Upload app.py

app.py

@@ -1,413 +1,225 @@
-import os
-import io
-import json
-import pathlib
-import shutil
-from typing import List, Tuple, Dict
-import gradio as gr
-import numpy as np
-import faiss
+_L='faiss.index'
+_K='meta.json'
+_J='source'
+_I='name'
+_H='\x00'
+_G=False
+_F='\n'
+_E='text'
+_D='mmr'
+_C='utf-8'
+_B=None
+_A=True
+import os,io,json,pathlib,shutil
+from typing import List,Tuple,Dict
+import gradio as gr,numpy as np,faiss
 from sentence_transformers import SentenceTransformer
 from pypdf import PdfReader
-import fitz  # PyMuPDF
+import fitz
 from collections import defaultdict
 from openai import OpenAI
-
-# =========================
-# LLM Endpoint
-# =========================
-API_KEY = os.environ.get("API_KEY")
-if not API_KEY:
-    raise RuntimeError("Missing API_KEY (set it in Hugging Face: Settings → Variables and secrets).")
-
-client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=API_KEY)
-
-# Model configuration
-SINGLE_MODEL_NAME = "deepseek/deepseek-r1:free"
-GEN_TEMPERATURE = 0.2
-GEN_TOP_P = 0.95
-GEN_MAX_TOKENS = 1024
-EMB_MODEL_NAME = "intfloat/multilingual-e5-base"
-
-def choose_store_dir() -> Tuple[str, bool]:
-    data_root = "/data"
-    if os.path.isdir(data_root) and os.access(data_root, os.W_OK):
-        d = os.path.join(data_root, "rag_store")
-        try:
-            os.makedirs(d, exist_ok=True)
-            testf = os.path.join(d, ".write_test")
-            with open(testf, "w", encoding="utf-8") as f:
-                f.write("ok")
-            os.remove(testf)
-            return d, True
-        except Exception:
-            pass
-    d = os.path.join(os.getcwd(), "store")
-    os.makedirs(d, exist_ok=True)
-    return d, False
-
-STORE_DIR, IS_PERSISTENT = choose_store_dir()
-META_PATH = os.path.join(STORE_DIR, "meta.json")
-INDEX_PATH = os.path.join(STORE_DIR, "faiss.index")
-LEGACY_STORE_DIR = os.path.join(os.getcwd(), "store")
-
+API_KEY=os.environ.get('API_KEY')
+if not API_KEY:raise RuntimeError('Missing API_KEY (set it in Hugging Face: Settings → Variables and secrets).')
+client=OpenAI(base_url='https://openrouter.ai/api/v1',api_key=API_KEY)
+SINGLE_MODEL_NAME='deepseek/deepseek-r1:free'
+GEN_TEMPERATURE=.2
+GEN_TOP_P=.95
+GEN_MAX_TOKENS=1024
+EMB_MODEL_NAME='intfloat/multilingual-e5-base'
+def choose_store_dir():
+	B='/data'
+	if os.path.isdir(B)and os.access(B,os.W_OK):
+		A=os.path.join(B,'rag_store')
+		try:
+			os.makedirs(A,exist_ok=_A);C=os.path.join(A,'.write_test')
+			with open(C,'w',encoding=_C)as D:D.write('ok')
+			os.remove(C);return A,_A
+		except Exception:pass
+	A=os.path.join(os.getcwd(),'store');os.makedirs(A,exist_ok=_A);return A,_G
+STORE_DIR,IS_PERSISTENT=choose_store_dir()
+META_PATH=os.path.join(STORE_DIR,_K)
+INDEX_PATH=os.path.join(STORE_DIR,_L)
+LEGACY_STORE_DIR=os.path.join(os.getcwd(),'store')
 def migrate_legacy_if_any():
-    try:
-        if IS_PERSISTENT:
-            legacy_meta = os.path.join(LEGACY_STORE_DIR, "meta.json")
-            legacy_index = os.path.join(LEGACY_STORE_DIR, "faiss.index")
-            if (not os.path.exists(META_PATH) or not os.path.exists(INDEX_PATH)) \
-                    and os.path.isdir(LEGACY_STORE_DIR) \
-                    and os.path.exists(legacy_meta) and os.path.exists(legacy_index):
-                shutil.copyfile(legacy_meta, META_PATH)
-                shutil.copyfile(legacy_index, INDEX_PATH)
-    except Exception:
-        pass
-
+	try:
+		if IS_PERSISTENT:
+			A=os.path.join(LEGACY_STORE_DIR,_K);B=os.path.join(LEGACY_STORE_DIR,_L)
