app.py

import streamlit as st
import shelve
import docx2txt
import PyPDF2
import time  # Used to simulate typing effect
import nltk
import re
import os
import time  # already imported in your code
from dotenv import load_dotenv
import torch
from sentence_transformers import SentenceTransformer, util
nltk.download('punkt')
import hashlib
from nltk import sent_tokenize
nltk.download('punkt_tab')
from transformers import LEDTokenizer, LEDForConditionalGeneration
from transformers import pipeline
import asyncio
import dateutil.parser
from datetime import datetime
import sys

from openai import OpenAI
import numpy as np


# Fix for RuntimeError: no running event loop on Windows
if sys.platform.startswith("win"):
    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

st.set_page_config(page_title="Legal Document Summarizer", layout="wide")

if "processed" not in st.session_state:
    st.session_state.processed = False
if "last_uploaded_hash" not in st.session_state:
    st.session_state.last_uploaded_hash = None
if "chat_prompt_processed" not in st.session_state:
    st.session_state.chat_prompt_processed = False

if "embedding_text" not in st.session_state:
    st.session_state.embedding_text = None

if "document_context" not in st.session_state:
    st.session_state.document_context = None

if "last_prompt_hash" not in st.session_state:
    st.session_state.last_prompt_hash = None


st.title("📄 Legal Document Summarizer (Document Augmentation RAG)")

USER_AVATAR = "👤"
BOT_AVATAR = "🤖"

# Load chat history
def load_chat_history():
    with shelve.open("chat_history") as db:
        return db.get("messages", [])

# Save chat history
def save_chat_history(messages):
    with shelve.open("chat_history") as db:
        db["messages"] = messages

# Function to limit text preview to 500 words
def limit_text(text, word_limit=500):
    words = text.split()
    return " ".join(words[:word_limit]) + ("..." if len(words) > word_limit else "")


# CLEAN AND NORMALIZE TEXT


def clean_text(text):
    # Remove newlines and extra spaces
    text = text.replace('\r\n', ' ').replace('\n', ' ')
    text = re.sub(r'\s+', ' ', text)
    
    # Remove page number markers like "Page 1 of 10"
    text = re.sub(r'Page\s+\d+\s+of\s+\d+', '', text, flags=re.IGNORECASE)

    # Remove long dashed or underscored lines
    text = re.sub(r'[_]{5,}', '', text)   # Lines with underscores: _____
    text = re.sub(r'[-]{5,}', '', text)   # Lines with hyphens: -----
    
    # Remove long dotted separators
    text = re.sub(r'[.]{4,}', '', text)   # Dots like "......" or ".............."
    
    # Trim final leading/trailing whitespace
    text = text.strip()

    return text


#######################################################################################################################


# LOADING MODELS FOR DIVIDING TEXT INTO SECTIONS

# Load token from .env file
load_dotenv()
HF_API_TOKEN = os.getenv("HF_API_TOKEN")

client = OpenAI(
    base_url="https://api.studio.nebius.com/v1/",
    api_key=os.getenv("OPENAI_API_KEY")
)

# print("API Key:", os.getenv("OPENAI_API_KEY"))  # Temporary for debugging


# Load once at the top (cache for performance)
@st.cache_resource
def load_local_zero_shot_classifier():
    return pipeline("zero-shot-classification", model="typeform/distilbert-base-uncased-mnli")

local_classifier = load_local_zero_shot_classifier()


SECTION_LABELS = ["Facts", "Arguments", "Judgement", "Others"]

def classify_chunk(text):
    result = local_classifier(text, candidate_labels=SECTION_LABELS)
    return result["labels"][0]


