta']})\n"
-        output_md += "---\n"
-    return output_md
-    
-# ---------------------- GRADIO INTERFACE -----------------------------------
-print("🚀 Launching Gradio Interface for ChatbotVol107...")
-iface = gr.Interface(
-    fn=hybrid_search_gradio,
-    inputs=gr.Textbox(lines=3, placeholder="Γράψε την ερώτησή σου εδώ...", label=f"Ερώτηση προς τον βοηθό (Μοντέλο: {MODEL_NAME.split('/')[-1]}):"),
-    outputs=gr.Markdown(label="Απαντήσεις από τα έγγραφα:", rtl=False, sanitize_html=False),
-    title=f"🏛️ Ελληνικό Chatbot Υβριδικής Αναζήτησης (ChatbotVol107 - {MODEL_NAME.split('/')[-1]})", 
-    description=(f"Πληκτρολογήστε την ερώτησή σας για αναζήτηση. Χρησιμοποιεί το μοντέλο: {MODEL_NAME}.\n"
-                 "Τα PDF ανοίγουν από Google Cloud Storage σε νέα καρτέλα."),
-    allow_flagging="never",
-    examples=[
-        ["Τεχνολογίας τροφίμων;", 5],
-        ["Αμπελουργίας και της οινολογίας", 3],
-        ["Ποιες θέσεις αφορούν διδάσκοντες μερικής απασχόλησης στο Τμήμα Νοσηλευτικής του Πανεπιστημίου Ιωαννίνων;", 5]
-    ],
-)
-
-if __name__ == '__main__':
-    # Το allowed_paths δεν είναι απαραίτητο αν δεν εξυπηρετούνται άλλα τοπικά στατικά αρχεία.
+import gradio as gr
+import torch
+import unicodedata
+import re
+import numpy as np
+from pathlib import Path
+from transformers import AutoTokenizer, AutoModel
+from sklearn.feature_extraction.text import HashingVectorizer
+from sklearn.preprocessing import normalize as sk_normalize
+import chromadb
+import joblib
+import pickle
+import scipy.sparse
+import textwrap
+import os
+import json # Για το διάβασμα του JSON κατά το setup
+import tqdm.auto as tq # Για progress bars κατά το setup
+
+# --------------------------- CONFIG για ChatbotVol107 -----------------------------------
+# --- Ρυθμίσεις Μοντέλου και Βάσης Δεδομένων ---
+MODEL_NAME        = "nlpaueb/bert-base-greek-uncased-v1"
+PERSISTENT_STORAGE_ROOT = Path("/data") # Για Hugging Face Spaces Persistent Storage
+DB_DIR_APP        = PERSISTENT_STORAGE_ROOT / "chroma_db_ChatbotVol107"
+COL_NAME          = "collection_chatbotvol107"
+ASSETS_DIR_APP    = PERSISTENT_STORAGE_ROOT / "assets_ChatbotVol107"
+DATA_PATH_FOR_SETUP = "./dataset14.json"
+
+# --- Ρυθμίσεις για Google Cloud Storage για τα PDF links ---
+GCS_BUCKET_NAME = "chatbotthesisihu" # Το δικό σας GCS Bucket Name
+GCS_PUBLIC_URL_PREFIX = f"https://storage.googleapis.com/{GCS_BUCKET_NAME}/"
+# -------------------------------------------------------------
+
+# --- Παράμετροι Αναζήτησης και Μοντέλου ---
+CHUNK_SIZE        = 512 
+CHUNK_OVERLAP     = 40
+BATCH_EMB         = 32  # Για τη δημιουργία των embeddings κατά το setup
+ALPHA_BASE        = 0.2 # Βέλτιστη τιμή alpha που βρήκατε
+ALPHA_LONGQ       = 0.35# Βέλτιστη τιμή alpha για μεγάλα queries που βρήκατε
+DEVICE            = "cuda" if torch.cuda.is_available() else "cpu"
+
+print(f"Running ChatbotVol107 on device: {DEVICE}")
+print(f"Using model: {MODEL_NAME}")
+
+# === ΛΟΓΙΚΗ ΔΗΜΙΟΥΡΓΙΑΣ ΒΑΣΗΣ ΚΑΙ ASSETS (Αν δεν υπάρχουν) ===
+def setup_database_and_assets():
+    print("Checking if database and assets need to be created...")