+			if(not os.path.exists(META_PATH)or not os.path.exists(INDEX_PATH))and os.path.isdir(LEGACY_STORE_DIR)and os.path.exists(A)and os.path.exists(B):shutil.copyfile(A,META_PATH);shutil.copyfile(B,INDEX_PATH)
+	except Exception:pass
 migrate_legacy_if_any()
-
-_emb_model = None
-_index: faiss.Index = None
-_meta: Dict[str, Dict] = {}
-
-DEFAULT_TOP_K = 6
-DEFAULT_POOL_K = 40
-DEFAULT_PER_SOURCE_CAP = 2
-DEFAULT_STRATEGY = "mmr"
-DEFAULT_MMR_LAMBDA = 0.5
-
+_emb_model=_B
+_index=_B
+_meta={}
+DEFAULT_TOP_K=6
+DEFAULT_POOL_K=40
+DEFAULT_PER_SOURCE_CAP=2
+DEFAULT_STRATEGY=_D
+DEFAULT_MMR_LAMBDA=.5
 def get_emb_model():
-    global _emb_model
-    if _emb_model is None:
-        _emb_model = SentenceTransformer(EMB_MODEL_NAME)
-    return _emb_model
-
-def _ensure_index(dim: int):
-    global _index
-    if _index is None:
-        _index = faiss.IndexFlatIP(dim)
-
+	global _emb_model
+	if _emb_model is _B:_emb_model=SentenceTransformer(EMB_MODEL_NAME)
+	return _emb_model
+def _ensure_index(dim):
+	global _index
+	if _index is _B:_index=faiss.IndexFlatIP(dim)
 def _persist():
-    faiss.write_index(_index, INDEX_PATH)
-    with open(META_PATH, "w", encoding="utf-8") as f:
-        json.dump(_meta, f, ensure_ascii=False)
-
+	faiss.write_index(_index,INDEX_PATH)
+	with open(META_PATH,'w',encoding=_C)as A:json.dump(_meta,A,ensure_ascii=_G)
 def _load_if_any():
-    global _index, _meta
-    if os.path.exists(INDEX_PATH) and os.path.exists(META_PATH):
-        _index = faiss.read_index(INDEX_PATH)
-        with open(META_PATH, "r", encoding="utf-8") as f:
-            _meta = json.load(f)
-
-def _chunk_text(text: str, chunk_size: int = 800, overlap: int = 120) -> List[str]:
-    text = text.replace("\u0000", "")
-    res, i, n = [], 0, len(text)
-    while i < n:
-        j = min(i + chunk_size, n)
-        seg = text[i:j].strip()
-        if seg:
-            res.append(seg)
-        i = max(0, j - overlap)
-        if j >= n:
-            break
-    return res
-
-def _read_bytes(file) -> bytes:
-    if isinstance(file, dict):
-        p = file.get("path") or file.get("name")
-        if p and os.path.exists(p):
-            with open(p, "rb") as f:
-                return f.read()
-    if "data" in file and isinstance(file["data"], (bytes, bytearray)):
-        return bytes(file["data"])
-    if isinstance(file, (str, pathlib.Path)):
-        with open(file, "rb") as f:
-            return f.read()
-    if hasattr(file, "read"):
-        try:
-            if hasattr(file, "seek"):
-                try:
-                    file.seek(0)
-                except Exception:
-                    pass
-            return file.read()
-        finally:
-            try:
-                file.close()
-            except Exception:
-                pass
-    raise ValueError("Unsupported file type from gr.File")
-
-def _decode_best_effort(raw: bytes) -> str:
-    for enc in ["utf-8", "cp932", "shift_jis", "cp950", "big5", "gb18030", "latin-1"]:
-        try:
-            return raw.decode(enc)
-        except Exception:
-            continue
-    return raw.decode("utf-8", errors="ignore")
-
-def _read_pdf(file_bytes: bytes) -> str:
-    try:
-        with fitz.open(stream=file_bytes, filetype="pdf") as doc:
-            if doc.is_encrypted:
-                try:
-                    doc.authenticate("")
-                except Exception:
-                    pass
-            texts = [(page.get_text("text") or "") for page in doc]
-            txt = "\n".join(texts)
-            if txt.strip():