# NEW: NLP-based sectioning using zero-shot classification
def section_by_zero_shot(text):
    sections = {"Facts": "", "Arguments": "", "Judgment": "", "Others": ""}
    sentences = sent_tokenize(text)
    chunk = ""

    for i, sent in enumerate(sentences):
        chunk += sent + " "
        if (i + 1) % 3 == 0 or i == len(sentences) - 1:
            label = classify_chunk(chunk.strip())
            print(f"🔎 Chunk: {chunk[:60]}...\n🔖 Predicted Label: {label}")
            # 👇 Normalize label (title case and fallback)
            label = label.capitalize()
            if label not in sections:
                label = "Others"
            sections[label] += chunk + "\n"
            chunk = ""

    return sections

#######################################################################################################################



# EXTRACTING TEXT FROM UPLOADED FILES

# Function to extract text from uploaded file
def extract_text(file):
    if file.name.endswith(".pdf"):
        reader = PyPDF2.PdfReader(file)
        full_text = "\n".join(page.extract_text() or "" for page in reader.pages)
    elif file.name.endswith(".docx"):
        full_text = docx2txt.process(file)
    elif file.name.endswith(".txt"):
        full_text = file.read().decode("utf-8")
    else:
        return "Unsupported file type."
    
    return full_text  # Full text is needed for summarization


#######################################################################################################################

# EXTRACTIVE AND ABSTRACTIVE SUMMARIZATION


@st.cache_resource
def load_legalbert():
    return SentenceTransformer("nlpaueb/legal-bert-base-uncased")


legalbert_model = load_legalbert()

@st.cache_resource
def load_led():
    tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
    model = LEDForConditionalGeneration.from_pretrained("allenai/led-base-16384")
    return tokenizer, model

tokenizer_led, model_led = load_led()


def legalbert_extractive_summary(text, top_ratio=0.2):
    sentences = sent_tokenize(text)
    top_k = max(3, int(len(sentences) * top_ratio))
    if len(sentences) <= top_k:
        return text
    sentence_embeddings = legalbert_model.encode(sentences, convert_to_tensor=True)
    doc_embedding = torch.mean(sentence_embeddings, dim=0)
    cosine_scores = util.pytorch_cos_sim(doc_embedding, sentence_embeddings)[0]
    top_results = torch.topk(cosine_scores, k=top_k)
    selected_sentences = [sentences[i] for i in sorted(top_results.indices.tolist())]
    return " ".join(selected_sentences)

    # Add LED Abstractive Summarization


def led_abstractive_summary(text, max_length=512, min_length=100):
    inputs = tokenizer_led(
        text, return_tensors="pt", padding="max_length",
        truncation=True, max_length=4096
    )
    global_attention_mask = torch.zeros_like(inputs["input_ids"])
    global_attention_mask[:, 0] = 1

    outputs = model_led.generate(
        inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        global_attention_mask=global_attention_mask,
        max_length=max_length,
        min_length=min_length,
        num_beams=4,                      # Use beam search
        repetition_penalty=2.0,           # Penalize repetition
        length_penalty=1.0,
        early_stopping=True,
        no_repeat_ngram_size=4            # Prevent repeated phrases
    )

    return tokenizer_led.decode(outputs[0], skip_special_tokens=True)



def led_abstractive_summary_chunked(text, max_tokens=3000):
    sentences = sent_tokenize(text)
    current_chunk, chunks, summaries = "", [], []
    for sent in sentences:
        if len(tokenizer_led(current_chunk + sent)["input_ids"]) > max_tokens:
            chunks.append(current_chunk)
            current_chunk = sent
        else:
            current_chunk += " " + sent
    if current_chunk:
        chunks.append(current_chunk)
    for chunk in chunks:
        inputs = tokenizer_led(chunk, return_tensors="pt", padding="max_length", truncation=True, max_length=4096)
        global_attention_mask = torch.zeros_like(inputs["input_ids"])
        global_attention_mask[:, 0] = 1
        output = model_led.generate(
            inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            global_attention_mask=global_attention_mask,
            max_length=512,
            min_length=100,
            num_beams=4,
            repetition_penalty=2.0,
            length_penalty=1.0,
            early_stopping=True,
            no_repeat_ngram_size=4,
        )
        summaries.append(tokenizer_led.decode(output[0], skip_special_tokens=True))
    return " ".join(summaries)



def hybrid_summary_hierarchical(text, top_ratio=0.8):
    cleaned_text = clean_text(text)
    sections = section_by_zero_shot(cleaned_text)

    structured_summary = {}  # <-- hierarchical summary here

    for name, content in sections.items():
        if content.strip():
            # Extractive summary
            extractive = legalbert_extractive_summary(content, top_ratio)