+    # Έλεγχος ύπαρξης βασικών αρχείων για να αποφασιστεί αν το setup χρειάζεται
+    # Ο έλεγχος col.count()
+    run_setup = True
+    if DB_DIR_APP.exists() and ASSETS_DIR_APP.exists() and (ASSETS_DIR_APP / "ids.pkl").exists():
+        try:
+            client_check = chromadb.PersistentClient(path=str(DB_DIR_APP.resolve()))
+            collection_check = client_check.get_collection(name=COL_NAME)
+            if collection_check.count() > 0:
+                print("✓ Database and assets appear to exist and collection is populated. Skipping setup.")
+                run_setup = False
+            else:
+                print("Collection exists but is empty. Proceeding with setup.")
+                if DB_DIR_APP.exists(): # Καθαρισμός αν η βάση υπάρχει αλλά είναι ελλιπής/άδεια
+                    import shutil
+                    print(f"Attempting to clean up existing empty/corrupt DB directory: {DB_DIR_APP}")
+                    shutil.rmtree(DB_DIR_APP)
+        except Exception as e_check: # Π.χ. η συλλογή δεν υπάρχει
+            print(f"Database or collection check failed (Error: {e_check}). Proceeding with setup.")
+            if DB_DIR_APP.exists(): # Καθαρισμός αν η βάση φαίνεται κατεστραμμένη
+                import shutil
+                print(f"Attempting to clean up existing corrupt DB directory: {DB_DIR_APP}")
+                shutil.rmtree(DB_DIR_APP)
+    
+    if not run_setup:
+        return True # Το setup δεν χρειάζεται
+
+    print(f"!Database/Assets not found or incomplete. Starting setup process.")
+    print(f"This will take a very long time, especially on the first run !")
+
+    ASSETS_DIR_APP.mkdir(parents=True, exist_ok=True)
+    DB_DIR_APP.mkdir(parents=True, exist_ok=True)
+
+    # --- Helper συναρτήσεις για το setup (τοπικές σε αυτή τη συνάρτηση) ---
+    def _strip_acc_setup(s:str)->str: return ''.join(ch for ch in unicodedata.normalize('NFD', s) if not unicodedata.combining(ch))
+    _STOP_SETUP = {"σχετικο","σχετικά","με","και"}
+    def _preprocess_setup(txt:str)->str:
+        txt = _strip_acc_setup(txt.lower())
+        txt = re.sub(r"[^a-zα-ω0-9 ]", " ", txt)
+        txt = re.sub(r"\s+", " ", txt).strip()
+        return " ".join(w for w in txt.split() if w not in _STOP_SETUP)
+
+    def _chunk_text_setup(text, tokenizer_setup):
+        token_ids = tokenizer_setup.encode(text, add_special_tokens=False)
+        if len(token_ids) <= (CHUNK_SIZE - 2): return [text]
+        ids_with_special_tokens = tokenizer_setup(text, truncation=False, padding=False)["input_ids"]
+        effective_chunk_size = CHUNK_SIZE
+        step = effective_chunk_size - CHUNK_OVERLAP
+        chunks = []
+        for i in range(0, len(ids_with_special_tokens), step):
+            current_chunk_ids = ids_with_special_tokens[i:i+effective_chunk_size]
+            if not current_chunk_ids: break
+            if len(chunks) > 0 and len(current_chunk_ids) < CHUNK_OVERLAP:
+                 if len(ids_with_special_tokens) - i < effective_chunk_size: pass
+                 else: break
+            decoded_chunk = tokenizer_setup.decode(current_chunk_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True).strip()