-                return txt
-    except Exception:
-        pass
-    try:
-        reader = PdfReader(io.BytesIO(file_bytes))
-        pages = []
-        for p in reader.pages:
-            try:
-                pages.append(p.extract_text() or "")
-            except Exception:
-                pages.append("")
-        return "\n".join(pages)
-    except Exception:
-        return ""
-
-def _read_any(file) -> str:
-    if isinstance(file, dict):
-        name = (file.get("orig_name") or file.get("name") or file.get("path") or "upload").lower()
-    else:
-        name = getattr(file, "name", None) or (str(file) if isinstance(file, (str, pathlib.Path)) else "upload")
-    name = name.lower()
-    raw = _read_bytes(file)
-    if name.endswith(".pdf"):
-        return _read_pdf(raw).replace("\u0000", "")
-    return _decode_best_effort(raw).replace("\u0000", "")
-
-DOCS_DIR = os.path.join(os.getcwd(), "docs")
-
-def get_docs_files() -> List[str]:
-    if not os.path.isdir(DOCS_DIR):
-        return []
-    files = []
-    for fname in os.listdir(DOCS_DIR):
-        if fname.lower().endswith((".pdf", ".txt")):
-            files.append(os.path.join(DOCS_DIR, fname))
-    return files
-
+	global _index,_meta
+	if os.path.exists(INDEX_PATH)and os.path.exists(META_PATH):
+		_index=faiss.read_index(INDEX_PATH)
+		with open(META_PATH,'r',encoding=_C)as A:_meta=json.load(A)
+def _chunk_text(text,chunk_size=800,overlap=120):
+	A=text;A=A.replace(_H,'');E,B,C=[],0,len(A)
+	while B<C:
+		D=min(B+chunk_size,C);F=A[B:D].strip()
+		if F:E.append(F)
+		B=max(0,D-overlap)
+		if D>=C:break
+	return E
+def _read_bytes(file):
+	D='data';A=file
+	if isinstance(A,dict):
+		B=A.get('path')or A.get(_I)
+		if B and os.path.exists(B):
+			with open(B,'rb')as C:return C.read()
+	if D in A and isinstance(A[D],(bytes,bytearray)):return bytes(A[D])
+	if isinstance(A,(str,pathlib.Path)):
+		with open(A,'rb')as C:return C.read()
+	if hasattr(A,'read'):
+		try:
+			if hasattr(A,'seek'):
+				try:A.seek(0)
+				except Exception:pass
+			return A.read()
+		finally:
+			try:A.close()
+			except Exception:pass
+	raise ValueError('Unsupported file type from gr.File')
+def _decode_best_effort(raw):
+	for A in[_C,'cp932','shift_jis','cp950','big5','gb18030','latin-1']:
+		try:return raw.decode(A)
+		except Exception:continue
+	return raw.decode(_C,errors='ignore')
+def _read_pdf(file_bytes):
+	C=file_bytes
+	try:
+		with fitz.open(stream=C,filetype='pdf')as A:
+			if A.is_encrypted:
+				try:A.authenticate('')
+				except Exception:pass
+			E=[A.get_text(_E)or''for A in A];D=_F.join(E)
+			if D.strip():return D
+	except Exception:pass
+	try:
+		F=PdfReader(io.BytesIO(C));B=[]
+		for G in F.pages:
+			try:B.append(G.extract_text()or'')
+			except Exception:B.append('')
+		return _F.join(B)
+	except Exception:return''
+def _read_any(file):
+	D='upload';A=file
+	if isinstance(A,dict):B=(A.get('orig_name')or A.get(_I)or A.get('path')or D).lower()
+	else:B=getattr(A,_I,_B)or(str(A)if isinstance(A,(str,pathlib.Path))else D)
+	B=B.lower();C=_read_bytes(A)
+	if B.endswith('.pdf'):return _read_pdf(C).replace(_H,'')
+	return _decode_best_effort(C).replace(_H,'')
+DOCS_DIR=os.path.join(os.getcwd(),'docs')
+def get_docs_files():
+	if not os.path.isdir(DOCS_DIR):return[]
+	A=[]
+	for B in os.listdir(DOCS_DIR):
+		if B.lower().endswith(('.pdf','.txt')):A.append(os.path.join(DOCS_DIR,B))
+	return A
 def build_corpus_from_docs():
-    global _index, _meta
-    files = get_docs_files()
-    if not files:
-        return "No files found in docs folder."