            # Abstractive summary
            abstractive = led_abstractive_summary_chunked(extractive)

            # Store in dictionary (hierarchical structure)
            structured_summary[name] = {
                "extractive": extractive,
                "abstractive": abstractive
            }

    return structured_summary


def chunk_text_custom(text, n=1000, overlap=200):
    chunks = []
    for i in range(0, len(text), n - overlap):
        chunks.append(text[i:i + n])
    return chunks



def get_embedding(text, model="BAAI/bge-en-icl"):
    """
    From your notebook:
    Creates an embedding for the given text chunk using the BGE-ICL model.
    """
    resp = client.embeddings.create(model=model, input=text)
    return np.array(resp.data[0].embedding)



def semantic_search(query, text_chunks, chunk_embeddings, k=5):
    """
    Compute cosine similarity between the query embedding and each chunk embedding,
    then pick the top-k chunks.
    """
    q_emb = get_embedding(query)
    # simple cosine:
    def cosine(a, b): return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
    scores = [cosine(q_emb, emb) for emb in chunk_embeddings]
    top_idxs = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:k]
    return [text_chunks[i] for i in top_idxs]


def generate_response(system_prompt, user_message, model="meta-llama/Llama-3.2-3B-Instruct"):
    return client.chat.completions.create(
        model=model,
        temperature=0,
        messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_message}]
    ).choices[0].message.content


def generate_questions(text_chunk, num_questions=5,
                       model="meta-llama/Llama-3.2-3B-Instruct"):
    system_prompt = (
      "You are an expert at generating relevant questions from text. "
      "Create concise questions that can be answered using only the provided text."
    )
    user_prompt = f"""
    Based on the following text, generate {num_questions} different questions 
    that can be answered using only this text:

    {text_chunk}

    Format your response as a numbered list of questions only.
    """
    resp = client.chat.completions.create(
      model=model,
      temperature=0.7,
      messages=[
        {"role":"system","content":system_prompt},
        {"role":"user","content":user_prompt}
      ]
    )
    raw = resp.choices[0].message.content.strip()
    questions = []
    for line in raw.split("\n"):
        q = re.sub(r"^\d+\.\s*", "", line).strip()
        if q.endswith("?"):
            questions.append(q)
    return questions

# 2) EMBEDDINGS
def create_embeddings(text, model="BAAI/bge-en-icl"):
    resp = client.embeddings.create(model=model, input=text)
    return resp.data[0].embedding

def cosine_similarity(a,b):
    return float(np.dot(a,b)/(np.linalg.norm(a)*np.linalg.norm(b)))

# 3) VECTOR STORE
class SimpleVectorStore:
    def __init__(self):
        self.items = []  # each item is dict {text, embedding, metadata}
    def add_item(self, text, embedding, metadata):
        self.items.append(dict(text=text, embedding=embedding, metadata=metadata))
    def search(self, query, k=5):
        q_emb = create_embeddings(query)
        scores = [(i, cosine_similarity(q_emb, item["embedding"]))
                  for i,item in enumerate(self.items)]
        scores.sort(key=lambda x:x[1], reverse=True)
        return [self.items[i] for i,_ in scores[:k]]