+            if decoded_chunk: chunks.append(decoded_chunk)
+        return chunks if chunks else [text]
+
+    def _cls_embed_setup(texts, tokenizer_setup, model_setup, bs=BATCH_EMB):
+        out_embeddings = []
+        for i in tq.tqdm(range(0, len(texts), bs), desc="Embedding texts for DB setup"):
+            enc = tokenizer_setup(texts[i:i+bs], padding=True, truncation=True, max_length=CHUNK_SIZE, return_tensors="pt").to(DEVICE)
+            with torch.no_grad():
+                model_output = model_setup(**enc)
+                last_hidden_state = model_output.last_hidden_state
+                cls_embedding = last_hidden_state[:, 0, :]
+                cls_normalized = torch.nn.functional.normalize(cls_embedding, p=2, dim=1)
+                out_embeddings.append(cls_normalized.cpu())
+        return torch.cat(out_embeddings).numpy()
+
+    # --- Κύρια Λογική του Setup ---
+    print(f"⏳ (Setup) Loading Model ({MODEL_NAME}) and Tokenizer...")
+    tokenizer_setup = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model_setup = AutoModel.from_pretrained(MODEL_NAME).to(DEVICE).eval()
+    print("✓ (Setup) Model and Tokenizer loaded.")
+
+    print(f"⏳ (Setup) Reading & chunking JSON data from {DATA_PATH_FOR_SETUP}...")
+    if not Path(DATA_PATH_FOR_SETUP).exists():
+        print(f"!!! CRITICAL SETUP ERROR: Dataset file {DATA_PATH_FOR_SETUP} not found in the Space repo! Please upload it.")
+        return False 
+
+    with open(DATA_PATH_FOR_SETUP, encoding="utf-8") as f: docs_json = json.load(f)
+    
+    raw_chunks_setup, pre_chunks_setup, metas_setup, ids_list_setup = [], [], [], []
+    for d_setup in tq.tqdm(docs_json, desc="(Setup) Processing documents"):
+        doc_text = d_setup.get("text")
+        if not doc_text: continue
+        chunked_doc_texts = _chunk_text_setup(doc_text, tokenizer_setup)
+        if not chunked_doc_texts: continue
+        for idx, chunk in enumerate(chunked_doc_texts):
+            if not chunk.strip(): continue
+            raw_chunks_setup.append(chunk)
+            pre_chunks_setup.append(_preprocess_setup(chunk))
+            metas_setup.append({"id": d_setup["id"], "title": d_setup["title"], "url": d_setup["url"], "chunk_num": idx+1, "total_chunks": len(chunked_doc_texts)})
+            ids_list_setup.append(f'{d_setup["id"]}_c{idx+1}')
+    
+    print(f"  → (Setup) Total chunks created: {len(raw_chunks_setup):,}")
+    if not raw_chunks_setup:
+        print("!!! CRITICAL SETUP ERROR: No chunks were created from the dataset.")
+        return False 
+
+    print("⏳ (Setup) Building lexical matrices (TF-IDF)...")
+    char_vec_setup = HashingVectorizer(analyzer="char_wb", ngram_range=(2,5), n_features=2**20, norm=None, alternate_sign=False, binary=True)
+    word_vec_setup = HashingVectorizer(analyzer="word", ngram_range=(1,2), n_features=2**19, norm=None, alternate_sign=False, binary=True)
+    X_char_setup = sk_normalize(char_vec_setup.fit_transform(pre_chunks_setup))
+    X_word_setup = sk_normalize(word_vec_setup.fit_transform(pre_chunks_setup))
+    print("✓ (Setup) Lexical matrices built.")
+
+    print(f"⏳ (Setup) Setting up ChromaDB client at {DB_DIR_APP}...")
