app.py

import os
import re
import io
import tempfile
import torch
import pandas as pd
import plotly.express as px
import streamlit as st
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    AutoModelForSeq2SeqLM,
    pipeline
)
from PyPDF2 import PdfReader
from docx import Document
from gtts import gTTS
from io import BytesIO
import spacy
import subprocess

# -----------------------------
# Hugging Face fix: ensure Streamlit runs properly
# -----------------------------
if __name__ == "__main__" and os.environ.get("SYSTEM") == "spaces":
    subprocess.Popen(["streamlit", "run", "app.py", "--server.port", "7860", "--server.address", "0.0.0.0"])
    exit()

# -----------------------------
# Page config
# -----------------------------
st.set_page_config(page_title="⚖ ClauseWise", page_icon="⚖", layout="wide")

# -----------------------------
# Language Map
# -----------------------------
LANG_MAP = {
    "English": "en", "French": "fr", "Spanish": "es", "German": "de",
    "Hindi": "hi", "Tamil": "ta", "Telugu": "te", "Kannada": "kn",
    "Marathi": "mr", "Gujarati": "gu", "Bengali": "bn"
}
LANG_NAMES = list(LANG_MAP.keys())

# -----------------------------
# Model Loading (cached)
# -----------------------------
@st.cache_resource
def load_models():
    simplify_model_name = "mrm8488/t5-small-finetuned-text-simplification"
    tokenizer_simplify = AutoTokenizer.from_pretrained(simplify_model_name)
    simplify_model = AutoModelForSeq2SeqLM.from_pretrained(simplify_model_name)

    gen_model_id = "microsoft/phi-2"
    gen_tokenizer = AutoTokenizer.from_pretrained(gen_model_id, trust_remote_code=True)
    gen_model = AutoModelForCausalLM.from_pretrained(gen_model_id, trust_remote_code=True)

    # ✅ Load SpaCy
    try:
        nlp = spacy.load("en_core_web_sm")
    except OSError:
        from spacy.cli import download
        download("en_core_web_sm")
        nlp = spacy.load("en_core_web_sm")

    classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

    return tokenizer_simplify, simplify_model, gen_tokenizer, gen_model, nlp, classifier, summarizer


tokenizer_simplify, simplify_model, gen_tokenizer, gen_model, nlp, classifier, summarizer = load_models()
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
gen_model.to(DEVICE)

# -----------------------------
# Utility Functions
# -----------------------------
def extract_text(file):
    if not file:
        return ""
    name = file.name.lower()
    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(name)[1]) as tmp:
        tmp.write(file.read())
        tmp_path = tmp.name
    text = ""
    try:
        if name.endswith(".pdf"):
            reader = PdfReader(tmp_path)
            for page in reader.pages:
                t = page.extract_text()
                if t:
                    text += t + "\n"
        elif name.endswith(".docx"):
            doc = Document(tmp_path)
            text = "\n".join([p.text for p in doc.paragraphs if p.text.strip()])
        else:
            with open(tmp_path, "r", encoding="utf-8", errors="ignore") as f:
                text = f.read()
    except Exception as e:
        st.error(f"Error reading file: {e}")
    finally:
        if os.path.exists(tmp_path):
            os.remove(tmp_path)
    return text.strip()


def translate_text(text, target_lang):
    if not text:
        return ""
    lang_code = LANG_MAP.get(target_lang, "en")
    if lang_code == "en":
        return text
    try:
        translator = pipeline("translation", model=f"Helsinki-NLP/opus-mt-en-{lang_code}")
        return translator(text[:1000])[0]["translation_text"]
    except Exception:
        return text


def text_to_speech(text, lang):
    try:
        lang_code = LANG_MAP.get(lang, "en")
        tts = gTTS(text=text[:1000], lang=lang_code)
        audio_fp = BytesIO()
        tts.write_to_fp(audio_fp)
        audio_fp.seek(0)
        return audio_fp
    except Exception:
        return None


def clause_simplification(text, mode):
    prefix = {
        "Simplified": "simplify: ",
        "Explain like I'm 5": "explain like I'm 5: ",
        "Professional": "rephrase professionally: "
    }.get(mode, "simplify: ")
    inputs = tokenizer_simplify(prefix + text[:500], return_tensors="pt", truncation=True, max_length=512)
    outputs = simplify_model.generate(**inputs, max_length=256, num_beams=4, early_stopping=True)
    return tokenizer_simplify.decode(outputs[0], skip_special_tokens=True)


def fairness_score_visual(text, lang):
    pos = len(re.findall(r"\b(mutual|both parties|shared|equal|fair|balanced)\b", text, re.I))
    neg = len(re.findall(r"\b(sole|unilateral|exclusive right|one-sided|only)\b", text, re.I))
    score = max(0, min(100, 50 + (pos * 5) - (neg * 5)))