# 4) DOCUMENT PROCESSOR
def process_document(raw_text,
                     chunk_size=1000,
                     chunk_overlap=200,
                     questions_per_chunk=5):
    # chunk the text
    chunks = []
    for i in range(0, len(raw_text), chunk_size - chunk_overlap):
        chunks.append(raw_text[i:i+chunk_size])
    store = SimpleVectorStore()
    for idx,chunk in enumerate(chunks):
        # chunk embedding
        emb = create_embeddings(chunk)
        store.add_item(chunk, emb, {"type":"chunk","index":idx})
        # generate Qs + their embeddings
        qs = generate_questions(chunk, num_questions=questions_per_chunk)
        for q in qs:
            q_emb = create_embeddings(q)
            store.add_item(q, q_emb, {
              "type":"question",
              "chunk_index":idx,
              "original_chunk": chunk
            })
    return chunks, store

# 5) CONTEXT BUILDER
def prepare_context(results):
    seen = set()
    ctx = []
    # first direct chunks
    for r in results:
        m = r["metadata"]
        if m["type"]=="chunk" and m["index"] not in seen:
            seen.add(m["index"])
            ctx.append(f"Chunk {m['index']}:\n{r['text']}")
    # then referenced by questions
    for r in results:
        m = r["metadata"]
        if m["type"]=="question":
            ci = m["chunk_index"]
            if ci not in seen:
                seen.add(ci)
                ctx.append(f"Chunk {ci} (via Q “{r['text']}”):\n{m['original_chunk']}")
    return "\n\n".join(ctx)

# 6) ANSWER GENERATOR (overrides your old generate_response)
def generate_response_from_context(query, context,
                                   model="meta-llama/Llama-3.2-3B-Instruct"):
    sp = (
      "You are an AI assistant that strictly answers based on the given context. "
      "If the answer cannot be derived directly from the provided context, "
      "respond with: 'I do not have enough information to answer that.'"
    )
    up = f"""
    Context:
    {context}

    Question: {query}

    Please answer the question based only on the context above.
    """
    resp = client.chat.completions.create(
      model=model,
      temperature=0,
      messages=[{"role":"system","content":sp},
                {"role":"user","content":up}]
    )
    return resp.choices[0].message.content




#######################################################################################################################


# STREAMLIT APP INTERFACE CODE

# Initialize or load chat history
if "messages" not in st.session_state:
    st.session_state.messages = load_chat_history()

# Initialize last_uploaded if not set
if "last_uploaded" not in st.session_state:
    st.session_state.last_uploaded = None



# Sidebar with a button to delete chat history
with st.sidebar:
    st.subheader("⚙️ Options")
    if st.button("Delete Chat History"):
        st.session_state.messages = []
        st.session_state.last_uploaded = None
        st.session_state.processed = False
        st.session_state.chat_prompt_processed = False
        save_chat_history([])


# Display chat messages with a typing effect
def display_with_typing_effect(text, speed=0.005):
    placeholder = st.empty()
    displayed_text = ""
    for char in text:
        displayed_text += char
        placeholder.markdown(displayed_text)
        time.sleep(speed)
    return displayed_text

# Show existing chat messages
for message in st.session_state.messages:
    avatar = USER_AVATAR if message["role"] == "user" else BOT_AVATAR
    with st.chat_message(message["role"], avatar=avatar):
        st.markdown(message["content"])


# Standard chat input field
prompt = st.chat_input("Type a message...")


# Place uploader before the chat so it's always visible
with st.container():
    st.subheader("📎 Upload a Legal Document")
    uploaded_file = st.file_uploader("Upload a file (PDF, DOCX, TXT)", type=["pdf", "docx", "txt"])
    reprocess_btn = st.button("🔄 Reprocess Last Uploaded File")



# Hashing logic
def get_file_hash(file):
    file.seek(0)
    content = file.read()
    file.seek(0)
    return hashlib.md5(content).hexdigest()

# Function to prepare text for embedding
# This function combines the extractive and abstractive summaries into a single string for embedding
def prepare_text_for_embedding(summary_dict):
    combined_chunks = []

    for section, content in summary_dict.items():
        ext = content.get("extractive", "").strip()
        abs = content.get("abstractive", "").strip()
        if ext:
            combined_chunks.append(f"{section} - Extractive Summary:\n{ext}")
        if abs:
            combined_chunks.append(f"{section} - Abstractive Summary:\n{abs}")

    return "\n\n".join(combined_chunks)