+    client_setup = chromadb.PersistentClient(path=str(DB_DIR_APP.resolve()))
+    print(f"  → (Setup) Creating collection: {COL_NAME}")
+    try: # Προσπάθεια διαγραφής αν υπάρχει για σίγουρη νέα δημιουργία
+        client_setup.delete_collection(COL_NAME)
+    except: pass
+    col_setup = client_setup.get_or_create_collection(COL_NAME, metadata={"hnsw:space":"cosine"})
+    
+    print("⏳ (Setup) Encoding chunks and streaming to ChromaDB...")
+    for start_idx in tq.tqdm(range(0, len(pre_chunks_setup), BATCH_EMB), desc="(Setup) Adding to ChromaDB"):
+        end_idx = min(start_idx + BATCH_EMB, len(pre_chunks_setup))
+        batch_pre_chunks  = pre_chunks_setup[start_idx:end_idx]
+        batch_ids         = ids_list_setup[start_idx:end_idx]
+        batch_metadatas   = metas_setup[start_idx:end_idx]
+        if not batch_pre_chunks: continue
+        batch_embeddings = _cls_embed_setup(batch_pre_chunks, tokenizer_setup, model_setup, bs=BATCH_EMB)
+        col_setup.add(embeddings=batch_embeddings.tolist(), documents=batch_pre_chunks, metadatas=batch_metadatas, ids=batch_ids)
+    
+    final_count = col_setup.count()
+    print(f"✓ (Setup) Index built and stored in ChromaDB. Final count: {final_count}")
+    if final_count != len(ids_list_setup):
+        print(f"!!! WARNING (Setup): Mismatch after setup! Expected {len(ids_list_setup)} items, got {final_count}")
+        # return False # Αποφασίζουμε αν αυτό είναι κρίσιμο σφάλμα ή απλή προειδοποίηση
+
+    print(f"💾 (Setup) Saving assets to {ASSETS_DIR_APP}...")
+    joblib.dump(char_vec_setup, ASSETS_DIR_APP / "char_vectorizer.joblib")
+    joblib.dump(word_vec_setup, ASSETS_DIR_APP / "word_vectorizer.joblib")
+    scipy.sparse.save_npz(ASSETS_DIR_APP / "X_char_sparse.npz", X_char_setup)
+    scipy.sparse.save_npz(ASSETS_DIR_APP / "X_word_sparse.npz", X_word_setup)
+    with open(ASSETS_DIR_APP / "pre_chunks.pkl", "wb") as f: pickle.dump(pre_chunks_setup, f)
+    with open(ASSETS_DIR_APP / "raw_chunks.pkl", "wb") as f: pickle.dump(raw_chunks_setup, f)
+    with open(ASSETS_DIR_APP / "ids.pkl", "wb") as f: pickle.dump(ids_list_setup, f)
+    with open(ASSETS_DIR_APP / "metas.pkl", "wb") as f: pickle.dump(metas_setup, f)
+    print("✓ (Setup) Assets saved.")
+    
+    del tokenizer_setup, model_setup, docs_json, raw_chunks_setup, pre_chunks_setup, metas_setup, ids_list_setup
+    del char_vec_setup, word_vec_setup, X_char_setup, X_word_setup, client_setup, col_setup
+    if DEVICE == "cuda":
+        torch.cuda.empty_cache()
+    print("🎉 (Setup) Database and assets creation process complete!")