-    emb_model = get_emb_model()
-    chunks, sources, failed = [], [], []
-    _index = None
-    _meta = {}
-    for f in files:
-        fname = os.path.basename(f)
-        try:
-            text = _read_any(f) or ""
-            parts = _chunk_text(text)
-            if not parts:
-                failed.append(fname)
-                continue
-            chunks.extend(parts)
-            sources.extend([fname] * len(parts))
-        except Exception:
-            failed.append(fname)
-    if not chunks:
-        return "No text extracted from docs."
-    passages = [f"passage: {c}" for c in chunks]
-    vec = emb_model.encode(passages, batch_size=64, convert_to_numpy=True, normalize_embeddings=True)
-    _ensure_index(vec.shape[1])
-    _index.add(vec)
-    for i, (src, c) in enumerate(zip(sources, chunks)):
-        _meta[str(i)] = {"source": src, "text": c}
-    _persist()
-    msg = f"Indexed {len(chunks)} chunks from {len(files)} files."
-    if failed:
-        msg += f" Failed files: {', '.join(failed)}"
-    return msg
-
-def _encode_query_vec(query: str) -> np.ndarray:
-    return get_emb_model().encode([f"query: {query}"], convert_to_numpy=True, normalize_embeddings=True)
-
-def retrieve_candidates(qvec: np.ndarray, pool_k: int = 40) -> List[Tuple[str, float]]:
-    if _index is None or _index.ntotal == 0:
-        return []
-    pool_k = min(pool_k, _index.ntotal)
-    D, I = _index.search(qvec, pool_k)
-    return [(str(idx), float(score)) for idx, score in zip(I[0], D[0]) if idx != -1]
-
-def select_diverse_by_source(cands: List[Tuple[str, float]], top_k: int = 6, per_source_cap: int = 2) -> List[Tuple[str, float]]:
-    if not cands:
-        return []
-    by_src: Dict[str, List[Tuple[str, float]]] = defaultdict(list)
-    for cid, s in cands:
-        m = _meta.get(cid)
-        if not m:
-            continue
-        by_src[m["source"]].append((cid, s))
-    for src in by_src:
-        by_src[src] = by_src[src][:per_source_cap]
-    picked, src_items, ptrs = [], [(s, it) for s, it in by_src.items()], {s: 0 for s in by_src}
-    while len(picked) < top_k:
-        advanced = False
-        for src, items in src_items:
-            i = ptrs[src]
-            if i < len(items):
-                picked.append(items[i])
-                ptrs[src] = i + 1
-                advanced = True
-            if len(picked) >= top_k:
-                break
-        if not advanced:
-            break
-    if len(picked) < top_k:
-        seen = {cid for cid, _ in picked}
-        for cid, s in cands:
-            if cid not in seen:
-                picked.append((cid, s))
-                seen.add(cid)
-                if len(picked) >= top_k:
-                    break
-    return picked[:top_k]
-
-def _encode_chunks_text(cids: List[str]) -> np.ndarray:
-    texts = [f"passage: {(_meta.get(cid) or {}).get('text','')}" for cid in cids]
-    return get_emb_model().encode(texts, convert_to_numpy=True, normalize_embeddings=True)
-
-def select_diverse_mmr(cands: List[Tuple[str, float]], qvec: np.ndarray, top_k: int = 6, mmr_lambda: float = 0.5) -> List[Tuple[str, float]]:
-    if not cands:
-        return []
-    cids = [cid for cid, _ in cands]
-    cvecs = _encode_chunks_text(cids)
-    sim_to_q = (cvecs @ qvec.T).reshape(-1)
-    selected, remaining = [], set(range(len(cids)))
-    while len(selected) < min(top_k, len(cids)):
-        if not selected:
-            i = int(np.argmax(sim_to_q))
-            selected.append(i)
-            remaining.remove(i)
-            continue
-        S = cvecs[selected]
-        sim_to_S = (cvecs[list(remaining)] @ S.T)
-        max_sim_to_S = sim_to_S.max(axis=1) if sim_to_S.size > 0 else np.zeros((len(remaining),), dtype=np.float32)
-        sim_q_rem = sim_to_q[list(remaining)]
-        mmr_scores = mmr_lambda * sim_q_rem - (1.0 - mmr_lambda) * max_sim_to_S
-        j_rel = int(np.argmax(mmr_scores))
-        j = list(remaining)[j_rel]