    st.subheader("⚖ Fairness Balance Meter")
    fairness_df = pd.DataFrame({
        "Aspect": ["Party A Favored", "Balanced", "Party B Favored"],
        "Score": [max(0, 100 - score), score, min(100, score)]
    })
    fig = px.bar(
        fairness_df, x="Score", y="Aspect", orientation="h", text="Score", color="Aspect",
        color_discrete_sequence=["#ff6b6b", "#4ecdc4", "#95e1d3"]
    )
    fig.update_layout(showlegend=False, xaxis_title="Score", yaxis_title="", height=300)
    st.plotly_chart(fig, use_container_width=True)
    st.info(translate_text(f"Fairness Score: {score}% (Approximate)", lang))


def chat_response(prompt, lang, history):
    """Persistent memory chat"""
    # Combine chat history context
    context = "\n".join([f"User: {u}\nAI: {a}" for u, a in history[-3:]])  # Keep last 3
    full_prompt = f"You are a helpful multilingual legal assistant. {context}\nUser: {prompt}\nAI:"
    inputs = gen_tokenizer(full_prompt, return_tensors="pt").to(DEVICE)
    outputs = gen_model.generate(**inputs, max_new_tokens=200, temperature=0.7, top_p=0.9, do_sample=True)
    response = gen_tokenizer.decode(outputs[0], skip_special_tokens=True)
    if "AI:" in response:
        response = response.split("AI:")[-1].strip()
    return translate_text(response, lang)


# -----------------------------
# Main Streamlit App
# -----------------------------
def main():
    st.title("⚖ ClauseWise: Multilingual Legal AI Assistant")
    st.markdown("Simplify, translate, and analyze legal documents with AI — in your language.")
    st.divider()

    tab1, tab2, tab3, tab4 = st.tabs(["📄 Analyzer", "🌐 Translate & Audio", "💬 Chatbot", "ℹ About"])

    with tab1:
        st.subheader("📁 Upload or Paste Legal Document")
        lang = st.selectbox("Select Language:", LANG_NAMES, index=0)
        file = st.file_uploader("Upload a Legal Document (PDF/DOCX/TXT)", type=["pdf", "docx", "txt"])
        text_input = st.text_area("Or Paste Text Here:", height=200)

        if file or text_input:
            text = extract_text(file) if file else text_input
            if not text:
                st.warning("No content found.")
            else:
                mode = st.radio("Simplify Mode", ["Explain like I'm 5", "Simplified", "Professional"])
                if st.button("🧾 Simplify Clauses"):
                    with st.spinner("Simplifying..."):
                        simplified = clause_simplification(text, mode)
                        translated = translate_text(simplified, lang)
                        st.success(translated)
                        audio = text_to_speech(translated, lang)
                        if audio:
                            st.audio(audio, format="audio/mp3")

                if st.button("⚖ Fairness Analysis"):
                    fairness_score_visual(text, lang)

    with tab2:
        st.subheader("🌐 Translate & Listen")
        text_input = st.text_area("Enter text:", height=200)
        lang = st.selectbox("Translate to:", LANG_NAMES, index=4)
        if st.button("Translate"):
            translated = translate_text(text_input, lang)
            st.success(translated)
        if st.button("🎧 Generate Audio"):
            audio = text_to_speech(text_input, lang)
            if audio:
                st.audio(audio, format="audio/mp3")

    with tab3:
        st.subheader("💬 Chat with ClauseWise (Memory Enabled)")
        lang = st.selectbox("Chat Language:", LANG_NAMES, index=0)
        query = st.text_area("Ask your question:", height=150)

        # Maintain persistent conversation
        if "chat_history" not in st.session_state:
            st.session_state.chat_history = []

        if st.button("Ask"):
            if query.strip():
                with st.spinner("Thinking..."):
                    response = chat_response(query, lang, st.session_state.chat_history)
                    st.session_state.chat_history.append((query, response))
                    st.success(response)
                    audio = text_to_speech(response, lang)
                    if audio:
                        st.audio(audio, format="audio/mp3")

        # Display conversation history
        if st.session_state.chat_history:
            st.markdown("### 🧠 Chat History")
            for q, a in st.session_state.chat_history[-5:]:
                st.markdown(f"*You:* {q}")
                st.markdown(f"*ClauseWise:* {a}")

        if st.button("Clear Chat"):
            st.session_state.chat_history = []
            st.info("Chat cleared.")

    with tab4:
        st.markdown("""
        ### ⚖ About ClauseWise
        ClauseWise is a multilingual AI-powered legal assistant that helps users:
        - Simplify legal language  
        - Translate and listen in 10+ languages  
        - Assess fairness visually  
        - Chat interactively with memory  
        ---
        *Disclaimer:* Educational use only — not legal advice.
        """)


if __name__ == "__main__":
    main()