+    return True
+# ==================================================================
+
+setup_successful = setup_database_and_assets()
+
+# ----------------------- PRE-/POST HELPERS (για την εφαρμογή Gradio) ----------------------------
+def strip_acc(s: str) -> str:
+    return ''.join(ch for ch in unicodedata.normalize('NFD', s)
+                   if not unicodedata.combining(ch))
+
+STOP = {"σχετικο", "σχετικα", "με", "και"}
+
+def preprocess(txt: str) -> str:
+    txt = strip_acc(txt.lower())
+    txt = re.sub(r"[^a-zα-ω0-9 ]", " ", txt)
+    txt = re.sub(r"\s+", " ", txt).strip()
+    return " ".join(w for w in txt.split() if w not in STOP)
+
+# cls_embed για την εφαρμογή Gradio (ένα query κάθε φορά)
+def cls_embed(texts, tokenizer_app, model_app): 
+    out = []
+    enc = tokenizer_app(texts, padding=True, truncation=True,
+              max_length=CHUNK_SIZE, return_tensors="pt").to(DEVICE)
+    with torch.no_grad():
+        model_output = model_app(**enc)
+        last_hidden_state = model_output.last_hidden_state
+        cls_embedding = last_hidden_state[:, 0, :] 
+        cls_normalized = torch.nn.functional.normalize(cls_embedding, p=2, dim=1)
+        out.append(cls_normalized.cpu())
+    return torch.cat(out).numpy()
+
+# ---------------------- LOAD MODELS & DATA (Για την εφαρμογή Gradio) --------------------
+tok = None
+model = None
+char_vec = None
+word_vec = None
+X_char = None
+X_word = None
+pre_chunks = None
+raw_chunks = None
+ids = None
+metas = None
+col = None
+
+if setup_successful:
+    print(f"⏳ Loading Model ({MODEL_NAME}) and Tokenizer for Gradio App...")
+    try:
+        tok = AutoTokenizer.from_pretrained(MODEL_NAME)
+        model = AutoModel.from_pretrained(MODEL_NAME).to(DEVICE).eval()
+        print("✓ Model and tokenizer loaded for Gradio App.")
+    except Exception as e:
+        print(f"CRITICAL ERROR loading model/tokenizer for Gradio App: {e}")
+        setup_successful = False 
+
+    if setup_successful:
+        print(f"⏳ Loading TF-IDF/Assets from {ASSETS_DIR_APP} for Gradio App...")
+        try:
+            char_vec = joblib.load(ASSETS_DIR_APP / "char_vectorizer.joblib")
+            word_vec = joblib.load(ASSETS_DIR_APP / "word_vectorizer.joblib")
+            X_char = scipy.sparse.load_npz(ASSETS_DIR_APP / "X_char_sparse.npz")
+            X_word = scipy.sparse.load_npz(ASSETS_DIR_APP / "X_word_sparse.npz")
+            with open(ASSETS_DIR_APP / "pre_chunks.pkl", "rb") as f: pre_chunks = pickle.load(f)
+            with open(ASSETS_DIR_APP / "raw_chunks.pkl", "rb") as f: raw_chunks = pickle.load(f)
+            with open(ASSETS_DIR_APP / "ids.pkl", "rb") as f: ids = pickle.load(f) 
+            with open(ASSETS_DIR_APP / "metas.pkl", "rb") as f: metas = pickle.load(f)
+            print("✓ TF-IDF/Assets loaded for Gradio App.")
+        except Exception as e:
+            print(f"CRITICAL ERROR loading TF-IDF/Assets for Gradio App: {e}")
+            setup_successful = False
+
+    if setup_successful:
+        print(f"⏳ Connecting to ChromaDB at {DB_DIR_APP} for Gradio App...")
+        try:
+            client = chromadb.PersistentClient(path=str(DB_DIR_APP.resolve()))
+            col = client.get_collection(COL_NAME) # Αν δεν υπάρχει μετά το setup, εδώ θα γίνει σφάλμα.
+            print(f"✓ Connected to ChromaDB. Collection '{COL_NAME}' count: {col.count()}")
+            if col.count() == 0 and len(ids) > 0: # Αν υπάρχουν ids αλλά η βάση είναι άδεια
+                print(f"!!! CRITICAL WARNING: ChromaDB collection '{COL_NAME}' is EMPTY at {DB_DIR_APP} but assets were loaded. Setup might have failed to populate DB correctly.")