-        selected.append(j)
-        remaining.remove(j)
-    return [(cids[i], float(sim_to_q[i])) for i in selected][:top_k]
-
-def retrieve_diverse(query: str,
-                     top_k: int = 6,
-                     pool_k: int = 40,
-                     per_source_cap: int = 2,
-                     strategy: str = "mmr",
-                     mmr_lambda: float = 0.5) -> List[Tuple[str, float]]:
-    qvec = _encode_query_vec(query)
-    cands = retrieve_candidates(qvec, pool_k=pool_k)
-    if strategy == "mmr":
-        return select_diverse_mmr(cands, qvec, top_k=top_k, mmr_lambda=mmr_lambda)
-    return select_diverse_by_source(cands, top_k=top_k, per_source_cap=per_source_cap)
-
-def _format_ctx(hits: List[Tuple[str, float]]) -> str:
-    if not hits:
-        return ""
-    lines = []
-    for cid, _ in hits:
-        m = _meta.get(cid)
-        if not m:
-            continue
-        source_clean = m.get("source", "")
-        text_clean = (m.get("text", "") or "").replace("\n", " ")
-        lines.append(f"[{cid}] ({source_clean}) " + text_clean)
-    return "\n".join(lines[:10])
-
-def chat_fn(message, history):
-    model_name = SINGLE_MODEL_NAME
-
-    if _index is None or _index.ntotal == 0:
-        status = build_corpus_from_docs()
-        if not (_index and _index.ntotal > 0):
-            yield f"**Index Status:** {status}\n\nPlease ensure you have a 'docs' folder with PDF/TXT files and try again."
-            return
-
-    hits = retrieve_diverse(
-        message,
-        top_k=6,
-        pool_k=40,
-        per_source_cap=2,
-        strategy="mmr",
-        mmr_lambda=0.5,
-    )
-
-    ctx = _format_ctx(hits) if hits else "(Current index is empty or no matching chunks found)"
-    sys_blocks = [
-        "You are a research assistant who has an excellent factual understanding of the legal policies, regulations, and compliance of enterprises, governments, and global organizations. You are a research assistant who reads Legal papers and provides factual answers to queries. If you do not know the answer, you should convey that to the user instead of hallucinating. Answers must be based on retrieved content with evidence and source numbers cited. If retrieval is insufficient, please clearly explain the shortcomings. When answering, please cite the numbers, e.g., [3]"
-    ]
-    messages = [{"role": "system", "content": "\n\n".join(sys_blocks)}]
-    for u, a in history:
-        messages.append({"role": "user", "content": u})
-        messages.append({"role": "assistant", "content": a})
-    messages.append({"role": "user", "content": message})
-
-    try:
-        response = client.chat.completions.create(
-            model=model_name,
-            messages=messages,
-            temperature=GEN_TEMPERATURE,
-            top_p=GEN_TOP_P,
-            max_tokens=GEN_MAX_TOKENS,
-            stream=True,
-        )
-
-        partial_message = ""
-        for chunk in response:
-            if hasattr(chunk.choices[0], "delta") and chunk.choices[0].delta.content is not None:
-                partial_message += chunk.choices[0].delta.content
-                yield partial_message
-            elif hasattr(chunk.choices[0], "message") and chunk.choices[0].message.content is not None:
-                partial_message += chunk.choices[0].message.content
-                yield partial_message
-    except Exception as e:
-        yield f"[Exception] {repr(e)}"
-
-# Load the FAISS index and metadata from storage if they exist
-try:
-    _load_if_any()
-except Exception as e:
-    print(f"Notice: Could not load existing index. A new one will be created. Error: {e}")
-
-# Main execution block to configure and launch the Gradio app
-if __name__ == "__main__":
-    
-    def chatbot_interface(user_message):
-        """
-        Adapter function to connect the stateless gr.Interface to the 
-        streaming backend chat function.
