app.py

import traceback
from fastapi import FastAPI, BackgroundTasks
from schemas import *
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from litellm.router import Router
from aiolimiter import AsyncLimiter
import pandas as pd
import asyncio
import re
import nltk

nltk.download('stopwords')
nltk.download('punkt_tab')
nltk.download('wordnet')

from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

import string
import subprocess
import requests
from dotenv import load_dotenv

load_dotenv()

import os
from lxml import etree
import zipfile
import io
import warnings

warnings.filterwarnings("ignore")

from bs4 import BeautifulSoup

app = FastAPI(title="Requirements Extractor")
app.add_middleware(CORSMiddleware, allow_credentials=True, allow_headers=["*"], allow_methods=["*"], allow_origins=["*"])
llm_router = Router(model_list=[{"model_name": "gemini-v1", "litellm_params": {"model": "gemini/gemini-2.0-flash", "api_key": os.environ.get("GEMINI"), "max_retries": 10, "rpm": 15}},
                                {"model_name": "gemini-v2", "litellm_params": {"model": "gemini/gemini-2.5-flash", "api_key": os.environ.get("GEMINI"), "max_retries": 10, "rpm": 10}}]
                                , fallbacks=[{"gemini-v2": ["gemini-v1"]}], num_retries=10)

limiter_mapping = {
    model["model_name"]: AsyncLimiter(model["litellm_params"]["rpm"], 60)
    for model in llm_router.model_list
}
lemmatizer = WordNetLemmatizer()

NSMAP = {
    'w': 'http://schemas.openxmlformats.org/wordprocessingml/2006/main',
    'v': 'urn:schemas-microsoft-com:vml'
}

def lemma(text: str):
    stop_words = set(stopwords.words('english'))
    txt = text.translate(str.maketrans('', '', string.punctuation)).strip()
    tokens = [token for token in word_tokenize(txt.lower()) if token not in stop_words]
    return [lemmatizer.lemmatize(token) for token in tokens]

def get_docx_archive(url: str) -> zipfile.ZipFile:
    """Récupère le docx depuis l'URL et le retourne comme objet ZipFile"""
    if not url.endswith("zip"): 
        raise ValueError("URL doit pointer vers un fichier ZIP")
    doc_id = os.path.splitext(os.path.basename(url))[0]
    resp = requests.get(url, verify=False, headers={
        "User-Agent": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
    })
    resp.raise_for_status()

    with zipfile.ZipFile(io.BytesIO(resp.content)) as zf:
        for file_name in zf.namelist():
            if file_name.endswith(".docx"):
                docx_bytes = zf.read(file_name)
                return zipfile.ZipFile(io.BytesIO(docx_bytes))
            elif file_name.endswith(".doc"):
                input_path = f"/tmp/{doc_id}.doc"
                output_path = f"/tmp/{doc_id}.docx"
                docx_bytes = zf.read(file_name)

                with open(input_path, "wb") as f:
                    f.write(docx_bytes)
                
                subprocess.run([
                    "libreoffice",
                    "--headless",
                    "--convert-to", "docx",
                    "--outdir", "/tmp",
                    input_path
                ], check=True)

                with open(output_path, "rb") as f:
                    docx_bytes = f.read()

                os.remove(input_path)
                os.remove(output_path)
                
                return zipfile.ZipFile(io.BytesIO(docx_bytes))

    raise ValueError("Aucun fichier docx/doc trouvé dans l'archive")

def parse_document_xml(docx_zip: zipfile.ZipFile) -> etree._ElementTree:
    """Parse le document.xml principal"""
    xml_bytes = docx_zip.read('word/document.xml')
    parser = etree.XMLParser(remove_blank_text=True)
    return etree.fromstring(xml_bytes, parser=parser)

def clean_document_xml(root: etree._Element) -> None:
    """Nettoie le XML en modifiant l'arbre directement"""
    # Suppression des balises <w:del> et leur contenu
    for del_elem in root.xpath('//w:del', namespaces=NSMAP):
        parent = del_elem.getparent()
        if parent is not None:
            parent.remove(del_elem)
    
    # Désencapsulation des balises <w:ins>
    for ins_elem in root.xpath('//w:ins', namespaces=NSMAP):
        parent = ins_elem.getparent()
        index = parent.index(ins_elem)
        for child in ins_elem.iterchildren():
            parent.insert(index, child)
            index += 1
        parent.remove(ins_elem)
    