+                setup_successful = False 
+        except Exception as e:
+            print(f"CRITICAL ERROR connecting to ChromaDB or getting collection for Gradio App: {e}")
+            setup_successful = False
+else:
+    print("!!! Setup process failed or was skipped. Gradio app will not function correctly. !!!")
+
+# ---------------------- HYBRID SEARCH (Κύρια Λογική) ---
+def hybrid_search_gradio(query, k=5):
+    if not setup_successful or not ids or not col or not model or not tok: 
+        return "Σφάλμα: Η εφαρμογή δεν αρχικοποιήθηκε σωστά. Τα δεδομένα ή το μοντέλο δεν φορτώθηκαν. Ελέγξτε τα logs εκκίνησης."
+    if not query.strip():
+        return "Παρακαλώ εισάγετε μια ερώτηση."
+    
+    q_pre = preprocess(query)
+    words = q_pre.split()
+    alpha = ALPHA_LONGQ if len(words) > 30 else ALPHA_BASE
+    exact_ids_set = {ids[i] for i, t in enumerate(pre_chunks) if q_pre in t}
+    q_emb_np = cls_embed([q_pre], tok, model) 
+    q_emb_list = q_emb_np.tolist()
+
+    try:
+        sem_results = col.query(query_embeddings=q_emb_list, n_results=min(k * 30, len(ids)), include=["distances"])
+    except Exception as e:
+        # Εκτύπωση του σφάλματος στα logs του server για διάγνωση
+        print(f"ERROR during ChromaDB query in hybrid_search_gradio: {type(e).__name__}: {e}")
+        return "Σφάλμα κατά την σημασιολογική αναζήτηση. Επικοινωνήστε με τον διαχειριστή."
+
+    sem_sims = {doc_id: 1 - dist for doc_id, dist in zip(sem_results["ids"][0], sem_results["distances"][0])}
+    q_char_sparse = char_vec.transform([q_pre])
+    q_char_normalized = sk_normalize(q_char_sparse)
+    char_sim_scores = (q_char_normalized @ X_char.T).toarray().flatten()
+    q_word_sparse = word_vec.transform([q_pre])
+    q_word_normalized = sk_normalize(q_word_sparse)
+    word_sim_scores = (q_word_normalized @ X_word.T).toarray().flatten()
+    lex_sims = {}
+    for idx, (c_score, w_score) in enumerate(zip(char_sim_scores, word_sim_scores)):
+        if c_score > 0 or w_score > 0:
+            if idx < len(ids): lex_sims[ids[idx]] = 0.85 * c_score + 0.15 * w_score
+            else: print(f"Warning (hybrid_search): Lexical score index {idx} out of bounds for ids list (len: {len(ids)}).")