-        """
-        history = []  # The new interface is stateless
-        gen = chat_fn(user_message, history)
-        final_response = ""
-        for chunk in gen:
-            final_response = chunk
-        return final_response
-
-    with gr.Blocks(theme=gr.themes.Default(primary_hue="sky")) as legalprodigy:
-        inputs = gr.Textbox(
-            lines=7, 
-            label="LegalProdigy Query:",
-            placeholder="Try: Explain Arbitration Process"
-        )
-        outputs = gr.Textbox(lines=10,label="LegalProdigy Response:")
-        
-        gr.Interface(
-            fn=chatbot_interface,
-            inputs=inputs,
-            outputs=outputs
-        )
-        
-    legalprodigy.launch()
+	global _index,_meta;C=get_docs_files()
+	if not C:return'No files found in docs folder.'
+	J=get_emb_model();A,F,B=[],[],[];_index=_B;_meta={}
+	for G in C:
+		D=os.path.basename(G)
+		try:
+			K=_read_any(G)or'';E=_chunk_text(K)
+			if not E:B.append(D);continue
+			A.extend(E);F.extend([D]*len(E))
+		except Exception:B.append(D)
+	if not A:return'No text extracted from docs.'
+	L=[f"passage: {A}"for A in A];H=J.encode(L,batch_size=64,convert_to_numpy=_A,normalize_embeddings=_A);_ensure_index(H.shape[1]);_index.add(H)
+	for(M,(N,O))in enumerate(zip(F,A)):_meta[str(M)]={_J:N,_E:O}
+	_persist();I=f"Indexed {len(A)} chunks from {len(C)} files."
+	if B:I+=f" Failed files: {', '.join(B)}"
+	return I
+def _encode_query_vec(query):return get_emb_model().encode([f"query: {query}"],convert_to_numpy=_A,normalize_embeddings=_A)
+def retrieve_candidates(qvec,pool_k=40):
+	A=pool_k
+	if _index is _B or _index.ntotal==0:return[]
+	A=min(A,_index.ntotal);B,C=_index.search(qvec,A);return[(str(A),float(B))for(A,B)in zip(C[0],B[0])if A!=-1]
+def select_diverse_by_source(cands,top_k=6,per_source_cap=2):
+	F=cands;D=top_k
+	if not F:return[]
+	B=defaultdict(list)
+	for(C,G)in F:
+		I=_meta.get(C)
+		if not I:continue
+		B[I[_J]].append((C,G))
+	for E in B:B[E]=B[E][:per_source_cap]
+	A,N,J=[],[(A,B)for(A,B)in B.items()],{A:0 for A in B}
+	while len(A)<D:
+		K=_G
+		for(E,L)in N:
+			H=J[E]
+			if H<len(L):A.append(L[H]);J[E]=H+1;K=_A
+			if len(A)>=D:break
+		if not K:break
+	if len(A)<D:
+		M={A for(A,B)in A}
+		for(C,G)in F:
+			if C not in M:
+				A.append((C,G));M.add(C)
+				if len(A)>=D:break
+	return A[:D]
+def _encode_chunks_text(cids):A=[f"passage: {(_meta.get(A)or{}).get(_E,'')}"for A in cids];return get_emb_model().encode(A,convert_to_numpy=_A,normalize_embeddings=_A)
+def select_diverse_mmr(cands,qvec,top_k=6,mmr_lambda=.5):
+	H=mmr_lambda;G=top_k;F=cands
+	if not F:return[]
+	C=[A for(A,B)in F];D=_encode_chunks_text(C);E=(D@qvec.T).reshape(-1);A,B=[],set(range(len(C)))
+	while len(A)<min(G,len(C)):
+		if not A:I=int(np.argmax(E));A.append(I);B.remove(I);continue
+		L=D[A];J=D[list(B)]@L.T;M=J.max(axis=1)if J.size>0 else np.zeros((len(B),),dtype=np.float32);N=E[list(B)];O=H*N-(1.-H)*M;P=int(np.argmax(O));K=list(B)[P];A.append(K);B.remove(K)