    # Nettoyage des commentaires
    for tag in ['w:commentRangeStart', 'w:commentRangeEnd', 'w:commentReference']:
        for elem in root.xpath(f'//{tag}', namespaces=NSMAP):
            parent = elem.getparent()
            if parent is not None:
                parent.remove(elem)

def create_modified_docx(original_zip: zipfile.ZipFile, modified_root: etree._Element) -> bytes:
    """Crée un nouveau docx avec le XML modifié"""
    output = io.BytesIO()
    
    with zipfile.ZipFile(output, 'w', compression=zipfile.ZIP_DEFLATED) as new_zip:
        # Copier tous les fichiers non modifiés
        for file in original_zip.infolist():
            if file.filename != 'word/document.xml':
                new_zip.writestr(file, original_zip.read(file.filename))
        
        # Ajouter le document.xml modifié
        xml_str = etree.tostring(
            modified_root,
            xml_declaration=True,
            encoding='UTF-8',
            pretty_print=True
        )
        new_zip.writestr('word/document.xml', xml_str)
    
    output.seek(0)
    return output.getvalue()

def docx_to_txt(doc_id: str, url: str):
    docx_zip = get_docx_archive(url)
    root = parse_document_xml(docx_zip)
    clean_document_xml(root)
    modified_bytes = create_modified_docx(docx_zip, root)

    input_path = f"/tmp/{doc_id}_cleaned.docx"
    output_path = f"/tmp/{doc_id}_cleaned.txt"
    with open(input_path, "wb") as f:
        f.write(modified_bytes)
    
    subprocess.run([
        "libreoffice",
        "--headless",
        "--convert-to", "txt",
        "--outdir", "/tmp",
        input_path
    ], check=True)

    with open(output_path, "r", encoding="utf-8") as f:
        txt_data = [line.strip() for line in f if line.strip()]

    os.remove(input_path)
    os.remove(output_path)
    return txt_data

@app.get("/")
def render_page():
    return FileResponse("index.html")

@app.post("/get_meetings", response_model=MeetingsResponse)
def get_meetings(req: MeetingsRequest):
    working_group = req.working_group
    tsg = re.sub(r"\d+", "", working_group)
    wg_number = re.search(r"\d", working_group).group(0)
    url = "https://www.3gpp.org/ftp/tsg_" + tsg
    resp = requests.get(url, verify=False)
    soup = BeautifulSoup(resp.text, "html.parser")
    meeting_folders = []
    all_meetings = []
    wg_folders = [item.get_text() for item in soup.select("tr td a")]
    selected_folder = None
    for folder in wg_folders:
        if str(wg_number) in folder:
            selected_folder = folder
            break

    url += "/" + selected_folder

    if selected_folder:
        resp = requests.get(url, verify=False)
        soup = BeautifulSoup(resp.text, "html.parser")
        meeting_folders = [item.get_text() for item in soup.select("tr td a") if item.get_text().startswith("TSG")]
        all_meetings = [working_group + "#" + meeting.split("_", 1)[1].replace("_", " ").replace("-", " ") for meeting in meeting_folders]
    
    return MeetingsResponse(meetings=dict(zip(all_meetings, meeting_folders)))

@app.post("/get_dataframe", response_model=DataResponse)
def get_change_request_dataframe(req: DataRequest):
    working_group = req.working_group
    tsg = re.sub(r"\d+", "", working_group)
    wg_number = re.search(r"\d", working_group).group(0)
    url = "https://www.3gpp.org/ftp/tsg_" + tsg
    resp = requests.get(url, verify=False)
    soup = BeautifulSoup(resp.text, "html.parser")
    wg_folders = [item.get_text() for item in soup.select("tr td a")]
    selected_folder = None
    for folder in wg_folders:
        if str(wg_number) in folder:
            selected_folder = folder
            break

    url += "/" + selected_folder + "/" + req.meeting + "/docs"
    resp = requests.get(url, verify=False)
    soup = BeautifulSoup(resp.text, "html.parser")
    files = [item.get_text() for item in soup.select("tr td a") if item.get_text().endswith(".xlsx")]

    def gen_url(tdoc: str):
        return f"{url}/{tdoc}.zip"

    df = pd.read_excel(str(url + "/" + files[0]).replace("#", "%23"))
    filtered_df = df[(((df["Type"] == "CR") & ((df["CR category"] == "B") | (df["CR category"] == "C"))) | (df["Type"] == "pCR")) & ~(df["Uploaded"].isna())][["TDoc", "Title", "CR category", "Source", "Type", "Agenda item", "Agenda item description", "TDoc Status"]]
    filtered_df["URL"] = filtered_df["TDoc"].apply(gen_url)

    df = filtered_df.fillna("")
    return DataResponse(data=df[["TDoc", "Title", "Type", "TDoc Status", "Agenda item description", "URL"]].to_dict(orient="records"))