##############################################################################################################

user_role = st.sidebar.selectbox(
    "🎭 Select Your Role for Custom Summary",
    ["General", "Judge", "Lawyer", "Student"]
)


def role_based_filter(section, summary, role):
    if role == "General":
        return summary
    
    filtered_summary = {
        "extractive": "",
        "abstractive": ""
    }

    if role == "Judge" and section in ["Judgement", "Facts"]:
        filtered_summary = summary
    elif role == "Lawyer" and section in ["Arguments", "Facts"]:
        filtered_summary = summary
    elif role == "Student" and section in ["Facts"]:
        filtered_summary = summary

    return filtered_summary



#########################################################################################################################


if uploaded_file:
    file_hash = get_file_hash(uploaded_file)
    if file_hash != st.session_state.last_uploaded_hash or reprocess_btn:
        st.session_state.processed = False

    if not st.session_state.processed:
        start_time = time.time()

        # 1) extract & summarize as before
        raw_text     = extract_text(uploaded_file)
        summary_dict = hybrid_summary_hierarchical(raw_text)
        embedding_text = prepare_text_for_embedding(summary_dict)

        # ─── NEW: document‐augmentation ingestion ───
        chunks, store = process_document(raw_text,
                                         chunk_size=1000,
                                         chunk_overlap=200,
                                         questions_per_chunk=5)
        st.session_state.vector_store = store
        # ────────────────────────────────────────────

        # 2) generate your “role‐specific prompt” as before
        st.session_state.document_context = embedding_text
      
        if user_role == "General":
            role_specific_prompt = (
            "Summarize the legal document focusing on the most relevant aspects "
            "such as facts, arguments, and judgments. Include key legal reasoning "
            "and a timeline of events where necessary."
        )
        else:
            role_specific_prompt = (
            f"As a {user_role}, summarize the legal document focusing on "
            "the most relevant aspects such as facts, arguments, and judgments "
            "tailored for your role. Include key legal reasoning and timeline of events."
        )

        # ─── REPLACE rag_query_response with doc‐augmentation RAG ───
        results = store.search(role_specific_prompt, k=5)
        context = prepare_context(results)
        rag_summary = generate_response_from_context(role_specific_prompt, context)
        #

        st.session_state.messages.append({
            "role": "user", 
            "content": f"📤 Uploaded **{uploaded_file.name}**"
        })
        st.session_state.messages.append({
            "role": "assistant",
            "content": rag_summary
        })
        with st.chat_message("assistant", avatar=BOT_AVATAR):
            display_with_typing_effect(rag_summary)

        processing_time = round((time.time() - start_time) / 60, 2)
        st.info(f"⏱️ Response generated in **{processing_time} minutes**.")

        st.session_state.generated_summary = rag_summary
        st.session_state.last_uploaded_hash = file_hash
        st.session_state.processed = True
        st.session_state.last_prompt_hash = None
        save_chat_history(st.session_state.messages)



if prompt:
    words = prompt.split()
    word_count   = len(words)
    prompt_hash  = hashlib.md5(prompt.encode("utf-8")).hexdigest()

    # 1) LONG prompts – echo & ingest like a “paste‐in” document
    if word_count > 30 and prompt_hash != st.session_state.last_prompt_hash:
        st.session_state.last_prompt_hash = prompt_hash

        raw_text = prompt
        st.session_state.messages.append({
            "role": "user",
            "content": f"📥 **Pasted Document Text:**\n\n{limit_text(raw_text,500)}"
        })
        with st.chat_message("user", avatar=USER_AVATAR):
            st.markdown(limit_text(raw_text,500))

        start_time = time.time()
        # summarization + emb_text as before
        summary_dict   = hybrid_summary_hierarchical(raw_text)
        emb_text       = prepare_text_for_embedding(summary_dict)
        st.session_state.document_context = emb_text
        st.session_state.processed        = True