+	return[(C[A],float(E[A]))for A in A][:G]
+def retrieve_diverse(query,top_k=6,pool_k=40,per_source_cap=2,strategy=_D,mmr_lambda=.5):
+	A=top_k;B=_encode_query_vec(query);C=retrieve_candidates(B,pool_k=pool_k)
+	if strategy==_D:return select_diverse_mmr(C,B,top_k=A,mmr_lambda=mmr_lambda)
+	return select_diverse_by_source(C,top_k=A,per_source_cap=per_source_cap)
+def _format_ctx(hits):
+	if not hits:return''
+	B=[]
+	for(C,F)in hits:
+		A=_meta.get(C)
+		if not A:continue
+		D=A.get(_J,'');E=(A.get(_E,'')or'').replace(_F,' ');B.append(f"[{C}] ({D}) "+E)
+	return _F.join(B[:10])
+def chat_fn(message,history):
+	H='user';F=message;E='content';D='role';I=SINGLE_MODEL_NAME
+	if _index is _B or _index.ntotal==0:
+		J=build_corpus_from_docs()
+		if not(_index and _index.ntotal>0):yield f"**Index Status:** {J}\n\nPlease ensure you have a 'docs' folder with PDF/TXT files and try again.";return
+	G=retrieve_diverse(F,top_k=6,pool_k=40,per_source_cap=2,strategy=_D,mmr_lambda=.5);P=_format_ctx(G)if G else'(Current index is empty or no matching chunks found)';K=['You are a research assistant who has an excellent factual understanding of the legal policies, regulations, and compliance of enterprises, governments, and global organizations. You are a research assistant who reads Legal papers and provides factual answers to queries. If you do not know the answer, you should convey that to the user instead of hallucinating. Answers must be based on retrieved content with evidence and source numbers cited. If retrieval is insufficient, please clearly explain the shortcomings. When answering, please cite the numbers, e.g., [3]'];B=[{D:'system',E:'\n\n'.join(K)}]
+	for(L,M)in history:B.append({D:H,E:L});B.append({D:'assistant',E:M})
+	B.append({D:H,E:F})
+	try:
+		N=client.chat.completions.create(model=I,messages=B,temperature=GEN_TEMPERATURE,top_p=GEN_TOP_P,max_tokens=GEN_MAX_TOKENS,stream=_A);C=''
+		for A in N:
+			if hasattr(A.choices[0],'delta')and A.choices[0].delta.content is not _B:C+=A.choices[0].delta.content;yield C
+			elif hasattr(A.choices[0],'message')and A.choices[0].message.content is not _B:C+=A.choices[0].message.content;yield C
+	except Exception as O:yield f"[Exception] {repr(O)}"
+try:_load_if_any()
+except Exception as e:print(f"Notice: Could not load existing index. A new one will be created. Error: {e}")
+if __name__=='__main__':
+	def chatbot_interface(user_message):
+		'\n        Adapter function to connect the stateless gr.Interface to the \n        streaming backend chat function.\n        ';B=[];C=chat_fn(user_message,B);A=''
+		for D in C:A=D
+		return A
+	with gr.Blocks(theme=gr.themes.Default(primary_hue='sky'))as legalprodigy:inputs=gr.Textbox(lines=7,label='LegalProdigy Query:',placeholder='Try: Explain Arbitration Process');outputs=gr.Textbox(lines=10,label='LegalProdigy Response:');gr.Interface(fn=chatbot_interface,inputs=inputs,outputs=outputs)
+	legalprodigy.launch()