@app.post("/generate_requirements", response_model=RequirementsResponse)
async def gen_reqs(req: RequirementsRequest, background_tasks: BackgroundTasks):
    documents = req.documents
    n_docs = len(documents)
    
    async def process_document(doc):
        doc_id = doc.document
        url = doc.url
        try:
            full = "\n".join(docx_to_txt(doc_id, url))
        except Exception as e:
            traceback.print_exception(e)
            return RequirementsResponse(requirements=[DocRequirements(document=doc_id, context="Error LLM", requirements=[])]).requirements
        
        try:
            model_used = "gemini-v2"  # À adapter si fallback activé
            async with limiter_mapping[model_used]:
                resp_ai = await llm_router.acompletion(
                    model=model_used,
                    messages=[{"role":"user","content": f"Here's the document whose ID is {doc_id} : {full}\n\nExtract all requirements and group them by context, returning a list of objects where each object includes a document ID, a concise description of the context where the requirements apply (not a chapter title or copied text), and a list of associated requirements; always return the result as a list, even if only one context is found."}],
                    response_format=RequirementsResponse
                )
            return RequirementsResponse.model_validate_json(resp_ai.choices[0].message.content).requirements
        except Exception as e:
            if "rate limit" in str(e).lower():
                try:
                    model_used = "gemini-v2"  # À adapter si fallback activé
                    async with limiter_mapping[model_used]:
                        resp_ai = await llm_router.acompletion(
                            model=model_used,
                            messages=[{"role":"user","content": f"Here's the document whose ID is {doc_id} : {full}\n\nExtract all requirements and group them by context, returning a list of objects where each object includes a document ID, a concise description of the context where the requirements apply (not a chapter title or copied text), and a list of associated requirements; always return the result as a list, even if only one context is found."}],
                            response_format=RequirementsResponse
                        )
                    return RequirementsResponse.model_validate_json(resp_ai.choices[0].message.content).requirements
                except Exception as fallback_e:
                    traceback.print_exception(fallback_e)
                    return RequirementsResponse(requirements=[DocRequirements(document=doc_id, context="Error LLM", requirements=[])]).requirements
            else:
                traceback.print_exception(e)
                return RequirementsResponse(requirements=[DocRequirements(document=doc_id, context="Error LLM", requirements=[])]).requirements
    
    async def process_batch(batch):
        results = await asyncio.gather(*(process_document(doc) for doc in batch))
        return [item for sublist in results for item in sublist]
    
    all_requirements = []
    
    if n_docs <= 30:
        batch_results = await process_batch(documents)
        all_requirements.extend(batch_results)
    else:
        batch_size = 30
        batches = [documents[i:i + batch_size] for i in range(0, n_docs, batch_size)]
        
        for i, batch in enumerate(batches):
            batch_results = await process_batch(batch)
            all_requirements.extend(batch_results)
            
            if i < len(batches) - 1:
                background_tasks.add_task(asyncio.sleep, 60)
    return RequirementsResponse(requirements=all_requirements)

@app.post("/get_reqs_from_query", response_model=ReqSearchResponse)
def find_requirements_from_problem_description(req: ReqSearchRequest):
    requirements = req.requirements
    query = req.query

    requirements_text = "\n".join([f"[Document: {r.document} | Context: {r.context} | Requirement: {r.requirement}]" for r in requirements])
    
    print("Called the LLM")
    resp_ai = llm_router.completion(
        model="gemini-v2",
        messages=[{"role":"user","content": f"Given all the requirements : \n {requirements_text} \n and the problem description \"{query}\", return a list of objects each with document ID, context, and requirement for the most relevant requirements that reference or best cover the problem."}],
        response_format=ReqSearchResponse
    )
    print("Answered")

    return ReqSearchResponse.model_validate_json(resp_ai.choices[0].message.content)