        # ─── NEW: ingest via document‐augmentation ───
        chunks, store = process_document(raw_text)
        st.session_state.vector_store = store

        if user_role == "General":
            role_prompt = (
                "Summarize the document focusing on facts, arguments, judgments, "
                "and include a timeline of events."
            )
        else:
            role_prompt = (
                f"As a {user_role}, summarize the document focusing on facts, "
                "arguments, judgments, plus timeline of events."
            )

        # ─── doc‐augmentation RAG here too ───
        results = store.search(role_prompt, k=5)
        context = prepare_context(results)
        initial_summary = generate_response_from_context(role_prompt, context)
      
        st.session_state.messages.append({
            "role": "assistant",
            "content": initial_summary
        })
        with st.chat_message("assistant", avatar=BOT_AVATAR):
            display_with_typing_effect(initial_summary)

        st.info(f"⏱️ Summary generated in {round((time.time()-start_time)/60,2)} minutes")
        save_chat_history(st.session_state.messages)


    # 2) SHORT prompts – normal RAG against last ingested context
    elif word_count <= 30 and st.session_state.processed:

        with st.chat_message("user", avatar=USER_AVATAR):
            st.markdown(prompt)

    # 2) save to history
        st.session_state.messages.append({"role": "user", "content": prompt})
        store = st.session_state.vector_store

        # ─── instead of rag_query_response, do doc‐augmentation RAG ───
        results = store.search(prompt, k=5)
        context = prepare_context(results)
        answer  = generate_response_from_context(prompt, context)

        # st.session_state.messages.append({"role":"user",     "content":prompt})
        st.session_state.messages.append({"role":"assistant","content":answer})
        with st.chat_message("assistant", avatar=BOT_AVATAR):
            display_with_typing_effect(answer)
        save_chat_history(st.session_state.messages)


    # 3) not enough input
    else:
        with st.chat_message("assistant", avatar=BOT_AVATAR):
            st.markdown("❗ Paste at least 30 words of your document to ingest it first.")


################################Evaluation###########################
######################################################################################################################

# 📚 Imports
import evaluate
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
from sklearn.metrics import f1_score

# 📌 Load Evaluators Once
@st.cache_resource
def load_evaluators():
    rouge = evaluate.load("rouge")
    bertscore = evaluate.load("bertscore")
    return rouge, bertscore

rouge, bertscore = load_evaluators()

# 📌 Define Evaluation Functions
def evaluate_summary(generated_summary, ground_truth_summary):
    """Evaluate ROUGE and BERTScore."""
    rouge_result = rouge.compute(predictions=[generated_summary], references=[ground_truth_summary])
    bert_result = bertscore.compute(predictions=[generated_summary], references=[ground_truth_summary], lang="en")
    return rouge_result, bert_result

def exact_match(prediction, ground_truth):
    return int(prediction.strip().lower() == ground_truth.strip().lower())

def compute_bleu(prediction, ground_truth):
    reference = [ground_truth.strip().split()]
    candidate = prediction.strip().split()
    smoothie = SmoothingFunction().method4
    return sentence_bleu(reference, candidate, smoothing_function=smoothie)

def compute_f1(prediction, ground_truth):
    """Compute F1 score based on token overlap, like in QA evaluation."""
    pred_tokens = prediction.strip().lower().split()
    gt_tokens = ground_truth.strip().lower().split()

    common_tokens = set(pred_tokens) & set(gt_tokens)
    num_common = len(common_tokens)

    if num_common == 0:
        return 0.0

    precision = num_common / len(pred_tokens)
    recall = num_common / len(gt_tokens)
    f1 = 2 * (precision * recall) / (precision + recall)
    return f1

def evaluate_additional_metrics(prediction, ground_truth):
    em = exact_match(prediction, ground_truth)
    bleu = compute_bleu(prediction, ground_truth)
    f1 = compute_f1(prediction, ground_truth)
    return {
        "Exact Match": em,
        "BLEU Score": bleu,
        "F1 Score": f1
    }