+
+    all_chunk_ids_set = set(sem_sims.keys()) | set(lex_sims.keys()) | exact_ids_set
+    scored = []
+    for chunk_id_key in all_chunk_ids_set:
+        s = alpha * sem_sims.get(chunk_id_key, 0.0) + (1 - alpha) * lex_sims.get(chunk_id_key, 0.0)
+        if chunk_id_key in exact_ids_set: s = 1.0
+        scored.append((chunk_id_key, s))
+    scored.sort(key=lambda x: x[1], reverse=True)
+    hits_output = []
+    seen_doc_main_ids = set()
+    for chunk_id_val, score_val in scored:
+        try: idx_in_lists = ids.index(chunk_id_val) 
+        except ValueError: print(f"Warning (hybrid_search): chunk_id '{chunk_id_val}' not found in loaded ids. Skipping."); continue
+        doc_meta = metas[idx_in_lists]
+        doc_main_id = doc_meta['id']
+        if doc_main_id in seen_doc_main_ids: continue
+        original_url_from_meta = doc_meta.get('url', '#')
+        pdf_gcs_url = "#" 
+        pdf_filename_display = "N/A"
+        if original_url_from_meta and original_url_from_meta != '#':
+            pdf_filename_extracted = os.path.basename(original_url_from_meta)
+            if pdf_filename_extracted and pdf_filename_extracted.lower().endswith(".pdf"):
+                pdf_gcs_url = f"{GCS_PUBLIC_URL_PREFIX}{pdf_filename_extracted}"
+                pdf_filename_display = pdf_filename_extracted 
+            elif pdf_filename_extracted: pdf_filename_display = "Source is not a PDF"
+            # else: pdf_filename_display = "No source URL" # This case is covered by initialization
+        # else: pdf_filename_display = "No source URL" # This case is covered by initialization
+
+        hits_output.append({
+            "score": score_val, "title": doc_meta.get('title', 'N/A'),
+            "snippet": raw_chunks[idx_in_lists][:500] + " ...",
+            "original_url_meta": original_url_from_meta, "pdf_serve_url": pdf_gcs_url, 
+            "pdf_filename_display": pdf_filename_display 
+        })
+        seen_doc_main_ids.add(doc_main_id)
+        if len(hits_output) >= k: break
+    if not hits_output: return "Δεν βρέθηκαν σχετικά αποτελέσματα."
+    output_md = f"Βρέθηκαν **{len(hits_output)}** σχετικά αποτελέσματα:\n\n"
+    for hit in hits_output:
+        output_md += f"### {hit['title']} (Score: {hit['score']:.3f})\n"
+        snippet_wrapped = textwrap.fill(hit['snippet'].replace("\n", " "), width=100)
+        output_md += f"**Απόσπασμα:** {snippet_wrapped}\n"
+        if hit['pdf_serve_url'] and hit['pdf_serve_url'] != '#':
+            output_md += f"**Πηγή (PDF):** <a href='{hit['pdf_serve_url']}' target='_blank'>{hit['pdf_filename_display']}</a>\n"
+        elif hit['original_url_meta'] and hit['original_url_meta'] != '#':
+            output_md += f"**Πηγή (αρχικό από metadata):** [{hit['original_url_meta']}]({hit['original_url_meta']})\n"
+        output_md += "---\n"
+    return output_md
+    
+# ---------------------- GRADIO INTERFACE -----------------------------------
+print("🚀 Launching Gradio Interface for Meltemi...")
+iface = gr.Interface(
+    fn=hybrid_search_gradio,
+    inputs=gr.Textbox(lines=3, placeholder="Γράψε την ερώτησή σου εδώ...", label=f"Ερώτηση προς τον βοηθό (Μοντέλο: {MODEL_NAME.split('/')[-1]}):"),
+    outputs=gr.Markdown(label="Απαντήσεις από τα έγγραφα:", rtl=False, sanitize_html=False),
+    title=f"🏛️ Ελληνικό Chatbot Υβριδικής Αναζήτησης (Meltemi - {MODEL_NAME.split('/')[-1]})", 
+    description=(f"Πληκτρολογήστε την ερώτησή σας για αναζήτηση. Χρησιμοποιεί το μοντέλο: {MODEL_NAME}.\n"
+                 "Τα PDF ανοίγουν από Google Cloud Storage σε νέα καρτέλα."),
+    allow_flagging="never",
+    examples=[
+        ["Τεχνολογίας τροφίμων;", 5],
+        ["Αμπελουργίας και της οινολογίας", 3],
+        ["Ποιες θέσεις αφορούν διδάσκοντες μερικής απασχόλησης στο Τμήμα Νοσηλευτικής του Πανεπιστημίου Ιωαννίνων;", 5]
+    ],
+)
+
+if __name__ == '__main__':
+    # Το allowed_paths δεν είναι απαραίτητο αν δεν εξυπηρετούνται άλλα τοπικά στατικά αρχεία.
     iface.launch()