# 📥 Upload and Evaluate
ground_truth_summary_file = st.file_uploader("📄 Upload Ground Truth Summary (.txt)", type=["txt"])

if ground_truth_summary_file:
    ground_truth_summary = ground_truth_summary_file.read().decode("utf-8").strip()

    if "generated_summary" in st.session_state and st.session_state.generated_summary:
        prediction = st.session_state.generated_summary

        # Evaluate ROUGE and BERTScore
        rouge_result, bert_result = evaluate_summary(prediction, ground_truth_summary)

        # Display ROUGE and BERTScore
        st.subheader("📊 Evaluation Results")
        st.write("🔹 ROUGE Scores:")
        st.json(rouge_result)
        st.write("🔹 BERTScore:")
        st.json(bert_result)

        # Evaluate and Display Exact Match, BLEU, F1
        additional_metrics = evaluate_additional_metrics(prediction, ground_truth_summary)
        st.subheader("🔎 Additional Evaluation Metrics")
        st.json(additional_metrics)

    else:
        st.warning("⚠️ Please generate a summary first by uploading a document.")





######################################################################################################################


# Run this along with streamlit run app.py to evaluate the model's performance on a test set
# Otherwise, comment the below code

# ⇒ EVALUATION HOOK: after the very first summary, fire off evaluate.main() once

# import json
# import pandas as pd
# import threading


# def run_eval(doc_context):

#     with open("test_case1.json", "r", encoding="utf-8") as f:
#             gt_data = json.load(f)

#         # 2) map document_id → local file

#     records = []
#     for entry in gt_data:
#         doc_id = entry["document_id"]
#         query  = entry["query"]
#         gt_ans = entry["ground_truth_answer"]
        
        
#         # model_ans = rag_query_response(query, emb_text)
#         model_ans = rag_query_response(query, doc_context)
        
#         records.append({
#                 "document_id": doc_id,
#                 "query": query,
#                 "ground_truth_answer": gt_ans,
#                 "model_answer": model_ans
#             })
#         print(f"✅ Done {doc_id} / “{query}”")

#         # 3) push to DataFrame + CSV
#         df = pd.DataFrame(records)
#         out = "evaluation_results.csv"
#         df.to_csv(out, index=False, encoding="utf-8")
#         print(f"\n📝 Saved {len(df)} rows to {out}")


# # you could log this somewhere
# def _run_evaluation():
#     try:
#         run_eval()
#     except Exception as e:
#         print("‼️ Evaluation script error:", e)

# if st.session_state.processed and not st.session_state.get("evaluation_launched", False):
#     st.session_state.evaluation_launched = True

#       # inform user
#     st.sidebar.info("🔬 Starting background evaluation run…")

#     # *capture* the context
#     doc_ctx = st.session_state.document_context

#     # spawn the thread, passing doc_ctx in
#     threading.Thread(
#         target=lambda: run_eval(doc_ctx),
#         daemon=True
#     ).start()

#     st.sidebar.success("✅ Evaluation launched — check evaluation_results.csv when done.")

#     # check for file existence & show download button
#     eval_path = os.path.abspath("evaluation_results.csv")
#     if os.path.exists(eval_path):
#         st.sidebar.success(f"✅ Results saved to:\n`{eval_path}`")
#         # load it into a small dataframe (optional)
#         df_eval = pd.read_csv(eval_path)
#         # add a download button
#         st.sidebar.download_button(
#             label="⬇️ Download evaluation_results.csv",
#             data=df_eval.to_csv(index=False).encode("utf-8"),
#             file_name="evaluation_results.csv",
#             mime="text/csv"
#         )
#     else:
#         # if you want, display the cwd so you can inspect it
#         st.sidebar.info(f"Current working dir:\n`{os.getcwd()}`")